/usr/bin/diffoscope --timeout 7200 --html /srv/reproducible-results/rbuild-debian/r-b-build.10dZ17Ba/pandas_1.1.5+dfsg-2.diffoscope.html --text /srv/reproducible-results/rbuild-debian/r-b-build.10dZ17Ba/pandas_1.1.5+dfsg-2.diffoscope.txt --json /srv/reproducible-results/rbuild-debian/r-b-build.10dZ17Ba/pandas_1.1.5+dfsg-2.diffoscope.json --profile=- /srv/reproducible-results/rbuild-debian/r-b-build.10dZ17Ba/b1/pandas_1.1.5+dfsg-2_amd64.changes /srv/reproducible-results/rbuild-debian/r-b-build.10dZ17Ba/b2/pandas_1.1.5+dfsg-2

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/srv/reproducible-results/rbuild-debian/r-b-build.10dZ17Ba/b1/pandas_1.1.5+dfsg-2_amd64.changes vs.

/srv/reproducible-results/rbuild-debian/r-b-build.10dZ17Ba/b2/pandas_1.1.5+dfsg-2_amd64.changes ¶

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731 B

Files ¶

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2.06 MB

python-pandas-doc_1.1.5+dfsg-2_all.deb ¶

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452 B

file list ¶

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641 B

control.tar.xz ¶

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613 B

control.tar ¶

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./control ¶

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./md5sums ¶

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./md5sums ¶

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data.tar.xz ¶

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data.tar ¶

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file list ¶

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36.6 KB

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Similarity: 0.9997853087998139% Differences: {"'cells'": "{5: {'outputs': {0: {'data': {'text/html': {insert: [(1, '</style><table " 'id="T_ae060dbe_e689_11ef_8b59_cf981f471b6c" ><thead> <tr> <th class="blank ' 'level0" ></th> <th class="col_heading level0 col0" >A</th> <th ' 'class="col_heading level0 col1" >B</th> <th class="col_heading level0 col2" ' '>C</th> <th class="col_heading level0 col3" >D</th> <th ' 'class="col_heading level0 col4" >E</ […]

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python3-pandas-lib_1.1.5+dfsg-2_amd64.deb ¶

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452 B

file list ¶

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98.0 B

control.tar.xz ¶

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70.0 B

control.tar ¶

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48.0 B

./md5sums ¶

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30.0 B

./md5sums ¶

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data.tar.xz ¶

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63.6 KB

data.tar ¶

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1.56 KB

./usr/lib/python3/dist-packages/pandas/_libs/groupby.cpython-39-x86_64-linux-gnu.so ¶

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readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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1.56 KB

./usr/lib/python3/dist-packages/pandas/_libs/hashing.cpython-39-x86_64-linux-gnu.so ¶

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755 B

readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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1.56 KB

./usr/lib/python3/dist-packages/pandas/_libs/hashtable.cpython-39-x86_64-linux-gnu.so ¶

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readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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./usr/lib/python3/dist-packages/pandas/_libs/index.cpython-39-x86_64-linux-gnu.so ¶

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readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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./usr/lib/python3/dist-packages/pandas/_libs/indexing.cpython-39-x86_64-linux-gnu.so ¶

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readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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./usr/lib/python3/dist-packages/pandas/_libs/internals.cpython-39-x86_64-linux-gnu.so ¶

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755 B

readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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./usr/lib/python3/dist-packages/pandas/_libs/interval.cpython-39-x86_64-linux-gnu.so ¶

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755 B

readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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./usr/lib/python3/dist-packages/pandas/_libs/join.cpython-39-x86_64-linux-gnu.so ¶

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readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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./usr/lib/python3/dist-packages/pandas/_libs/json.cpython-39-x86_64-linux-gnu.so ¶

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readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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./usr/lib/python3/dist-packages/pandas/_libs/lib.cpython-39-x86_64-linux-gnu.so ¶

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readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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./usr/lib/python3/dist-packages/pandas/_libs/missing.cpython-39-x86_64-linux-gnu.so ¶

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readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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./usr/lib/python3/dist-packages/pandas/_libs/ops.cpython-39-x86_64-linux-gnu.so ¶

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755 B

readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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./usr/lib/python3/dist-packages/pandas/_libs/ops_dispatch.cpython-39-x86_64-linux-gnu.so ¶

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755 B

readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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./usr/lib/python3/dist-packages/pandas/_libs/parsers.cpython-39-x86_64-linux-gnu.so ¶

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readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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./usr/lib/python3/dist-packages/pandas/_libs/properties.cpython-39-x86_64-linux-gnu.so ¶

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readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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./usr/lib/python3/dist-packages/pandas/_libs/reduction.cpython-39-x86_64-linux-gnu.so ¶

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readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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./usr/lib/python3/dist-packages/pandas/_libs/reshape.cpython-39-x86_64-linux-gnu.so ¶

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755 B

readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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./usr/lib/python3/dist-packages/pandas/_libs/sparse.cpython-39-x86_64-linux-gnu.so ¶

File has been modified after NT_GNU_BUILD_ID has been applied.

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755 B

readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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./usr/lib/python3/dist-packages/pandas/_libs/testing.cpython-39-x86_64-linux-gnu.so ¶

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755 B

readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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./usr/lib/python3/dist-packages/pandas/_libs/tslib.cpython-39-x86_64-linux-gnu.so ¶

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755 B

readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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1.56 KB

./usr/lib/python3/dist-packages/pandas/_libs/tslibs/base.cpython-39-x86_64-linux-gnu.so ¶

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755 B

readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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2.13 KB

./usr/lib/python3/dist-packages/pandas/_libs/tslibs/ccalendar.cpython-39-x86_64-linux-gnu.so ¶

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430 B

readelf --wide --notes {} ¶

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755 B

readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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811 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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./usr/lib/python3/dist-packages/pandas/_libs/tslibs/conversion.cpython-39-x86_64-linux-gnu.so ¶

File has been modified after NT_GNU_BUILD_ID has been applied.

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755 B

readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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./usr/lib/python3/dist-packages/pandas/_libs/tslibs/dtypes.cpython-39-x86_64-linux-gnu.so ¶

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readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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./usr/lib/python3/dist-packages/pandas/_libs/tslibs/fields.cpython-39-x86_64-linux-gnu.so ¶

File has been modified after NT_GNU_BUILD_ID has been applied.

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430 B

readelf --wide --notes {} ¶

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755 B

readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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811 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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1.57 KB

./usr/lib/python3/dist-packages/pandas/_libs/tslibs/nattype.cpython-39-x86_64-linux-gnu.so ¶

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755 B

readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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1.58 KB

./usr/lib/python3/dist-packages/pandas/_libs/tslibs/np_datetime.cpython-39-x86_64-linux-gnu.so ¶

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755 B

readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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1.57 KB

./usr/lib/python3/dist-packages/pandas/_libs/tslibs/offsets.cpython-39-x86_64-linux-gnu.so ¶

File has been modified after NT_GNU_BUILD_ID has been applied.

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755 B

readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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1.57 KB

./usr/lib/python3/dist-packages/pandas/_libs/tslibs/parsing.cpython-39-x86_64-linux-gnu.so ¶

File has been modified after NT_GNU_BUILD_ID has been applied.

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readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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1.57 KB

./usr/lib/python3/dist-packages/pandas/_libs/tslibs/period.cpython-39-x86_64-linux-gnu.so ¶

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755 B

readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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1.57 KB

./usr/lib/python3/dist-packages/pandas/_libs/tslibs/strptime.cpython-39-x86_64-linux-gnu.so ¶

File has been modified after NT_GNU_BUILD_ID has been applied.

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755 B

readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

⊟

1.58 KB

./usr/lib/python3/dist-packages/pandas/_libs/tslibs/timedeltas.cpython-39-x86_64-linux-gnu.so ¶

File has been modified after NT_GNU_BUILD_ID has been applied.

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readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

⊟

1.58 KB

./usr/lib/python3/dist-packages/pandas/_libs/tslibs/timestamps.cpython-39-x86_64-linux-gnu.so ¶

File has been modified after NT_GNU_BUILD_ID has been applied.

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readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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1.57 KB

./usr/lib/python3/dist-packages/pandas/_libs/tslibs/timezones.cpython-39-x86_64-linux-gnu.so ¶

File has been modified after NT_GNU_BUILD_ID has been applied.

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readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

⊟

1.58 KB

./usr/lib/python3/dist-packages/pandas/_libs/tslibs/tzconversion.cpython-39-x86_64-linux-gnu.so ¶

File has been modified after NT_GNU_BUILD_ID has been applied.

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readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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1.58 KB

./usr/lib/python3/dist-packages/pandas/_libs/tslibs/vectorized.cpython-39-x86_64-linux-gnu.so ¶

File has been modified after NT_GNU_BUILD_ID has been applied.

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755 B

readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

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1.58 KB

./usr/lib/python3/dist-packages/pandas/_libs/window/aggregations.cpython-39-x86_64-linux-gnu.so ¶

File has been modified after NT_GNU_BUILD_ID has been applied.

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755 B

readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

⊟

1.51 KB

./usr/lib/python3/dist-packages/pandas/_libs/window/indexers.cpython-39-x86_64-linux-gnu.so ¶

⊟

755 B

readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

⊟

1.56 KB

./usr/lib/python3/dist-packages/pandas/_libs/writers.cpython-39-x86_64-linux-gnu.so ¶

File has been modified after NT_GNU_BUILD_ID has been applied.

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755 B

readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

⊟

1.55 KB

./usr/lib/python3/dist-packages/pandas/io/sas/_sas.cpython-39-x86_64-linux-gnu.so ¶

File has been modified after NT_GNU_BUILD_ID has been applied.

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755 B

readelf --wide --decompress --hex-dump=.gnu_debugaltlink {} ¶

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610 B

readelf --wide --decompress --hex-dump=.gnu_debuglink {} ¶

error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`: readelf: Error: no .dynamic section in the dynamic segment

⊟

58.4 KB

python3-pandas-lib-dbgsym_1.1.5+dfsg-2_amd64.deb ¶

⊟

452 B

file list ¶

⊟

6.37 KB

control.tar.xz ¶

⊟

6.34 KB

control.tar ¶

⊟

3.75 KB

./control ¶

⊟

2.57 KB

./md5sums ¶

⊟

30.0 B

./md5sums ¶

Files differ

⊟

2.52 KB

line order ¶

⊟

51.5 KB

data.tar.xz ¶

⊟

51.5 KB

data.tar ¶

⊟

7.5 KB

file list ¶

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Files 4% similar despite different names

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Offset 1, 5 lines modified		Offset 1, 5 lines modified

1	·a~~184~~246~~4740248~~1d4158599371e9~~3d7b~~·8196756·doc·optional·python-pandas-doc_1.1.5+dfsg-2_all.deb	1	·cca2b615df6231f4a537906930d9c29d·8196764·doc·optional·python-pandas-doc_1.1.5+dfsg-2_all.deb
2	·b~~117f9~~dc7~~47c6~~4845~~e21abc0c~~b~~1b3~~43f·2433~~8332~~·debug·optional·python3-pandas-lib-dbgsym_1.1.5+dfsg-2_amd64.deb	2	·dfdabd7fe7a4459096d9d3db266574cf·24334404·debug·optional·python3-pandas-lib-dbgsym_1.1.5+dfsg-2_amd64.deb
3	·~~faa975~~a5~~924~~1ebec3~~ee25b3~~35~~e2f1827~~·32~~913~~16·python·optional·python3-pandas-lib_1.1.5+dfsg-2_amd64.deb	3	·0957b30ae1585d124c908d193cbccb3d·3288916·python·optional·python3-pandas-lib_1.1.5+dfsg-2_amd64.deb
4	·e6541380ee2e4732c3cfb039a92ab3e3·2095828·python·optional·python3-pandas_1.1.5+dfsg-2_all.deb	4	·e6541380ee2e4732c3cfb039a92ab3e3·2095828·python·optional·python3-pandas_1.1.5+dfsg-2_all.deb


Offset 1, 3 lines modified		Offset 1, 3 lines modified
1	-rw-r--r--···0········0········0········4·2021-01-12·21:06:04.000000·debian-binary	1	-rw-r--r--···0········0········0········4·2021-01-12·21:06:04.000000·debian-binary
2	-rw-r--r--···0········0········0···146832·2021-01-12·21:06:04.000000·control.tar.xz	2	-rw-r--r--···0········0········0···146848·2021-01-12·21:06:04.000000·control.tar.xz
3	-rw-r--r--···0········0········0··8049732·2021-01-12·21:06:04.000000·data.tar.xz	3	-rw-r--r--···0········0········0··8049724·2021-01-12·21:06:04.000000·data.tar.xz


Offset 1, 13 lines modified		Offset 1, 13 lines modified
1	Package:·python-pandas-doc	1	Package:·python-pandas-doc
2	Source:·pandas	2	Source:·pandas
3	Version:·1.1.5+dfsg-2	3	Version:·1.1.5+dfsg-2
4	Architecture:·all	4	Architecture:·all
5	Maintainer:·Debian·Science·Team·<debian-science-maintainers@lists.alioth.debian.org>	5	Maintainer:·Debian·Science·Team·<debian-science-maintainers@lists.alioth.debian.org>
6	Installed-Size:·49020	6	Installed-Size:·49019
7	Depends:·libjs-sphinxdoc·(>=·2.4.3-5~)	7	Depends:·libjs-sphinxdoc·(>=·2.4.3-5~)
8	Suggests:·python3-pandas	8	Suggests:·python3-pandas
9	Section:·doc	9	Section:·doc
10	Priority:·optional	10	Priority:·optional
11	Homepage:·https://pandas.pydata.org/	11	Homepage:·https://pandas.pydata.org/
12	Description:·data·structures·for·"relational"·or·"labeled"·data·-·documentation	12	Description:·data·structures·for·"relational"·or·"labeled"·data·-·documentation
13	·pandas·is·a·Python·package·providing·fast,·flexible,·and·expressive	13	·pandas·is·a·Python·package·providing·fast,·flexible,·and·expressive


Offset 2505, 22 lines modified		Offset 2505, 20 lines modified
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2508	········"010081":·12,	2508	········"010081":·12,
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2525	········"0133":·2202,	2523	········"0133":·2202,
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Offset 2528, 14 lines modified		Offset 2526, 15 lines modified
2528	········"013638":·2192,	2526	········"013638":·2192,
2529	········"013684":·[180,·761],	2527	········"013684":·[180,·761],
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2535	········"014138":·2192,	2534	········"014138":·2192,
2536	········"014144":·[96,·1158],	2535	········"014144":·[96,·1158],
2537	········"014500":·2199,	2536	········"014500":·2199,
2538	········"014648":·2187,	2537	········"014648":·2187,
2539	········"014752":·2233,	2538	········"014752":·2233,
2540	········"014805":·2202,	2539	········"014805":·2202,
2541	········"014871":·[2186,·2197,·2202,·2203],	2540	········"014871":·[2186,·2197,·2202,·2203],
Offset 2551, 29 lines modified		Offset 2550, 29 lines modified
2551	········"016331":·2209,	2550	········"016331":·2209,
2552	········"016424":·[13,·15],	2551	········"016424":·[13,·15],
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2556	········"016828":·2199,	2555	········"016828":·2199,
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2558	········"017060":·2190,	2558	········"017060":·2190,
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2563	········"018":·2199,	2563	········"018":·[2193,·2199],
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2573	········"019462":·2195,	2572	········"019462":·2195,
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2578	········"01t00":·[906,·1233,·2165,·2199,·2209,·2233,·2244,·2259],	2577	········"01t00":·[906,·1233,·2165,·2199,·2209,·2233,·2244,·2259],
2579	········"01t01":·2209,	2578	········"01t01":·2209,
Offset 2585, 24 lines modified		Offset 2584, 24 lines modified
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2677	········"034069":·2195,	2676	········"034069":·2195,
2678	········"034326":·[2185,·2255],	2677	········"034326":·[2185,·2255],
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2680	········"034523":·2209,	2679	········"034523":·2209,
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2683	········"034907":·2185,	2682	········"034907":·2185,
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2684	········"035215":·2203,	2684	········"035215":·2203,
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2690	········"036047":·2212,	2690	········"036047":·2212,
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2692	········"036142":·[2218,·2229],	2692	········"036142":·[2218,·2229],
2693	········"0362":·2202,	2693	········"0362":·2202,
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2695	········"037181":·2192,	2696	········"037181":·2192,
2696	········"037528":·[2206,·2233],	2697	········"037528":·[2206,·2233],
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2700	········"037870":·2190,	2701	········"037870":·2190,
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2762	········"047990":·2192,	2763	········"047990":·2192,
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6306	-rw-r--r--···0·root·········(0)·root·········(0)····85862·2021-01-12·21:06:04.000000·./usr/share/doc/python-pandas-doc/html/user_guide/timedeltas.html	6306	-rw-r--r--···0·root·········(0)·root·········(0)····85862·2021-01-12·21:06:04.000000·./usr/share/doc/python-pandas-doc/html/user_guide/timedeltas.html
6307	-rw-r--r--···0·root·········(0)·root·········(0)···429331·2021-01-12·21:06:04.000000·./usr/share/doc/python-pandas-doc/html/user_guide/timeseries.html	6307	-rw-r--r--···0·root·········(0)·root·········(0)···429331·2021-01-12·21:06:04.000000·./usr/share/doc/python-pandas-doc/html/user_guide/timeseries.html
6308	-rw-r--r--···0·root·········(0)·root·········(0)···177639·2021-01-12·21:06:04.000000·./usr/share/doc/python-pandas-doc/html/user_guide/visualization.html	6308	-rw-r--r--···0·root·········(0)·root·········(0)···177639·2021-01-12·21:06:04.000000·./usr/share/doc/python-pandas-doc/html/user_guide/visualization.html
6309	-rw-r--r--···0·root·········(0)·root·········(0)······264·2021-01-12·21:06:04.000000·./usr/share/doc/python-pandas-doc/html/visualization.html	6309	-rw-r--r--···0·root·········(0)·root·········(0)······264·2021-01-12·21:06:04.000000·./usr/share/doc/python-pandas-doc/html/visualization.html
6310	drwxr-xr-x···0·root·········(0)·root·········(0)········0·2021-01-12·21:06:04.000000·./usr/share/doc/python-pandas-doc/html/whatsnew/	6310	drwxr-xr-x···0·root·········(0)·root·········(0)········0·2021-01-12·21:06:04.000000·./usr/share/doc/python-pandas-doc/html/whatsnew/
6311	-rw-r--r--···0·root·········(0)·root·········(0)····57200·2021-01-12·21:06:04.000000·./usr/share/doc/python-pandas-doc/html/whatsnew/index.html	6311	-rw-r--r--···0·root·········(0)·root·········(0)····57200·2021-01-12·21:06:04.000000·./usr/share/doc/python-pandas-doc/html/whatsnew/index.html


Offset 1211, 15 lines modified		Offset 1211, 15 lines modified
1211	<span·class="gp">In·[138]:·</span><span·class="n">plt</span><span·class="o">.</span><span·class="n">figure</span><span·class="p">()</span>	1211	<span·class="gp">In·[138]:·</span><span·class="n">plt</span><span·class="o">.</span><span·class="n">figure</span><span·class="p">()</span>
1212	<span·class="gh">Out[138]:·</span><span·class="go"><Figure·size·640x480·with·0·Axes></span>	1212	<span·class="gh">Out[138]:·</span><span·class="go"><Figure·size·640x480·with·0·Axes></span>

1213	<span·class="gp">In·[139]:·</span><span·class="n">df</span><span·class="o">.</span><span·class="n">plot</span><span·class="p">()</span>	1213	<span·class="gp">In·[139]:·</span><span·class="n">df</span><span·class="o">.</span><span·class="n">plot</span><span·class="p">()</span>
1214	<span·class="gh">Out[139]:·</span><span·class="go"><AxesSubplot:></span>	1214	<span·class="gh">Out[139]:·</span><span·class="go"><AxesSubplot:></span>

1215	<span·class="gp">In·[140]:·</span><span·class="n">plt</span><span·class="o">.</span><span·class="n">legend</span><span·class="p">(</span><span·class="n">loc</span><span·class="o">=</span><span·class="s1">'best'</span><span·class="p">)</span>	1215	<span·class="gp">In·[140]:·</span><span·class="n">plt</span><span·class="o">.</span><span·class="n">legend</span><span·class="p">(</span><span·class="n">loc</span><span·class="o">=</span><span·class="s1">'best'</span><span·class="p">)</span>
1216	<span·class="gh">Out[140]:·</span><span·class="go"><matplotlib.legend.Legend·at·0x7f~~b720f2719~~0></span>	1216	<span·class="gh">Out[140]:·</span><span·class="go"><matplotlib.legend.Legend·at·0x7f4b01e3d220></span>
1217	</pre></div>	1217	</pre></div>
1218	</div>	1218	</div>
1219	<img·alt="../_images/frame_plot_basic.png"·src="../_images/frame_plot_basic.png"·/>	1219	<img·alt="../_images/frame_plot_basic.png"·src="../_images/frame_plot_basic.png"·/>
1220	</div>	1220	</div>
1221	<div·class="section"·id="getting-data-in-out">	1221	<div·class="section"·id="getting-data-in-out">
1222	<h2>Getting·data·in/out<a·class="headerlink"·href="#getting-data-in-out"·title="Permalink·to·this·headline">¶</a></h2>	1222	<h2>Getting·data·in/out<a·class="headerlink"·href="#getting-data-in-out"·title="Permalink·to·this·headline">¶</a></h2>
1223	<div·class="section"·id="csv">	1223	<div·class="section"·id="csv">


Offset 978, 15 lines modified		Offset 978, 15 lines modified
978	In·[138]:·plt.figure()	978	In·[138]:·plt.figure()
979	Out[138]:·<Figure·size·640x480·with·0·Axes>	979	Out[138]:·<Figure·size·640x480·with·0·Axes>

980	In·[139]:·df.plot()	980	In·[139]:·df.plot()
981	Out[139]:·<AxesSubplot:>	981	Out[139]:·<AxesSubplot:>

982	In·[140]:·plt.legend(loc='best')	982	In·[140]:·plt.legend(loc='best')
983	Out[140]:·<matplotlib.legend.Legend·at·0x7f~~b720f2719~~0>	983	Out[140]:·<matplotlib.legend.Legend·at·0x7f4b01e3d220>
984	[../_images/frame_plot_basic.png]	984	[../_images/frame_plot_basic.png]
985	***·Getting·data·in/outÂ¶·***	985	***·Getting·data·in/outÂ¶·***
986	**·CSVÂ¶·**	986	**·CSVÂ¶·**
987	Writing_to_a_csv_file.	987	Writing_to_a_csv_file.
988	In·[141]:·df.to_csv('foo.csv')	988	In·[141]:·df.to_csv('foo.csv')
989	Reading_from_a_csv_file.	989	Reading_from_a_csv_file.
990	In·[142]:·pd.read_csv('foo.csv')	990	In·[142]:·pd.read_csv('foo.csv')


Offset 1295, 25 lines modified		Offset 1295, 25 lines modified
1295	<span·class="gp">In·[141]:·</span><span·class="n">indexer</span>·<span·class="o">=</span>·<span·class="n">np</span><span·class="o">.</span><span·class="n">arange</span><span·class="p">(</span><span·class="mi">10000</span><span·class="p">)</span>	1295	<span·class="gp">In·[141]:·</span><span·class="n">indexer</span>·<span·class="o">=</span>·<span·class="n">np</span><span·class="o">.</span><span·class="n">arange</span><span·class="p">(</span><span·class="mi">10000</span><span·class="p">)</span>

1296	<span·class="gp">In·[142]:·</span><span·class="n">random</span><span·class="o">.</span><span·class="n">shuffle</span><span·class="p">(</span><span·class="n">indexer</span><span·class="p">)</span>	1296	<span·class="gp">In·[142]:·</span><span·class="n">random</span><span·class="o">.</span><span·class="n">shuffle</span><span·class="p">(</span><span·class="n">indexer</span><span·class="p">)</span>

1297	<span·class="gp">In·[143]:·</span><span·class="o">%</span><span·class="k">timeit</span>·arr[indexer]	1297	<span·class="gp">In·[143]:·</span><span·class="o">%</span><span·class="k">timeit</span>·arr[indexer]
1298	<span·class="gp">···.....:·</span><span·class="o">%</span><span·class="k">timeit</span>·arr.take(indexer,·axis=0)	1298	<span·class="gp">···.....:·</span><span·class="o">%</span><span·class="k">timeit</span>·arr.take(indexer,·axis=0)
1299	<span·class="gp">···.....:·</span>	1299	<span·class="gp">···.....:·</span>
1300	<span·class="go">443·us·+-·88.1·us·per·loop·(mean·+-·std.·dev.·of·7·runs,·1000·loops·each)</span>	1300	<span·class="go">383·us·+-·12.7·us·per·loop·(mean·+-·std.·dev.·of·7·runs,·1000·loops·each)</span>
1301	<span·class="go">157·us·+-·18.5·us·per·loop·(mean·+-·std.·dev.·of·7·runs,·10000·loops·each)</span>	1301	<span·class="go">172·us·+-·30.4·us·per·loop·(mean·+-·std.·dev.·of·7·runs,·10000·loops·each)</span>
1302	</pre></div>	1302	</pre></div>
1303	</div>	1303	</div>
1304	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[144]:·</span><span·class="n">ser</span>·<span·class="o">=</span>·<span·class="n">pd</span><span·class="o">.</span><span·class="n">Series</span><span·class="p">(</span><span·class="n">arr</span><span·class="p">[:,</span>·<span·class="mi">0</span><span·class="p">])</span>	1304	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[144]:·</span><span·class="n">ser</span>·<span·class="o">=</span>·<span·class="n">pd</span><span·class="o">.</span><span·class="n">Series</span><span·class="p">(</span><span·class="n">arr</span><span·class="p">[:,</span>·<span·class="mi">0</span><span·class="p">])</span>

1305	<span·class="gp">In·[145]:·</span><span·class="o">%</span><span·class="k">timeit</span>·ser.iloc[indexer]	1305	<span·class="gp">In·[145]:·</span><span·class="o">%</span><span·class="k">timeit</span>·ser.iloc[indexer]
1306	<span·class="gp">···.....:·</span><span·class="o">%</span><span·class="k">timeit</span>·ser.take(indexer)	1306	<span·class="gp">···.....:·</span><span·class="o">%</span><span·class="k">timeit</span>·ser.take(indexer)
1307	<span·class="gp">···.....:·</span>	1307	<span·class="gp">···.....:·</span>
		1308	<span·class="go">431·us·+-·26.9·us·per·loop·(mean·+-·std.·dev.·of·7·runs,·1000·loops·each)</span>
1308	<span·class="go">259·us·+-·70.9·us·per·loop·(mean·+-·std.·dev.·of·7·runs,·10000·loops·each)</span>	1309	<span·class="go">269·us·+-·93.3·us·per·loop·(mean·+-·std.·dev.·of·7·runs,·1000·loops·each)</span>
1309	<span·class="go">212·us·+-·41.1·us·per·loop·(mean·+-·std.·dev.·of·7·runs,·10000·loops·each)</span>
1310	</pre></div>	1310	</pre></div>
1311	</div>	1311	</div>
1312	</div>	1312	</div>
1313	<div·class="section"·id="index-types">	1313	<div·class="section"·id="index-types">
1314	<span·id="indexing-index-types"></span><h2>Index·types<a·class="headerlink"·href="#index-types"·title="Permalink·to·this·headline">¶</a></h2>	1314	<span·id="indexing-index-types"></span><h2>Index·types<a·class="headerlink"·href="#index-types"·title="Permalink·to·this·headline">¶</a></h2>
1315	<p>We·have·discussed·<code·class="docutils·literal·notranslate"><span·class="pre">MultiIndex</span></code>·in·the·previous·sections·pretty·extensively.	1315	<p>We·have·discussed·<code·class="docutils·literal·notranslate"><span·class="pre">MultiIndex</span></code>·in·the·previous·sections·pretty·extensively.
1316	Documentation·about·<code·class="docutils·literal·notranslate"><span·class="pre">DatetimeIndex</span></code>·and·<code·class="docutils·literal·notranslate"><span·class="pre">PeriodIndex</span></code>·are·shown·<a·class="reference·internal"·href="timeseries.html#timeseries-overview"><span·class="std·std-ref">here</span></a>,	1316	Documentation·about·<code·class="docutils·literal·notranslate"><span·class="pre">DatetimeIndex</span></code>·and·<code·class="docutils·literal·notranslate"><span·class="pre">PeriodIndex</span></code>·are·shown·<a·class="reference·internal"·href="timeseries.html#timeseries-overview"><span·class="std·std-ref">here</span></a>,


Offset 1113, 23 lines modified		Offset 1113, 23 lines modified
1113	In·[141]:·indexer·=·np.arange(10000)	1113	In·[141]:·indexer·=·np.arange(10000)

1114	In·[142]:·random.shuffle(indexer)	1114	In·[142]:·random.shuffle(indexer)

1115	In·[143]:·%timeit·arr[indexer]	1115	In·[143]:·%timeit·arr[indexer]
1116	···.....:·%timeit·arr.take(indexer,·axis=0)	1116	···.....:·%timeit·arr.take(indexer,·axis=0)
1117	···.....:	1117	···.....:
1118	443·us·+-·88.1·us·per·loop·(mean·+-·std.·dev.·of·7·runs,·1000·loops·each)	1118	383·us·+-·12.7·us·per·loop·(mean·+-·std.·dev.·of·7·runs,·1000·loops·each)
1119	157·us·+-·18.5·us·per·loop·(mean·+-·std.·dev.·of·7·runs,·10000·loops·each)	1119	172·us·+-·30.4·us·per·loop·(mean·+-·std.·dev.·of·7·runs,·10000·loops·each)
1120	In·[144]:·ser·=·pd.Series(arr[:,·0])	1120	In·[144]:·ser·=·pd.Series(arr[:,·0])

1121	In·[145]:·%timeit·ser.iloc[indexer]	1121	In·[145]:·%timeit·ser.iloc[indexer]
1122	···.....:·%timeit·ser.take(indexer)	1122	···.....:·%timeit·ser.take(indexer)
1123	···.....:	1123	···.....:
		1124	431·us·+-·26.9·us·per·loop·(mean·+-·std.·dev.·of·7·runs,·1000·loops·each)
1124	259·us·+-·70.9·us·per·loop·(mean·+-·std.·dev.·of·7·runs,·10000·loops·each)	1125	269·us·+-·93.3·us·per·loop·(mean·+-·std.·dev.·of·7·runs,·1000·loops·each)
1125	212·us·+-·41.1·us·per·loop·(mean·+-·std.·dev.·of·7·runs,·10000·loops·each)
1126	***·Index·typesÂ¶·***	1126	***·Index·typesÂ¶·***
1127	We·have·discussed·MultiIndex·in·the·previous·sections·pretty·extensively.	1127	We·have·discussed·MultiIndex·in·the·previous·sections·pretty·extensively.
1128	Documentation·about·DatetimeIndex·and·PeriodIndex·are·shown·here,·and	1128	Documentation·about·DatetimeIndex·and·PeriodIndex·are·shown·here,·and
1129	documentation·about·TimedeltaIndex·is·found·here.	1129	documentation·about·TimedeltaIndex·is·found·here.
1130	In·the·following·sub-sections·we·will·highlight·some·other·index·types.	1130	In·the·following·sub-sections·we·will·highlight·some·other·index·types.
1131	**·CategoricalIndexÂ¶·**	1131	**·CategoricalIndexÂ¶·**
1132	CategoricalIndex·is·a·type·of·index·that·is·useful·for·supporting·indexing·with	1132	CategoricalIndex·is·a·type·of·index·that·is·useful·for·supporting·indexing·with


Offset 3551, 20 lines modified		Offset 3551, 20 lines modified
3551	</pre></div>	3551	</pre></div>
3552	</div>	3552	</div>
3553	<p>The·number·of·columns·of·each·type·in·a·<code·class="docutils·literal·notranslate"><span·class="pre">DataFrame</span></code>·can·be·found·by·calling	3553	<p>The·number·of·columns·of·each·type·in·a·<code·class="docutils·literal·notranslate"><span·class="pre">DataFrame</span></code>·can·be·found·by·calling
3554	<code·class="docutils·literal·notranslate"><span·class="pre">DataFrame.dtypes.value_counts()</span></code>.</p>	3554	<code·class="docutils·literal·notranslate"><span·class="pre">DataFrame.dtypes.value_counts()</span></code>.</p>
3555	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[352]:·</span><span·class="n">dft</span><span·class="o">.</span><span·class="n">dtypes</span><span·class="o">.</span><span·class="n">value_counts</span><span·class="p">()</span>	3555	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[352]:·</span><span·class="n">dft</span><span·class="o">.</span><span·class="n">dtypes</span><span·class="o">.</span><span·class="n">value_counts</span><span·class="p">()</span>
3556	<span·class="gh">Out[352]:·</span><span·class="go"></span>	3556	<span·class="gh">Out[352]:·</span><span·class="go"></span>
3557	<span·class="go">int64·············1</span>	3557	<span·class="go">int64·············1</span>
3558	<span·class="go">bool··············1</span>
3559	<span·class="go">float64···········1</span>
3560	<span·class="go">datetime64[ns]····1</span>
3561	<span·class="go">object············1</span>	3558	<span·class="go">object············1</span>
3562	<span·class="go">~~int8···~~···········1</span>	3559	<span·class="go">float64···········1</span>
3563	<span·class="go">float32···········1</span>	3560	<span·class="go">float32···········1</span>
		3561	<span·class="go">int8··············1</span>
		3562	<span·class="go">datetime64[ns]····1</span>
		3563	<span·class="go">bool··············1</span>
3564	<span·class="go">dtype:·int64</span>	3564	<span·class="go">dtype:·int64</span>
3565	</pre></div>	3565	</pre></div>
3566	</div>	3566	</div>
3567	<p>Numeric·dtypes·will·propagate·and·can·coexist·in·DataFrames.	3567	<p>Numeric·dtypes·will·propagate·and·can·coexist·in·DataFrames.
3568	If·a·dtype·is·passed·(either·directly·via·the·<code·class="docutils·literal·notranslate"><span·class="pre">dtype</span></code>·keyword,·a·passed·<code·class="docutils·literal·notranslate"><span·class="pre">ndarray</span></code>,	3568	If·a·dtype·is·passed·(either·directly·via·the·<code·class="docutils·literal·notranslate"><span·class="pre">dtype</span></code>·keyword,·a·passed·<code·class="docutils·literal·notranslate"><span·class="pre">ndarray</span></code>,
3569	or·a·passed·<code·class="docutils·literal·notranslate"><span·class="pre">Series</span></code>),·then·it·will·be·preserved·in·DataFrame·operations.·Furthermore,	3569	or·a·passed·<code·class="docutils·literal·notranslate"><span·class="pre">Series</span></code>),·then·it·will·be·preserved·in·DataFrame·operations.·Furthermore,
3570	different·numeric·dtypes·will·<strong>NOT</strong>·be·combined.·The·following·example·will·give·you·a·taste.</p>	3570	different·numeric·dtypes·will·<strong>NOT</strong>·be·combined.·The·following·example·will·give·you·a·taste.</p>


Offset 2908, 20 lines modified		Offset 2908, 20 lines modified
2908	4····foo	2908	4····foo
2909	dtype:·object	2909	dtype:·object
2910	The·number·of·columns·of·each·type·in·a·DataFrame·can·be·found·by·calling	2910	The·number·of·columns·of·each·type·in·a·DataFrame·can·be·found·by·calling
2911	DataFrame.dtypes.value_counts().	2911	DataFrame.dtypes.value_counts().
2912	In·[352]:·dft.dtypes.value_counts()	2912	In·[352]:·dft.dtypes.value_counts()
2913	Out[352]:	2913	Out[352]:
2914	int64·············1	2914	int64·············1
2915	bool··············1
2916	float64···········1
2917	datetime64[ns]····1
2918	object············1	2915	object············1
2919	~~int8···~~···········1	2916	float64···········1
2920	float32···········1	2917	float32···········1
		2918	int8··············1
		2919	datetime64[ns]····1
		2920	bool··············1
2921	dtype:·int64	2921	dtype:·int64
2922	Numeric·dtypes·will·propagate·and·can·coexist·in·DataFrames.·If·a·dtype·is	2922	Numeric·dtypes·will·propagate·and·can·coexist·in·DataFrames.·If·a·dtype·is
2923	passed·(either·directly·via·the·dtype·keyword,·a·passed·ndarray,·or·a·passed	2923	passed·(either·directly·via·the·dtype·keyword,·a·passed·ndarray,·or·a·passed
2924	Series),·then·it·will·be·preserved·in·DataFrame·operations.·Furthermore,	2924	Series),·then·it·will·be·preserved·in·DataFrame·operations.·Furthermore,
2925	different·numeric·dtypes·will·NOT·be·combined.·The·following·example·will·give	2925	different·numeric·dtypes·will·NOT·be·combined.·The·following·example·will·give
2926	you·a·taste.	2926	you·a·taste.
2927	In·[353]:·df1·=·pd.DataFrame(np.random.randn(8,·1),·columns=['A'],	2927	In·[353]:·df1·=·pd.DataFrame(np.random.randn(8,·1),·columns=['A'],


Offset 78, 26 lines modified		Offset 78, 26 lines modified
78	In·[7]:·%timeit·df.apply(lambda·x:·integrate_f(x['a'],·x['b'],·x['N']),·axis=1)	78	In·[7]:·%timeit·df.apply(lambda·x:·integrate_f(x['a'],·x['b'],·x['N']),·axis=1)
79	10·loops,·best·of·3:·174·ms·per·loop	79	10·loops,·best·of·3:·174·ms·per·loop
80	But·clearly·this·isnât·fast·enough·for·us.·Letâs·take·a·look·and·see·where	80	But·clearly·this·isnât·fast·enough·for·us.·Letâs·take·a·look·and·see·where
81	the·time·is·spent·during·this·operation·(limited·to·the·most·time·consuming	81	the·time·is·spent·during·this·operation·(limited·to·the·most·time·consuming
82	four·calls)·using·the·prun_ipython_magic_function:	82	four·calls)·using·the·prun_ipython_magic_function:
83	In·[5]:·%prun·-l·4·df.apply(lambda·x:·integrate_f(x['a'],·x['b'],·x['N']),	83	In·[5]:·%prun·-l·4·df.apply(lambda·x:·integrate_f(x['a'],·x['b'],·x['N']),
84	axis=1)··#·noqa·E999	84	axis=1)··#·noqa·E999
85	·········622830·function·calls·(622809·primitive·calls)·in·1.~~019~~·seconds	85	·········622830·function·calls·(622809·primitive·calls)·in·0.990·seconds

86	···Ordered·by:·internal·time	86	···Ordered·by:·internal·time
87	···List·reduced·from·214·to·4·due·to·restriction·<4>	87	···List·reduced·from·214·to·4·due·to·restriction·<4>

88	···ncalls··tottime··percall··cumtime··percall·filename:lineno(function)	88	···ncalls··tottime··percall··cumtime··percall·filename:lineno(function)
89	·····1000····0.~~538~~····0.001····0.~~816~~····0.001·<ipython-input-4-c2a74e076cf0>:1	89	·····1000····0.474····0.000····0.788····0.001·<ipython-input-4-c2a74e076cf0>:1
90	(integrate_f)	90	(integrate_f)
91	···552423····0.~~278~~····0.000····0.~~278~~····0.000·<ipython-input-3-c138bdd570e3>:1	91	···552423····0.314····0.000····0.314····0.000·<ipython-input-3-c138bdd570e3>:1
92	(f)	92	(f)
		93	·····3000····0.022····0.000····0.057····0.000·base.py:2854(get_loc)
93	·····3000····0.021····0.000····0.049····0.000·base.py:4626(_get_values_for_loc)	94	·····3000····0.018····0.000····0.037····0.000·base.py:4626(_get_values_for_loc)
94	·····3000····0.019····0.000····0.019····0.000·managers.py:1584(_block)
95	By·far·the·majority·of·time·is·spend·inside·either·integrate_f·or·f,·hence	95	By·far·the·majority·of·time·is·spend·inside·either·integrate_f·or·f,·hence
96	weâll·concentrate·our·efforts·cythonizing·these·two·functions.	96	weâll·concentrate·our·efforts·cythonizing·these·two·functions.
97	**·Plain·CythonÂ¶·**	97	**·Plain·CythonÂ¶·**
98	First·weâre·going·to·need·to·import·the·Cython·magic·function·to·ipython:	98	First·weâre·going·to·need·to·import·the·Cython·magic·function·to·ipython:
99	In·[6]:·%load_ext·Cython	99	In·[6]:·%load_ext·Cython
100	Now,·letâs·simply·copy·our·functions·over·to·Cython·as·is·(the·suffix·is·here	100	Now,·letâs·simply·copy·our·functions·over·to·Cython·as·is·(the·suffix·is·here
101	to·distinguish·between·function·versions):	101	to·distinguish·between·function·versions):
Offset 136, 26 lines modified		Offset 136, 24 lines modified
136	axis=1)	136	axis=1)
137	10·loops,·best·of·3:·20.3·ms·per·loop	137	10·loops,·best·of·3:·20.3·ms·per·loop
138	Now,·weâre·talking!·Itâs·now·over·ten·times·faster·than·the·original·python	138	Now,·weâre·talking!·Itâs·now·over·ten·times·faster·than·the·original·python
139	implementation,·and·we·havenât·really·modified·the·code.·Letâs·have·another	139	implementation,·and·we·havenât·really·modified·the·code.·Letâs·have·another
140	look·at·whatâs·eating·up·time:	140	look·at·whatâs·eating·up·time:
141	In·[9]:·%prun·-l·4·df.apply(lambda·x:·integrate_f_typed(x['a'],·x['b'],·x	141	In·[9]:·%prun·-l·4·df.apply(lambda·x:·integrate_f_typed(x['a'],·x['b'],·x
142	['N']),·axis=1)	142	['N']),·axis=1)
143	·········70396·function·calls·(70375·primitive·calls)·in·0.~~245~~·seconds	143	·········70396·function·calls·(70375·primitive·calls)·in·0.182·seconds

144	···Ordered·by:·internal·time	144	···Ordered·by:·internal·time
145	···List·reduced·from·208·to·4·due·to·restriction·<4>	145	···List·reduced·from·208·to·4·due·to·restriction·<4>

146	···ncalls··tottime··percall··cumtime··percall·filename:lineno(function)	146	···ncalls··tottime··percall··cumtime··percall·filename:lineno(function)
147	·····3000····0.079····0.000····0.185····0.000·series.py:868(__getitem__)	147	·····3000····0.027····0.000····0.142····0.000·series.py:868(__getitem__)
148	·····3000····0.021····0.000····0.027····0.000·base.py:~~4979~~(~~_may~~be~~_cas~~t_~~indexer~~)	148	·····3000····0.022····0.000····0.053····0.000·base.py:2854(get_loc)
		149	·····3000····0.017····0.000····0.105····0.000·series.py:973(_get_value)
		150	·····3000····0.014····0.000····0.035····0.000·base.py:4626(_get_values_for_loc)
149	·····3000····0.012····0.000····0.012····0.000·{method·'get_loc'·of
150	'pandas._libs.index.IndexEngine'·objects}
151	········1····0.011····0.011····0.238····0.238·apply.py:281
152	(apply_series_generator)
153	**·Using·ndarrayÂ¶·**	151	**·Using·ndarrayÂ¶·**
154	Itâs·calling·seriesâ¦·a·lot!·Itâs·creating·a·Series·from·each·row,·and	152	Itâs·calling·seriesâ¦·a·lot!·Itâs·creating·a·Series·from·each·row,·and
155	get-ting·from·both·the·index·and·the·series·(three·times·for·each·row).	153	get-ting·from·both·the·index·and·the·series·(three·times·for·each·row).
156	Function·calls·are·expensive·in·Python,·so·maybe·we·could·minimize·these·by	154	Function·calls·are·expensive·in·Python,·so·maybe·we·could·minimize·these·by
157	cythonizing·the·apply·part.	155	cythonizing·the·apply·part.
158	Note	156	Note
159	We·are·now·passing·ndarrays·into·the·Cython·function,·fortunately·Cython·plays	157	We·are·now·passing·ndarrays·into·the·Cython·function,·fortunately·Cython·plays
Offset 215, 16 lines modified		Offset 213, 16 lines modified

215	···Ordered·by:·internal·time	213	···Ordered·by:·internal·time
216	···List·reduced·from·59·to·4·due·to·restriction·<4>	214	···List·reduced·from·59·to·4·due·to·restriction·<4>

217	···ncalls··tottime··percall··cumtime··percall·filename:lineno(function)	215	···ncalls··tottime··percall··cumtime··percall·filename:lineno(function)
218	········1····0.002····0.002····0.002····0.002·{built-in·method	216	········1····0.002····0.002····0.002····0.002·{built-in·method
219	_cython_magic_7dc7064016b351e6f537d7542a365f15.apply_integrate_f}	217	_cython_magic_7dc7064016b351e6f537d7542a365f15.apply_integrate_f}
220	········3····0.000····0.000····0.001····0.000·frame.py:2869(__getitem__)
221	········1····0.000····0.000····0.003····0.003·{built-in·method·builtins.exec}	218	········1····0.000····0.000····0.003····0.003·{built-in·method·builtins.exec}
		219	········3····0.000····0.000····0.001····0.000·frame.py:2869(__getitem__)
222	········3····0.000····0.000····0.000····0.000·managers.py:993(iget)	220	········3····0.000····0.000····0.000····0.000·managers.py:993(iget)
223	As·one·might·expect,·the·majority·of·the·time·is·now·spent·in	221	As·one·might·expect,·the·majority·of·the·time·is·now·spent·in
224	apply_integrate_f,·so·if·we·wanted·to·make·anymore·efficiencies·we·must	222	apply_integrate_f,·so·if·we·wanted·to·make·anymore·efficiencies·we·must
225	continue·to·concentrate·our·efforts·here.	223	continue·to·concentrate·our·efforts·here.
226	**·More·advanced·techniquesÂ¶·**	224	**·More·advanced·techniquesÂ¶·**
227	There·is·still·hope·for·improvement.·Hereâs·an·example·of·using·some·more	225	There·is·still·hope·for·improvement.·Hereâs·an·example·of·using·some·more
228	advanced·Cython·techniques:	226	advanced·Cython·techniques:
Offset 422, 29 lines modified		Offset 420, 29 lines modified
422	In·[13]:·nrows,·ncols·=·20000,·100	420	In·[13]:·nrows,·ncols·=·20000,·100

423	In·[14]:·df1,·df2,·df3,·df4·=·[pd.DataFrame(np.random.randn(nrows,·ncols))·for	421	In·[14]:·df1,·df2,·df3,·df4·=·[pd.DataFrame(np.random.randn(nrows,·ncols))·for
424	_·in·range(4)]	422	_·in·range(4)]
425	Now·letâs·compare·adding·them·together·using·plain·olâ·Python·versus·eval	423	Now·letâs·compare·adding·them·together·using·plain·olâ·Python·versus·eval
426	():	424	():
427	In·[15]:·%timeit·df1·+·df2·+·df3·+·df4	425	In·[15]:·%timeit·df1·+·df2·+·df3·+·df4
428	79.6·ms·+-·2.47·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)	426	74.4·ms·+-·7.69·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)
429	In·[16]:·%timeit·pd.eval('df1·+·df2·+·df3·+·df4')	427	In·[16]:·%timeit·pd.eval('df1·+·df2·+·df3·+·df4')
430	36.6·ms·+-·3.49·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)	428	37.1·ms·+-·2.85·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)
431	Now·letâs·do·the·same·thing·but·with·comparisons:	429	Now·letâs·do·the·same·thing·but·with·comparisons:
432	In·[17]:·%timeit·(df1·>·0)·&·(df2·>·0)·&·(df3·>·0)·&·(df4·>·0)	430	In·[17]:·%timeit·(df1·>·0)·&·(df2·>·0)·&·(df3·>·0)·&·(df4·>·0)
433	67.9·ms·+-·6.19·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)	431	64.7·ms·+-·7.25·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)
434	In·[18]:·%timeit·pd.eval('(df1·>·0)·&·(df2·>·0)·&·(df3·>·0)·&·(df4·>·0)')	432	In·[18]:·%timeit·pd.eval('(df1·>·0)·&·(df2·>·0)·&·(df3·>·0)·&·(df4·>·0)')
435	44.3·ms·+-·2.35·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)	433	35.4·ms·+-·3.47·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)
436	eval()·also·works·with·unaligned·pandas·objects:	434	eval()·also·works·with·unaligned·pandas·objects:
437	In·[19]:·s·=·pd.Series(np.random.randn(50))	435	In·[19]:·s·=·pd.Series(np.random.randn(50))

438	In·[20]:·%timeit·df1·+·df2·+·df3·+·df4·+·s	436	In·[20]:·%timeit·df1·+·df2·+·df3·+·df4·+·s
439	128·ms·+-·7.91·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)	437	101·ms·+-·5.42·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)
440	In·[21]:·%timeit·pd.eval('df1·+·df2·+·df3·+·df4·+·s')	438	In·[21]:·%timeit·pd.eval('df1·+·df2·+·df3·+·df4·+·s')
441	44.4·ms·+-·3.39·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)	439	34.8·ms·+-·6.44·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)
442	Note	440	Note
443	Operations·such·as	441	Operations·such·as
444	·····1·and·2··#·would·parse·to·1·&·2,·but·should·evaluate·to·2	442	·····1·and·2··#·would·parse·to·1·&·2,·but·should·evaluate·to·2
445	·····3·or·4··#·would·parse·to·3·\|·4,·but·should·evaluate·to·3	443	·····3·or·4··#·would·parse·to·3·\|·4,·but·should·evaluate·to·3
446	·····~1··#·this·is·okay,·but·slower·when·using·eval	444	·····~1··#·this·is·okay,·but·slower·when·using·eval
447	should·be·performed·in·Python.·An·exception·will·be·raised·if·you·try·to	445	should·be·performed·in·Python.·An·exception·will·be·raised·if·you·try·to
448	perform·any·boolean/bitwise·operations·with·scalar·operands·that·are·not·of	446	perform·any·boolean/bitwise·operations·with·scalar·operands·that·are·not·of
Offset 669, 17 lines modified		Offset 667, 17 lines modified
669	Note	667	Note
670	Using·the·'python'·engine·is·generally·not·useful,·except·for·testing·other	668	Using·the·'python'·engine·is·generally·not·useful,·except·for·testing·other
671	evaluation·engines·against·it.·You·will·achieve·no·performance·benefits·using	669	evaluation·engines·against·it.·You·will·achieve·no·performance·benefits·using
672	eval()·with·engine='python'·and·in·fact·may·incur·a·performance·hit.	670	eval()·with·engine='python'·and·in·fact·may·incur·a·performance·hit.
673	You·can·see·this·by·using·pandas.eval()·with·the·'python'·engine.·It·is·a·bit	671	You·can·see·this·by·using·pandas.eval()·with·the·'python'·engine.·It·is·a·bit
674	slower·(not·by·much)·than·evaluating·the·same·expression·in·Python	672	slower·(not·by·much)·than·evaluating·the·same·expression·in·Python
675	In·[62]:·%timeit·df1·+·df2·+·df3·+·df4	673	In·[62]:·%timeit·df1·+·df2·+·df3·+·df4
676	78.9·ms·+-·4.38·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)	674	77·ms·+-·4.67·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)
677	In·[63]:·%timeit·pd.eval('df1·+·df2·+·df3·+·df4',·engine='python')	675	In·[63]:·%timeit·pd.eval('df1·+·df2·+·df3·+·df4',·engine='python')
678	79.4·ms·+-·5.14·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)	676	68.6·ms·+-·7.25·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)
679	**·pandas.eval()·performanceÂ¶·**	677	**·pandas.eval()·performanceÂ¶·**
680	eval()·is·intended·to·speed·up·certain·kinds·of·operations.·In·particular,	678	eval()·is·intended·to·speed·up·certain·kinds·of·operations.·In·particular,
681	those·operations·involving·complex·expressions·with·large·DataFrame/Series	679	those·operations·involving·complex·expressions·with·large·DataFrame/Series
682	objects·should·see·a·significant·performance·benefit.·Here·is·a·plot·showing	680	objects·should·see·a·significant·performance·benefit.·Here·is·a·plot·showing
683	the·running·time·of·pandas.eval()·as·function·of·the·size·of·the·frame·involved	681	the·running·time·of·pandas.eval()·as·function·of·the·size·of·the·frame·involved
684	in·the·computation.·The·two·lines·are·two·different·engines.	682	in·the·computation.·The·two·lines·are·two·different·engines.
685	[../_images/eval-perf.png]	683	[../_images/eval-perf.png]


Offset 559, 15 lines modified		Offset 559, 15 lines modified
559	<span·class="gp">In·[59]:·</span><span·class="n">grouped_C</span>·<span·class="o">=</span>·<span·class="n">grouped</span><span·class="p">[</span><span·class="s1">'C'</span><span·class="p">]</span>	559	<span·class="gp">In·[59]:·</span><span·class="n">grouped_C</span>·<span·class="o">=</span>·<span·class="n">grouped</span><span·class="p">[</span><span·class="s1">'C'</span><span·class="p">]</span>

560	<span·class="gp">In·[60]:·</span><span·class="n">grouped_D</span>·<span·class="o">=</span>·<span·class="n">grouped</span><span·class="p">[</span><span·class="s1">'D'</span><span·class="p">]</span>	560	<span·class="gp">In·[60]:·</span><span·class="n">grouped_D</span>·<span·class="o">=</span>·<span·class="n">grouped</span><span·class="p">[</span><span·class="s1">'D'</span><span·class="p">]</span>
561	</pre></div>	561	</pre></div>
562	</div>	562	</div>
563	<p>This·is·mainly·syntactic·sugar·for·the·alternative·and·much·more·verbose:</p>	563	<p>This·is·mainly·syntactic·sugar·for·the·alternative·and·much·more·verbose:</p>
564	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[61]:·</span><span·class="n">df</span><span·class="p">[</span><span·class="s1">'C'</span><span·class="p">]</span><span·class="o">.</span><span·class="n">groupby</span><span·class="p">(</span><span·class="n">df</span><span·class="p">[</span><span·class="s1">'A'</span><span·class="p">])</span>	564	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[61]:·</span><span·class="n">df</span><span·class="p">[</span><span·class="s1">'C'</span><span·class="p">]</span><span·class="o">.</span><span·class="n">groupby</span><span·class="p">(</span><span·class="n">df</span><span·class="p">[</span><span·class="s1">'A'</span><span·class="p">])</span>
565	<span·class="gh">Out[61]:·</span><span·class="go"><pandas.core.groupby.generic.SeriesGroupBy·object·at·0x7f~~b720~~e~~67a6~~0></span>	565	<span·class="gh">Out[61]:·</span><span·class="go"><pandas.core.groupby.generic.SeriesGroupBy·object·at·0x7f4b01e398b0></span>
566	</pre></div>	566	</pre></div>
567	</div>	567	</div>
568	<p>Additionally·this·method·avoids·recomputing·the·internal·grouping·information	568	<p>Additionally·this·method·avoids·recomputing·the·internal·grouping·information
569	derived·from·the·passed·key.</p>	569	derived·from·the·passed·key.</p>
570	</div>	570	</div>
571	</div>	571	</div>
572	<div·class="section"·id="iterating-through-groups">	572	<div·class="section"·id="iterating-through-groups">


Offset 444, 15 lines modified		Offset 444, 15 lines modified
444	In·[58]:·grouped·=·df.groupby(['A'])	444	In·[58]:·grouped·=·df.groupby(['A'])

445	In·[59]:·grouped_C·=·grouped['C']	445	In·[59]:·grouped_C·=·grouped['C']

446	In·[60]:·grouped_D·=·grouped['D']	446	In·[60]:·grouped_D·=·grouped['D']
447	This·is·mainly·syntactic·sugar·for·the·alternative·and·much·more·verbose:	447	This·is·mainly·syntactic·sugar·for·the·alternative·and·much·more·verbose:
448	In·[61]:·df['C'].groupby(df['A'])	448	In·[61]:·df['C'].groupby(df['A'])
449	Out[61]:·<pandas.core.groupby.generic.SeriesGroupBy·object·at·0x7f~~b720~~e~~67a6~~0>	449	Out[61]:·<pandas.core.groupby.generic.SeriesGroupBy·object·at·0x7f4b01e398b0>
450	Additionally·this·method·avoids·recomputing·the·internal·grouping·information	450	Additionally·this·method·avoids·recomputing·the·internal·grouping·information
451	derived·from·the·passed·key.	451	derived·from·the·passed·key.
452	***·Iterating·through·groupsÂ¶·***	452	***·Iterating·through·groupsÂ¶·***
453	With·the·GroupBy·object·in·hand,·iterating·through·the·grouped·data·is·very	453	With·the·GroupBy·object·in·hand,·iterating·through·the·grouped·data·is·very
454	natural·and·functions·similarly·to·itertools.groupby():	454	natural·and·functions·similarly·to·itertools.groupby():
455	In·[62]:·grouped·=·df.groupby('A')	455	In·[62]:·grouped·=·df.groupby('A')


Offset 122, 24 lines modified		Offset 122, 24 lines modified
122	<span·class="go">10·loops,·best·of·3:·174·ms·per·loop</span>	122	<span·class="go">10·loops,·best·of·3:·174·ms·per·loop</span>
123	</pre></div>	123	</pre></div>
124	</div>	124	</div>
125	<p>But·clearly·this·isn’t·fast·enough·for·us.·Let’s·take·a·look·and·see·where·the	125	<p>But·clearly·this·isn’t·fast·enough·for·us.·Let’s·take·a·look·and·see·where·the
126	time·is·spent·during·this·operation·(limited·to·the·most·time·consuming	126	time·is·spent·during·this·operation·(limited·to·the·most·time·consuming
127	four·calls)·using·the·<a·class="reference·external"·href="https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-prun">prun·ipython·magic·function</a>:</p>	127	four·calls)·using·the·<a·class="reference·external"·href="https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-prun">prun·ipython·magic·function</a>:</p>
128	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[5]:·</span><span·class="o">%</span><span·class="k">prun</span>·-l·4·df.apply(lambda·x:·integrate_f(x['a'],·x['b'],·x['N']),·axis=1)··#·noqa·E999	128	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[5]:·</span><span·class="o">%</span><span·class="k">prun</span>·-l·4·df.apply(lambda·x:·integrate_f(x['a'],·x['b'],·x['N']),·axis=1)··#·noqa·E999
129	<span·class="go">·········622830·function·calls·(622809·primitive·calls)·in·1.~~019~~·seconds</span>	129	<span·class="go">·········622830·function·calls·(622809·primitive·calls)·in·0.990·seconds</span>

130	<span·class="go">···Ordered·by:·internal·time</span>	130	<span·class="go">···Ordered·by:·internal·time</span>
131	<span·class="go">···List·reduced·from·214·to·4·due·to·restriction·<4></span>	131	<span·class="go">···List·reduced·from·214·to·4·due·to·restriction·<4></span>

132	<span·class="go">···ncalls··tottime··percall··cumtime··percall·filename:lineno(function)</span>	132	<span·class="go">···ncalls··tottime··percall··cumtime··percall·filename:lineno(function)</span>
133	<span·class="go">·····1000····0.~~538~~····0.001····0.~~816~~····0.001·<ipython-input-4-c2a74e076cf0>:1(integrate_f)</span>	133	<span·class="go">·····1000····0.474····0.000····0.788····0.001·<ipython-input-4-c2a74e076cf0>:1(integrate_f)</span>
134	<span·class="go">···552423····0.~~278~~····0.000····0.~~278~~····0.000·<ipython-input-3-c138bdd570e3>:1(f)</span>	134	<span·class="go">···552423····0.314····0.000····0.314····0.000·<ipython-input-3-c138bdd570e3>:1(f)</span>
		135	<span·class="go">·····3000····0.022····0.000····0.057····0.000·base.py:2854(get_loc)</span>
135	<span·class="go">·····3000····0.021····0.000····0.049····0.000·base.py:4626(_get_values_for_loc)</span>	136	<span·class="go">·····3000····0.018····0.000····0.037····0.000·base.py:4626(_get_values_for_loc)</span>
136	<span·class="go">·····3000····0.019····0.000····0.019····0.000·managers.py:1584(_block)</span>
137	</pre></div>	137	</pre></div>
138	</div>	138	</div>
139	<p>By·far·the·majority·of·time·is·spend·inside·either·<code·class="docutils·literal·notranslate"><span·class="pre">integrate_f</span></code>·or·<code·class="docutils·literal·notranslate"><span·class="pre">f</span></code>,	139	<p>By·far·the·majority·of·time·is·spend·inside·either·<code·class="docutils·literal·notranslate"><span·class="pre">integrate_f</span></code>·or·<code·class="docutils·literal·notranslate"><span·class="pre">f</span></code>,
140	hence·we’ll·concentrate·our·efforts·cythonizing·these·two·functions.</p>	140	hence·we’ll·concentrate·our·efforts·cythonizing·these·two·functions.</p>
141	</div>	141	</div>
142	<div·class="section"·id="plain-cython">	142	<div·class="section"·id="plain-cython">
143	<span·id="enhancingperf-plain"></span><h3>Plain·Cython<a·class="headerlink"·href="#plain-cython"·title="Permalink·to·this·headline">¶</a></h3>	143	<span·id="enhancingperf-plain"></span><h3>Plain·Cython<a·class="headerlink"·href="#plain-cython"·title="Permalink·to·this·headline">¶</a></h3>
Offset 193, 24 lines modified		Offset 193, 24 lines modified
193	<span·class="go">10·loops,·best·of·3:·20.3·ms·per·loop</span>	193	<span·class="go">10·loops,·best·of·3:·20.3·ms·per·loop</span>
194	</pre></div>	194	</pre></div>
195	</div>	195	</div>
196	<p>Now,·we’re·talking!·It’s·now·over·ten·times·faster·than·the·original·python	196	<p>Now,·we’re·talking!·It’s·now·over·ten·times·faster·than·the·original·python
197	implementation,·and·we·haven’t·<em>really</em>·modified·the·code.·Let’s·have·another	197	implementation,·and·we·haven’t·<em>really</em>·modified·the·code.·Let’s·have·another
198	look·at·what’s·eating·up·time:</p>	198	look·at·what’s·eating·up·time:</p>
199	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[9]:·</span><span·class="o">%</span><span·class="k">prun</span>·-l·4·df.apply(lambda·x:·integrate_f_typed(x['a'],·x['b'],·x['N']),·axis=1)	199	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[9]:·</span><span·class="o">%</span><span·class="k">prun</span>·-l·4·df.apply(lambda·x:·integrate_f_typed(x['a'],·x['b'],·x['N']),·axis=1)
200	<span·class="go">·········70396·function·calls·(70375·primitive·calls)·in·0.~~245~~·seconds</span>	200	<span·class="go">·········70396·function·calls·(70375·primitive·calls)·in·0.182·seconds</span>

201	<span·class="go">···Ordered·by:·internal·time</span>	201	<span·class="go">···Ordered·by:·internal·time</span>
202	<span·class="go">···List·reduced·from·208·to·4·due·to·restriction·<4></span>	202	<span·class="go">···List·reduced·from·208·to·4·due·to·restriction·<4></span>


Offset 2069, 15 lines modified		Offset 2069, 15 lines modified
2069	<span·class="go">9···0.732206··0.419540··0.604675</span>	2069	<span·class="go">9···0.732206··0.419540··0.604675</span>
2070	<span·class="go">10··0.604466··0.848974··0.896165</span>	2070	<span·class="go">10··0.604466··0.848974··0.896165</span>
2071	<span·class="go">11··0.589168··0.920046··0.732716</span>	2071	<span·class="go">11··0.589168··0.920046··0.732716</span>

2072	<span·class="gp">In·[230]:·</span><span·class="n">expr</span>·<span·class="o">=</span>·<span·class="s1">'0.0·<=·a·<=·c·<=·0.5'</span>	2072	<span·class="gp">In·[230]:·</span><span·class="n">expr</span>·<span·class="o">=</span>·<span·class="s1">'0.0·<=·a·<=·c·<=·0.5'</span>

2073	<span·class="gp">In·[231]:·</span><span·class="nb">map</span><span·class="p">(</span><span·class="k">lambda</span>·<span·class="n">frame</span><span·class="p">:</span>·<span·class="n">frame</span><span·class="o">.</span><span·class="n">query</span><span·class="p">(</span><span·class="n">expr</span><span·class="p">),</span>·<span·class="p">[</span><span·class="n">df</span><span·class="p">,</span>·<span·class="n">df2</span><span·class="p">])</span>	2073	<span·class="gp">In·[231]:·</span><span·class="nb">map</span><span·class="p">(</span><span·class="k">lambda</span>·<span·class="n">frame</span><span·class="p">:</span>·<span·class="n">frame</span><span·class="o">.</span><span·class="n">query</span><span·class="p">(</span><span·class="n">expr</span><span·class="p">),</span>·<span·class="p">[</span><span·class="n">df</span><span·class="p">,</span>·<span·class="n">df2</span><span·class="p">])</span>
2074	<span·class="gh">Out[231]:·</span><span·class="go"><map·at·0x7fb71f~~84d2b~~0></span>	2074	<span·class="gh">Out[231]:·</span><span·class="go"><map·at·0x7f4b01e15c70></span>
2075	</pre></div>	2075	</pre></div>
2076	</div>	2076	</div>
2077	</div>	2077	</div>
2078	<div·class="section"·id="query-python-versus-pandas-syntax-comparison">	2078	<div·class="section"·id="query-python-versus-pandas-syntax-comparison">
2079	<h3><a·class="reference·internal"·href="../reference/api/pandas.DataFrame.query.html#pandas.DataFrame.query"·title="pandas.DataFrame.query"><code·class="xref·py·py-meth·docutils·literal·notranslate"><span·class="pre">query()</span></code></a>·Python·versus·pandas·Syntax·Comparison<a·class="headerlink"·href="#query-python-versus-pandas-syntax-comparison"·title="Permalink·to·this·headline">¶</a></h3>	2079	<h3><a·class="reference·internal"·href="../reference/api/pandas.DataFrame.query.html#pandas.DataFrame.query"·title="pandas.DataFrame.query"><code·class="xref·py·py-meth·docutils·literal·notranslate"><span·class="pre">query()</span></code></a>·Python·versus·pandas·Syntax·Comparison<a·class="headerlink"·href="#query-python-versus-pandas-syntax-comparison"·title="Permalink·to·this·headline">¶</a></h3>
2080	<p>Full·numpy-like·syntax:</p>	2080	<p>Full·numpy-like·syntax:</p>
2081	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[232]:·</span><span·class="n">df</span>·<span·class="o">=</span>·<span·class="n">pd</span><span·class="o">.</span><span·class="n">DataFrame</span><span·class="p">(</span><span·class="n">np</span><span·class="o">.</span><span·class="n">random</span><span·class="o">.</span><span·class="n">randint</span><span·class="p">(</span><span·class="n">n</span><span·class="p">,</span>·<span·class="n">size</span><span·class="o">=</span><span·class="p">(</span><span·class="n">n</span><span·class="p">,</span>·<span·class="mi">3</span><span·class="p">)),</span>·<span·class="n">columns</span><span·class="o">=</span><span·class="nb">list</span><span·class="p">(</span><span·class="s1">'abc'</span><span·class="p">))</span>	2081	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[232]:·</span><span·class="n">df</span>·<span·class="o">=</span>·<span·class="n">pd</span><span·class="o">.</span><span·class="n">DataFrame</span><span·class="p">(</span><span·class="n">np</span><span·class="o">.</span><span·class="n">random</span><span·class="o">.</span><span·class="n">randint</span><span·class="p">(</span><span·class="n">n</span><span·class="p">,</span>·<span·class="n">size</span><span·class="o">=</span><span·class="p">(</span><span·class="n">n</span><span·class="p">,</span>·<span·class="mi">3</span><span·class="p">)),</span>·<span·class="n">columns</span><span·class="o">=</span><span·class="nb">list</span><span·class="p">(</span><span·class="s1">'abc'</span><span·class="p">))</span>


Offset 1773, 15 lines modified		Offset 1773, 15 lines modified
1773	9···0.732206··0.419540··0.604675	1773	9···0.732206··0.419540··0.604675
1774	10··0.604466··0.848974··0.896165	1774	10··0.604466··0.848974··0.896165
1775	11··0.589168··0.920046··0.732716	1775	11··0.589168··0.920046··0.732716

1776	In·[230]:·expr·=·'0.0·<=·a·<=·c·<=·0.5'	1776	In·[230]:·expr·=·'0.0·<=·a·<=·c·<=·0.5'

1777	In·[231]:·map(lambda·frame:·frame.query(expr),·[df,·df2])	1777	In·[231]:·map(lambda·frame:·frame.query(expr),·[df,·df2])
1778	Out[231]:·<map·at·0x7fb71f~~84d2b~~0>	1778	Out[231]:·<map·at·0x7f4b01e15c70>
1779	**·query()·Python·versus·pandas·Syntax·ComparisonÂ¶·**	1779	**·query()·Python·versus·pandas·Syntax·ComparisonÂ¶·**
1780	Full·numpy-like·syntax:	1780	Full·numpy-like·syntax:
1781	In·[232]:·df·=·pd.DataFrame(np.random.randint(n,·size=(n,·3)),·columns=list	1781	In·[232]:·df·=·pd.DataFrame(np.random.randint(n,·size=(n,·3)),·columns=list
1782	('abc'))	1782	('abc'))

1783	In·[233]:·df	1783	In·[233]:·df
1784	Out[233]:	1784	Out[233]:


Offset 2085, 15 lines modified		Offset 2085, 15 lines modified
2085	</pre></div>	2085	</pre></div>
2086	</div>	2086	</div>
2087	<p>By·specifying·a·<code·class="docutils·literal·notranslate"><span·class="pre">chunksize</span></code>·to·<code·class="docutils·literal·notranslate"><span·class="pre">read_csv</span></code>,·the·return	2087	<p>By·specifying·a·<code·class="docutils·literal·notranslate"><span·class="pre">chunksize</span></code>·to·<code·class="docutils·literal·notranslate"><span·class="pre">read_csv</span></code>,·the·return
2088	value·will·be·an·iterable·object·of·type·<code·class="docutils·literal·notranslate"><span·class="pre">TextFileReader</span></code>:</p>	2088	value·will·be·an·iterable·object·of·type·<code·class="docutils·literal·notranslate"><span·class="pre">TextFileReader</span></code>:</p>
2089	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[190]:·</span><span·class="n">reader</span>·<span·class="o">=</span>·<span·class="n">pd</span><span·class="o">.</span><span·class="n">read_csv</span><span·class="p">(</span><span·class="s1">'tmp.sv'</span><span·class="p">,</span>·<span·class="n">sep</span><span·class="o">=</span><span·class="s1">'\|'</span><span·class="p">,</span>·<span·class="n">chunksize</span><span·class="o">=</span><span·class="mi">4</span><span·class="p">)</span>	2089	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[190]:·</span><span·class="n">reader</span>·<span·class="o">=</span>·<span·class="n">pd</span><span·class="o">.</span><span·class="n">read_csv</span><span·class="p">(</span><span·class="s1">'tmp.sv'</span><span·class="p">,</span>·<span·class="n">sep</span><span·class="o">=</span><span·class="s1">'\|'</span><span·class="p">,</span>·<span·class="n">chunksize</span><span·class="o">=</span><span·class="mi">4</span><span·class="p">)</span>

2090	<span·class="gp">In·[191]:·</span><span·class="n">reader</span>	2090	<span·class="gp">In·[191]:·</span><span·class="n">reader</span>
2091	<span·class="gh">Out[191]:·</span><span·class="go"><pandas.io.parsers.TextFileReader·at·0x7f~~b7197758b~~0></span>	2091	<span·class="gh">Out[191]:·</span><span·class="go"><pandas.io.parsers.TextFileReader·at·0x7f4afa69c040></span>

2092	<span·class="gp">In·[192]:·</span><span·class="k">for</span>·<span·class="n">chunk</span>·<span·class="ow">in</span>·<span·class="n">reader</span><span·class="p">:</span>	2092	<span·class="gp">In·[192]:·</span><span·class="k">for</span>·<span·class="n">chunk</span>·<span·class="ow">in</span>·<span·class="n">reader</span><span·class="p">:</span>
2093	<span·class="gp">···.....:·</span>····<span·class="nb">print</span><span·class="p">(</span><span·class="n">chunk</span><span·class="p">)</span>	2093	<span·class="gp">···.....:·</span>····<span·class="nb">print</span><span·class="p">(</span><span·class="n">chunk</span><span·class="p">)</span>
2094	<span·class="gp">···.....:·</span>	2094	<span·class="gp">···.....:·</span>
2095	<span·class="go">···Unnamed:·0·········0·········1·········2·········3</span>	2095	<span·class="go">···Unnamed:·0·········0·········1·········2·········3</span>
2096	<span·class="go">0···········0··0.469112·-0.282863·-1.509059·-1.135632</span>	2096	<span·class="go">0···········0··0.469112·-0.282863·-1.509059·-1.135632</span>
2097	<span·class="go">1···········1··1.212112·-0.173215··0.119209·-1.044236</span>	2097	<span·class="go">1···········1··1.212112·-0.173215··0.119209·-1.044236</span>
Offset 2679, 31 lines modified		Offset 2679, 31 lines modified

2679	<span·class="gp">In·[249]:·</span><span·class="n">dffloats</span>·<span·class="o">=</span>·<span·class="n">pd</span><span·class="o">.</span><span·class="n">DataFrame</span><span·class="p">(</span><span·class="n">randfloats</span><span·class="p">,</span>·<span·class="n">columns</span><span·class="o">=</span><span·class="nb">list</span><span·class="p">(</span><span·class="s1">'ABCDEFGHIJ'</span><span·class="p">))</span>	2679	<span·class="gp">In·[249]:·</span><span·class="n">dffloats</span>·<span·class="o">=</span>·<span·class="n">pd</span><span·class="o">.</span><span·class="n">DataFrame</span><span·class="p">(</span><span·class="n">randfloats</span><span·class="p">,</span>·<span·class="n">columns</span><span·class="o">=</span><span·class="nb">list</span><span·class="p">(</span><span·class="s1">'ABCDEFGHIJ'</span><span·class="p">))</span>

2680	<span·class="gp">In·[250]:·</span><span·class="n">jsonfloats</span>·<span·class="o">=</span>·<span·class="n">dffloats</span><span·class="o">.</span><span·class="n">to_json</span><span·class="p">()</span>	2680	<span·class="gp">In·[250]:·</span><span·class="n">jsonfloats</span>·<span·class="o">=</span>·<span·class="n">dffloats</span><span·class="o">.</span><span·class="n">to_json</span><span·class="p">()</span>
2681	</pre></div>	2681	</pre></div>
2682	</div>	2682	</div>
2683	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[251]:·</span><span·class="o">%</span><span·class="k">timeit</span>·pd.read_json(jsonfloats)	2683	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[251]:·</span><span·class="o">%</span><span·class="k">timeit</span>·pd.read_json(jsonfloats)
2684	<span·class="go">55.2·ms·+-·~~8.79~~·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)</span>	2684	<span·class="go">34.4·ms·+-·12.6·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)</span>
2685	</pre></div>	2685	</pre></div>
2686	</div>	2686	</div>
2687	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[252]:·</span><span·class="o">%</span><span·class="k">timeit</span>·pd.read_json(jsonfloats,·numpy=True)	2687	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[252]:·</span><span·class="o">%</span><span·class="k">timeit</span>·pd.read_json(jsonfloats,·numpy=True)
2688	<span·class="go">43.3·ms·+-·4~~.08~~·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)</span>	2688	<span·class="go">35.2·ms·+-·631·us·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)</span>
2689	</pre></div>	2689	</pre></div>
2690	</div>	2690	</div>
2691	<p>The·speedup·is·less·noticeable·for·smaller·datasets:</p>	2691	<p>The·speedup·is·less·noticeable·for·smaller·datasets:</p>
2692	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[253]:·</span><span·class="n">jsonfloats</span>·<span·class="o">=</span>·<span·class="n">dffloats</span><span·class="o">.</span><span·class="n">head</span><span·class="p">(</span><span·class="mi">100</span><span·class="p">)</span><span·class="o">.</span><span·class="n">to_json</span><span·class="p">()</span>	2692	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[253]:·</span><span·class="n">jsonfloats</span>·<span·class="o">=</span>·<span·class="n">dffloats</span><span·class="o">.</span><span·class="n">head</span><span·class="p">(</span><span·class="mi">100</span><span·class="p">)</span><span·class="o">.</span><span·class="n">to_json</span><span·class="p">()</span>
2693	</pre></div>	2693	</pre></div>
2694	</div>	2694	</div>
2695	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[254]:·</span><span·class="o">%</span><span·class="k">timeit</span>·pd.read_json(jsonfloats)	2695	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[254]:·</span><span·class="o">%</span><span·class="k">timeit</span>·pd.read_json(jsonfloats)
2696	<span·class="go">41·ms·+-·6.76·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)</span>	2696	<span·class="go">33.4·ms·+-·987·us·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)</span>
2697	</pre></div>	2697	</pre></div>
2698	</div>	2698	</div>


Offset 1717, 15 lines modified		Offset 1717, 15 lines modified
1717	8···········8··1.075770·-0.109050··1.643563·-1.469388	1717	8···········8··1.075770·-0.109050··1.643563·-1.469388
1718	9···········9··0.357021·-0.674600·-1.776904·-0.968914	1718	9···········9··0.357021·-0.674600·-1.776904·-0.968914
1719	By·specifying·a·chunksize·to·read_csv,·the·return·value·will·be·an·iterable	1719	By·specifying·a·chunksize·to·read_csv,·the·return·value·will·be·an·iterable
1720	object·of·type·TextFileReader:	1720	object·of·type·TextFileReader:
1721	In·[190]:·reader·=·pd.read_csv('tmp.sv',·sep='\|',·chunksize=4)	1721	In·[190]:·reader·=·pd.read_csv('tmp.sv',·sep='\|',·chunksize=4)

1722	In·[191]:·reader	1722	In·[191]:·reader
1723	Out[191]:·<pandas.io.parsers.TextFileReader·at·0x7f~~b7197758b~~0>	1723	Out[191]:·<pandas.io.parsers.TextFileReader·at·0x7f4afa69c040>

1724	In·[192]:·for·chunk·in·reader:	1724	In·[192]:·for·chunk·in·reader:
1725	···.....:·····print(chunk)	1725	···.....:·····print(chunk)
1726	···.....:	1726	···.....:
1727	···Unnamed:·0·········0·········1·········2·········3	1727	···Unnamed:·0·········0·········1·········2·········3
1728	0···········0··0.469112·-0.282863·-1.509059·-1.135632	1728	0···········0··0.469112·-0.282863·-1.509059·-1.135632
1729	1···········1··1.212112·-0.173215··0.119209·-1.044236	1729	1···········1··1.212112·-0.173215··0.119209·-1.044236
Offset 2220, 23 lines modified		Offset 2220, 23 lines modified

2220	In·[248]:·randfloats.shape·=·(1000,·10)	2220	In·[248]:·randfloats.shape·=·(1000,·10)

2221	In·[249]:·dffloats·=·pd.DataFrame(randfloats,·columns=list('ABCDEFGHIJ'))	2221	In·[249]:·dffloats·=·pd.DataFrame(randfloats,·columns=list('ABCDEFGHIJ'))

2222	In·[250]:·jsonfloats·=·dffloats.to_json()	2222	In·[250]:·jsonfloats·=·dffloats.to_json()
2223	In·[251]:·%timeit·pd.read_json(jsonfloats)	2223	In·[251]:·%timeit·pd.read_json(jsonfloats)
2224	55.2·ms·+-·~~8.79~~·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)	2224	34.4·ms·+-·12.6·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)
2225	In·[252]:·%timeit·pd.read_json(jsonfloats,·numpy=True)	2225	In·[252]:·%timeit·pd.read_json(jsonfloats,·numpy=True)
2226	43.3·ms·+-·4~~.08~~·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)	2226	35.2·ms·+-·631·us·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)
2227	The·speedup·is·less·noticeable·for·smaller·datasets:	2227	The·speedup·is·less·noticeable·for·smaller·datasets:
2228	In·[253]:·jsonfloats·=·dffloats.head(100).to_json()	2228	In·[253]:·jsonfloats·=·dffloats.head(100).to_json()
2229	In·[254]:·%timeit·pd.read_json(jsonfloats)	2229	In·[254]:·%timeit·pd.read_json(jsonfloats)
2230	41·ms·+-·6.76·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)	2230	33.4·ms·+-·987·us·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)
2231	In·[255]:·%timeit·pd.read_json(jsonfloats,·numpy=True)	2231	In·[255]:·%timeit·pd.read_json(jsonfloats,·numpy=True)
2232	28.2·ms·+-·5.05·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)	2232	20.8·ms·+-·6.53·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)
2233	Warning	2233	Warning
2234	Direct·NumPy·decoding·makes·a·number·of·assumptions·and·may·fail·or·produce	2234	Direct·NumPy·decoding·makes·a·number·of·assumptions·and·may·fail·or·produce
2235	unexpected·output·if·these·assumptions·are·not·satisfied:	2235	unexpected·output·if·these·assumptions·are·not·satisfied:
2236	·········*·data·is·numeric.	2236	·········*·data·is·numeric.
2237	·········*·data·is·uniform.·The·dtype·is·sniffed·from·the·first·value	2237	·········*·data·is·uniform.·The·dtype·is·sniffed·from·the·first·value
2238	···········decoded.·A·ValueError·may·be·raised,·or·incorrect·output·may·be	2238	···········decoded.·A·ValueError·may·be·raised,·or·incorrect·output·may·be
2239	···········produced·if·this·condition·is·not·satisfied.	2239	···········produced·if·this·condition·is·not·satisfied.
Offset 2319, 15 lines modified		Offset 2319, 15 lines modified
2319	In·[265]:·df.to_json(orient='records',·lines=True)	2319	In·[265]:·df.to_json(orient='records',·lines=True)
2320	Out[265]:·'{"a":1,"b":2}\n{"a":3,"b":4}'	2320	Out[265]:·'{"a":1,"b":2}\n{"a":3,"b":4}'

2321	#·reader·is·an·iterator·that·returns·`chunksize`·lines·each·iteration	2321	#·reader·is·an·iterator·that·returns·`chunksize`·lines·each·iteration
2322	In·[266]:·reader·=·pd.read_json(StringIO(jsonl),·lines=True,·chunksize=1)	2322	In·[266]:·reader·=·pd.read_json(StringIO(jsonl),·lines=True,·chunksize=1)

2323	In·[267]:·reader	2323	In·[267]:·reader
2324	Out[267]:·<pandas.io.json._json.JsonReader·at·0x7f~~b7196ca~~730>	2324	Out[267]:·<pandas.io.json._json.JsonReader·at·0x7f4afa703760>

2325	In·[268]:·for·chunk·in·reader:	2325	In·[268]:·for·chunk·in·reader:
2326	···.....:·····print(chunk)	2326	···.....:·····print(chunk)
2327	···.....:	2327	···.....:
2328	Empty·DataFrame	2328	Empty·DataFrame
2329	Columns:·[]	2329	Columns:·[]
2330	Index:·[]	2330	Index:·[]
Offset 3949, 18 lines modified		Offset 3949, 18 lines modified
3949	6·-1.177365··0.793644·-0.131959··string····1··True·2001-01-02	3949	6·-1.177365··0.793644·-0.131959··string····1··True·2001-01-02
3950	7··1.236988··0.221252··0.089012··string····1··True·2001-01-02	3950	7··1.236988··0.221252··0.089012··string····1··True·2001-01-02

3951	In·[393]:·df_mixed1.dtypes.value_counts()	3951	In·[393]:·df_mixed1.dtypes.value_counts()
3952	Out[393]:	3952	Out[393]:
3953	float64···········2	3953	float64···········2
3954	int64·············1	3954	int64·············1
3955	bool··············1
3956	datetime64[ns]····1
3957	object············1	3955	object············1
		3956	bool··············1
3958	float32···········1	3957	float32···········1
		3958	datetime64[ns]····1
3959	dtype:·int64	3959	dtype:·int64

3960	#·we·have·provided·a·minimum·string·column·size	3960	#·we·have·provided·a·minimum·string·column·size
3961	In·[394]:·store.root.df_mixed.table	3961	In·[394]:·store.root.df_mixed.table
3962	Out[394]:	3962	Out[394]:
3963	/df_mixed/table·(Table(8,))·''	3963	/df_mixed/table·(Table(8,))·''
3964	··description·:=·{	3964	··description·:=·{


Offset 232, 17 lines modified		Offset 232, 17 lines modified
232	<span·class="go">e··0.119209·-1.044236·-0.861849··bar···True·2012-01-01</span>	232	<span·class="go">e··0.119209·-1.044236·-0.861849··bar···True·2012-01-01</span>
233	<span·class="go">f·-2.104569·-0.494929··1.071804··bar··False·2012-01-01</span>	233	<span·class="go">f·-2.104569·-0.494929··1.071804··bar··False·2012-01-01</span>
234	<span·class="go">h·······NaN·-0.706771·-1.039575··bar···True········NaT</span>	234	<span·class="go">h·······NaN·-0.706771·-1.039575··bar···True········NaT</span>

235	<span·class="gp">In·[20]:·</span><span·class="n">df2</span><span·class="o">.</span><span·class="n">dtypes</span><span·class="o">.</span><span·class="n">value_counts</span><span·class="p">()</span>	235	<span·class="gp">In·[20]:·</span><span·class="n">df2</span><span·class="o">.</span><span·class="n">dtypes</span><span·class="o">.</span><span·class="n">value_counts</span><span·class="p">()</span>
236	<span·class="gh">Out[20]:·</span><span·class="go"></span>	236	<span·class="gh">Out[20]:·</span><span·class="go"></span>
237	<span·class="go">float64···········3</span>	237	<span·class="go">float64···········3</span>
		238	<span·class="go">object············1</span>
238	<span·class="go">bool··············1</span>	239	<span·class="go">bool··············1</span>
239	<span·class="go">datetime64[ns]····1</span>	240	<span·class="go">datetime64[ns]····1</span>
240	<span·class="go">object············1</span>
241	<span·class="go">dtype:·int64</span>	241	<span·class="go">dtype:·int64</span>
242	</pre></div>	242	</pre></div>
243	</div>	243	</div>
244	</div>	244	</div>
245	</div>	245	</div>
246	<div·class="section"·id="inserting-missing-data">	246	<div·class="section"·id="inserting-missing-data">
247	<span·id="missing-inserting"></span><h2>Inserting·missing·data<a·class="headerlink"·href="#inserting-missing-data"·title="Permalink·to·this·headline">¶</a></h2>	247	<span·id="missing-inserting"></span><h2>Inserting·missing·data<a·class="headerlink"·href="#inserting-missing-data"·title="Permalink·to·this·headline">¶</a></h2>


Offset 182, 17 lines modified		Offset 182, 17 lines modified
182	e··0.119209·-1.044236·-0.861849··bar···True·2012-01-01	182	e··0.119209·-1.044236·-0.861849··bar···True·2012-01-01
183	f·-2.104569·-0.494929··1.071804··bar··False·2012-01-01	183	f·-2.104569·-0.494929··1.071804··bar··False·2012-01-01
184	h·······NaN·-0.706771·-1.039575··bar···True········NaT	184	h·······NaN·-0.706771·-1.039575··bar···True········NaT

185	In·[20]:·df2.dtypes.value_counts()	185	In·[20]:·df2.dtypes.value_counts()
186	Out[20]:	186	Out[20]:
187	float64···········3	187	float64···········3
		188	object············1
188	bool··············1	189	bool··············1
189	datetime64[ns]····1	190	datetime64[ns]····1
190	object············1
191	dtype:·int64	191	dtype:·int64
192	***·Inserting·missing·dataÂ¶·***	192	***·Inserting·missing·dataÂ¶·***
193	You·can·insert·missing·values·by·simply·assigning·to·containers.·The·actual	193	You·can·insert·missing·values·by·simply·assigning·to·containers.·The·actual
194	missing·value·used·will·be·chosen·based·on·the·dtype.	194	missing·value·used·will·be·chosen·based·on·the·dtype.
195	For·example,·numeric·containers·will·always·use·NaN·regardless·of·the·missing	195	For·example,·numeric·containers·will·always·use·NaN·regardless·of·the·missing
196	value·type·chosen:	196	value·type·chosen:
197	In·[21]:·s·=·pd.Series([1,·2,·3])	197	In·[21]:·s·=·pd.Series([1,·2,·3])


Offset 314, 16 lines modified		Offset 314, 16 lines modified
314	<span·class="gp">···....:·</span><span·class="n">files</span>·<span·class="o">=</span>·<span·class="n">pathlib</span><span·class="o">.</span><span·class="n">Path</span><span·class="p">(</span><span·class="s2">"data/timeseries/"</span><span·class="p">)</span><span·class="o">.</span><span·class="n">glob</span><span·class="p">(</span><span·class="s2">"ts*.parquet"</span><span·class="p">)</span>	314	<span·class="gp">···....:·</span><span·class="n">files</span>·<span·class="o">=</span>·<span·class="n">pathlib</span><span·class="o">.</span><span·class="n">Path</span><span·class="p">(</span><span·class="s2">"data/timeseries/"</span><span·class="p">)</span><span·class="o">.</span><span·class="n">glob</span><span·class="p">(</span><span·class="s2">"ts*.parquet"</span><span·class="p">)</span>
315	<span·class="gp">···....:·</span><span·class="n">counts</span>·<span·class="o">=</span>·<span·class="n">pd</span><span·class="o">.</span><span·class="n">Series</span><span·class="p">(</span><span·class="n">dtype</span><span·class="o">=</span><span·class="nb">int</span><span·class="p">)</span>	315	<span·class="gp">···....:·</span><span·class="n">counts</span>·<span·class="o">=</span>·<span·class="n">pd</span><span·class="o">.</span><span·class="n">Series</span><span·class="p">(</span><span·class="n">dtype</span><span·class="o">=</span><span·class="nb">int</span><span·class="p">)</span>
316	<span·class="gp">···....:·</span><span·class="k">for</span>·<span·class="n">path</span>·<span·class="ow">in</span>·<span·class="n">files</span><span·class="p">:</span>	316	<span·class="gp">···....:·</span><span·class="k">for</span>·<span·class="n">path</span>·<span·class="ow">in</span>·<span·class="n">files</span><span·class="p">:</span>
317	<span·class="gp">···....:·</span>····<span·class="n">df</span>·<span·class="o">=</span>·<span·class="n">pd</span><span·class="o">.</span><span·class="n">read_parquet</span><span·class="p">(</span><span·class="n">path</span><span·class="p">)</span>	317	<span·class="gp">···....:·</span>····<span·class="n">df</span>·<span·class="o">=</span>·<span·class="n">pd</span><span·class="o">.</span><span·class="n">read_parquet</span><span·class="p">(</span><span·class="n">path</span><span·class="p">)</span>
318	<span·class="gp">···....:·</span>····<span·class="n">counts</span>·<span·class="o">=</span>·<span·class="n">counts</span><span·class="o">.</span><span·class="n">add</span><span·class="p">(</span><span·class="n">df</span><span·class="p">[</span><span·class="s1">'name'</span><span·class="p">]</span><span·class="o">.</span><span·class="n">value_counts</span><span·class="p">(),</span>·<span·class="n">fill_value</span><span·class="o">=</span><span·class="mi">0</span><span·class="p">)</span>	318	<span·class="gp">···....:·</span>····<span·class="n">counts</span>·<span·class="o">=</span>·<span·class="n">counts</span><span·class="o">.</span><span·class="n">add</span><span·class="p">(</span><span·class="n">df</span><span·class="p">[</span><span·class="s1">'name'</span><span·class="p">]</span><span·class="o">.</span><span·class="n">value_counts</span><span·class="p">(),</span>·<span·class="n">fill_value</span><span·class="o">=</span><span·class="mi">0</span><span·class="p">)</span>
319	<span·class="gp">···....:·</span><span·class="n">counts</span><span·class="o">.</span><span·class="n">astype</span><span·class="p">(</span><span·class="nb">int</span><span·class="p">)</span>	319	<span·class="gp">···....:·</span><span·class="n">counts</span><span·class="o">.</span><span·class="n">astype</span><span·class="p">(</span><span·class="nb">int</span><span·class="p">)</span>
320	<span·class="gp">···....:·</span>	320	<span·class="gp">···....:·</span>
321	<span·class="go">CPU·times:·user·1.62·ms,·sys:·0·ns,·total:·1.62·ms</span>	321	<span·class="go">CPU·times:·user·1.66·ms,·sys:·0·ns,·total:·1.66·ms</span>
322	<span·class="go">Wall·time:·2.08·ms</span>	322	<span·class="go">Wall·time:·1.67·ms</span>
323	<span·class="gh">Out[19]:·</span><span·class="go">Series([],·dtype:·int64)</span>	323	<span·class="gh">Out[19]:·</span><span·class="go">Series([],·dtype:·int64)</span>
324	</pre></div>	324	</pre></div>
325	</div>	325	</div>
326	<p>Some·readers,·like·<a·class="reference·internal"·href="../reference/api/pandas.read_csv.html#pandas.read_csv"·title="pandas.read_csv"><code·class="xref·py·py-meth·docutils·literal·notranslate"><span·class="pre">pandas.read_csv()</span></code></a>,·offer·parameters·to·control·the	326	<p>Some·readers,·like·<a·class="reference·internal"·href="../reference/api/pandas.read_csv.html#pandas.read_csv"·title="pandas.read_csv"><code·class="xref·py·py-meth·docutils·literal·notranslate"><span·class="pre">pandas.read_csv()</span></code></a>,·offer·parameters·to·control·the
327	<code·class="docutils·literal·notranslate"><span·class="pre">chunksize</span></code>·when·reading·a·single·file.</p>	327	<code·class="docutils·literal·notranslate"><span·class="pre">chunksize</span></code>·when·reading·a·single·file.</p>
328	<p>Manually·chunking·is·an·OK·option·for·workflows·that·don’t	328	<p>Manually·chunking·is·an·OK·option·for·workflows·that·don’t
329	require·too·sophisticated·of·operations.·Some·operations,·like·<code·class="docutils·literal·notranslate"><span·class="pre">groupby</span></code>,·are	329	require·too·sophisticated·of·operations.·Some·operations,·like·<code·class="docutils·literal·notranslate"><span·class="pre">groupby</span></code>,·are


Offset 279, 16 lines modified		Offset 279, 16 lines modified
279	···....:·files·=·pathlib.Path("data/timeseries/").glob("ts*.parquet")	279	···....:·files·=·pathlib.Path("data/timeseries/").glob("ts*.parquet")
280	···....:·counts·=·pd.Series(dtype=int)	280	···....:·counts·=·pd.Series(dtype=int)
281	···....:·for·path·in·files:	281	···....:·for·path·in·files:
282	···....:·····df·=·pd.read_parquet(path)	282	···....:·····df·=·pd.read_parquet(path)
283	···....:·····counts·=·counts.add(df['name'].value_counts(),·fill_value=0)	283	···....:·····counts·=·counts.add(df['name'].value_counts(),·fill_value=0)
284	···....:·counts.astype(int)	284	···....:·counts.astype(int)
285	···....:	285	···....:
286	CPU·times:·user·1.62·ms,·sys:·0·ns,·total:·1.62·ms	286	CPU·times:·user·1.66·ms,·sys:·0·ns,·total:·1.66·ms
287	Wall·time:·2.08·ms	287	Wall·time:·1.67·ms
288	Out[19]:·Series([],·dtype:·int64)	288	Out[19]:·Series([],·dtype:·int64)
289	Some·readers,·like·pandas.read_csv(),·offer·parameters·to·control·the·chunksize	289	Some·readers,·like·pandas.read_csv(),·offer·parameters·to·control·the·chunksize
290	when·reading·a·single·file.	290	when·reading·a·single·file.
291	Manually·chunking·is·an·OK·option·for·workflows·that·donât·require·too	291	Manually·chunking·is·an·OK·option·for·workflows·that·donât·require·too
292	sophisticated·of·operations.·Some·operations,·like·groupby,·are·much·harder·to	292	sophisticated·of·operations.·Some·operations,·like·groupby,·are·much·harder·to
293	do·chunkwise.·In·these·cases,·you·may·be·better·switching·to·a·different	293	do·chunkwise.·In·these·cases,·you·may·be·better·switching·to·a·different
294	library·that·implements·these·out-of-core·algorithms·for·you.	294	library·that·implements·these·out-of-core·algorithms·for·you.


Offset 77, 38 lines modified		Offset 77, 38 lines modified
77	The·above·output·looks·very·similar·to·the·standard·DataFrame·HTML	77	The·above·output·looks·very·similar·to·the·standard·DataFrame·HTML
78	representation.·But·weâve·done·some·work·behind·the·scenes·to·attach·CSS	78	representation.·But·weâve·done·some·work·behind·the·scenes·to·attach·CSS
79	classes·to·each·cell.·We·can·view·these·by·calling·the·.render·method.	79	classes·to·each·cell.·We·can·view·these·by·calling·the·.render·method.
80	[4]:	80	[4]:
81	df.style.highlight_null().render().split('\n')[:10]	81	df.style.highlight_null().render().split('\n')[:10]
82	[4]:	82	[4]:
83	['<style··type="text/css"·>',	83	['<style··type="text/css"·>',
84	·'#T_~~c5768032~~_ad82_11ee_~~aac0~~_c38~~60ef~~16~~076~~row0_col2,#T_~~c5768032~~_ad82_11ee_~~aac0~~_c38~~60ef~~16~~076~~row3_col3	84	·'#T_ae121fe6_e689_11ef_8b59_cf981f471b6crow0_col2,#T_ae121fe6_e689_11ef_8b59_cf981f471b6crow3_col3
85	{',	85	{',
86	·'············background-color:··red;',	86	·'············background-color:··red;',
87	·'········}</style><table·id="T_~~c5768032~~_ad82_11ee_~~aac0~~_c38~~60ef~~16~~076~~"·><thead>	87	·'········}</style><table·id="T_ae121fe6_e689_11ef_8b59_cf981f471b6c"·><thead>
88	<tr>········<th·class="blank·level0"·></th>········<th·class="col_heading	88	<tr>········<th·class="blank·level0"·></th>········<th·class="col_heading
89	level0·col0"·>A</th>········<th·class="col_heading·level0·col1"·>B</th>	89	level0·col0"·>A</th>········<th·class="col_heading·level0·col1"·>B</th>
90	<th·class="col_heading·level0·col2"·>C</th>········<th·class="col_heading	90	<th·class="col_heading·level0·col2"·>C</th>········<th·class="col_heading
91	level0·col3"·>D</th>········<th·class="col_heading·level0·col4"·>E</th>····</	91	level0·col3"·>D</th>········<th·class="col_heading·level0·col4"·>E</th>····</
92	tr></thead><tbody>',	92	tr></thead><tbody>',
93	·'················<tr>',	93	·'················<tr>',
94	·'························<th	94	·'························<th
95	id="T_~~c5768032~~_ad82_11ee_~~aac0~~_c38~~60ef~~16~~076~~level0_row0"·class="row_heading	95	id="T_ae121fe6_e689_11ef_8b59_cf981f471b6clevel0_row0"·class="row_heading
96	level0·row0"·>0</th>',	96	level0·row0"·>0</th>',
97	·'························<td	97	·'························<td
98	id="T_~~c5768032~~_ad82_11ee_~~aac0~~_c38~~60ef~~16~~076~~row0_col0"·class="data·row0·col0"	98	id="T_ae121fe6_e689_11ef_8b59_cf981f471b6crow0_col0"·class="data·row0·col0"
99	>1.000000</td>',	99	>1.000000</td>',
100	·'························<td	100	·'························<td
101	id="T_~~c5768032~~_ad82_11ee_~~aac0~~_c38~~60ef~~16~~076~~row0_col1"·class="data·row0·col1"	101	id="T_ae121fe6_e689_11ef_8b59_cf981f471b6crow0_col1"·class="data·row0·col1"
102	>1.329212</td>',	102	>1.329212</td>',
103	·'························<td	103	·'························<td
104	id="T_~~c5768032~~_ad82_11ee_~~aac0~~_c38~~60ef~~16~~076~~row0_col2"·class="data·row0·col2"	104	id="T_ae121fe6_e689_11ef_8b59_cf981f471b6crow0_col2"·class="data·row0·col2"
105	>nan</td>',	105	>nan</td>',
106	·'························<td	106	·'························<td
107	id="T_~~c5768032~~_ad82_11ee_~~aac0~~_c38~~60ef~~16~~076~~row0_col3"·class="data·row0·col3"·>-	107	id="T_ae121fe6_e689_11ef_8b59_cf981f471b6crow0_col3"·class="data·row0·col3"·>-
108	0.316280</td>']	108	0.316280</td>']
109	The·row0_col2·is·the·identifier·for·that·particular·cell.·Weâve·also	109	The·row0_col2·is·the·identifier·for·that·particular·cell.·Weâve·also
110	prepended·each·row/column·identifier·with·a·UUID·unique·to·each·DataFrame·so	110	prepended·each·row/column·identifier·with·a·UUID·unique·to·each·DataFrame·so
111	that·the·style·from·one·doesnât·collide·with·the·styling·from·another·within	111	that·the·style·from·one·doesnât·collide·with·the·styling·from·another·within
112	the·same·notebook·or·page·(you·can·set·the·uuid·if·youâd·like·to·tie·together	112	the·same·notebook·or·page·(you·can·set·the·uuid·if·youâd·like·to·tie·together
113	the·styling·of·two·DataFrames).	113	the·styling·of·two·DataFrames).
114	When·writing·style·functions,·you·take·care·of·producing·the·CSS·attribute·/	114	When·writing·style·functions,·you·take·care·of·producing·the·CSS·attribute·/


Offset 2344, 24 lines modified		Offset 2344, 24 lines modified
2344	<span·class="gp">···.....:·</span>	2344	<span·class="gp">···.....:·</span>

2345	<span·class="gp">In·[271]:·</span><span·class="n">cal</span>·<span·class="o">=</span>·<span·class="n">get_calendar</span><span·class="p">(</span><span·class="s1">'ExampleCalendar'</span><span·class="p">)</span>	2345	<span·class="gp">In·[271]:·</span><span·class="n">cal</span>·<span·class="o">=</span>·<span·class="n">get_calendar</span><span·class="p">(</span><span·class="s1">'ExampleCalendar'</span><span·class="p">)</span>

2346	<span·class="gp">In·[272]:·</span><span·class="n">cal</span><span·class="o">.</span><span·class="n">rules</span>	2346	<span·class="gp">In·[272]:·</span><span·class="n">cal</span><span·class="o">.</span><span·class="n">rules</span>
2347	<span·class="gh">Out[272]:·</span><span·class="go"></span>	2347	<span·class="gh">Out[272]:·</span><span·class="go"></span>
2348	<span·class="go">[Holiday:·Memorial·Day·(month=5,·day=31,·offset=<DateOffset:·weekday=MO(-1)>),</span>	2348	<span·class="go">[Holiday:·Memorial·Day·(month=5,·day=31,·offset=<DateOffset:·weekday=MO(-1)>),</span>
2349	<span·class="go">·Holiday:·July·4th·(month=7,·day=4,·observance=<function·nearest_workday·at·0x7f~~b711~~5d60d0>),</span>	2349	<span·class="go">·Holiday:·July·4th·(month=7,·day=4,·observance=<function·nearest_workday·at·0x7f4af05090d0>),</span>
2350	<span·class="go">·Holiday:·Columbus·Day·(month=10,·day=1,·offset=<DateOffset:·weekday=MO(+2)>)]</span>	2350	<span·class="go">·Holiday:·Columbus·Day·(month=10,·day=1,·offset=<DateOffset:·weekday=MO(+2)>)]</span>

2351	<span·class="gp">In·[273]:·</span><span·class="n">new_cal</span>·<span·class="o">=</span>·<span·class="n">HolidayCalendarFactory</span><span·class="p">(</span><span·class="s1">'NewExampleCalendar'</span><span·class="p">,</span>·<span·class="n">cal</span><span·class="p">,</span>·<span·class="n">USLaborDay</span><span·class="p">)</span>	2351	<span·class="gp">In·[273]:·</span><span·class="n">new_cal</span>·<span·class="o">=</span>·<span·class="n">HolidayCalendarFactory</span><span·class="p">(</span><span·class="s1">'NewExampleCalendar'</span><span·class="p">,</span>·<span·class="n">cal</span><span·class="p">,</span>·<span·class="n">USLaborDay</span><span·class="p">)</span>

2352	<span·class="gp">In·[274]:·</span><span·class="n">new_cal</span><span·class="o">.</span><span·class="n">rules</span>	2352	<span·class="gp">In·[274]:·</span><span·class="n">new_cal</span><span·class="o">.</span><span·class="n">rules</span>
2353	<span·class="gh">Out[274]:·</span><span·class="go"></span>	2353	<span·class="gh">Out[274]:·</span><span·class="go"></span>
2354	<span·class="go">[Holiday:·Labor·Day·(month=9,·day=1,·offset=<DateOffset:·weekday=MO(+1)>),</span>	2354	<span·class="go">[Holiday:·Labor·Day·(month=9,·day=1,·offset=<DateOffset:·weekday=MO(+1)>),</span>
2355	<span·class="go">·Holiday:·Memorial·Day·(month=5,·day=31,·offset=<DateOffset:·weekday=MO(-1)>),</span>	2355	<span·class="go">·Holiday:·Memorial·Day·(month=5,·day=31,·offset=<DateOffset:·weekday=MO(-1)>),</span>
2356	<span·class="go">·Holiday:·July·4th·(month=7,·day=4,·observance=<function·nearest_workday·at·0x7f~~b711~~5d60d0>),</span>	2356	<span·class="go">·Holiday:·July·4th·(month=7,·day=4,·observance=<function·nearest_workday·at·0x7f4af05090d0>),</span>
2357	<span·class="go">·Holiday:·Columbus·Day·(month=10,·day=1,·offset=<DateOffset:·weekday=MO(+2)>)]</span>	2357	<span·class="go">·Holiday:·Columbus·Day·(month=10,·day=1,·offset=<DateOffset:·weekday=MO(+2)>)]</span>
2358	</pre></div>	2358	</pre></div>
2359	</div>	2359	</div>
2360	</div>	2360	</div>
2361	</div>	2361	</div>
2362	<div·class="section"·id="time-series-related-instance-methods">	2362	<div·class="section"·id="time-series-related-instance-methods">
2363	<span·id="timeseries-advanced-datetime"></span><h2>Time·series-related·instance·methods<a·class="headerlink"·href="#time-series-related-instance-methods"·title="Permalink·to·this·headline">¶</a></h2>	2363	<span·id="timeseries-advanced-datetime"></span><h2>Time·series-related·instance·methods<a·class="headerlink"·href="#time-series-related-instance-methods"·title="Permalink·to·this·headline">¶</a></h2>


Offset 1745, 26 lines modified		Offset 1745, 26 lines modified

1745	In·[271]:·cal·=·get_calendar('ExampleCalendar')	1745	In·[271]:·cal·=·get_calendar('ExampleCalendar')

1746	In·[272]:·cal.rules	1746	In·[272]:·cal.rules
1747	Out[272]:	1747	Out[272]:
1748	[Holiday:·Memorial·Day·(month=5,·day=31,·offset=<DateOffset:·weekday=MO(-1)>),	1748	[Holiday:·Memorial·Day·(month=5,·day=31,·offset=<DateOffset:·weekday=MO(-1)>),
1749	·Holiday:·July·4th·(month=7,·day=4,·observance=<function·nearest_workday·at	1749	·Holiday:·July·4th·(month=7,·day=4,·observance=<function·nearest_workday·at
1750	0x7f~~b711~~5d60d0>),	1750	0x7f4af05090d0>),
1751	·Holiday:·Columbus·Day·(month=10,·day=1,·offset=<DateOffset:·weekday=MO(+2)>)]	1751	·Holiday:·Columbus·Day·(month=10,·day=1,·offset=<DateOffset:·weekday=MO(+2)>)]

1752	In·[273]:·new_cal·=·HolidayCalendarFactory('NewExampleCalendar',·cal,	1752	In·[273]:·new_cal·=·HolidayCalendarFactory('NewExampleCalendar',·cal,
1753	USLaborDay)	1753	USLaborDay)

1754	In·[274]:·new_cal.rules	1754	In·[274]:·new_cal.rules
1755	Out[274]:	1755	Out[274]:
1756	[Holiday:·Labor·Day·(month=9,·day=1,·offset=<DateOffset:·weekday=MO(+1)>),	1756	[Holiday:·Labor·Day·(month=9,·day=1,·offset=<DateOffset:·weekday=MO(+1)>),
1757	·Holiday:·Memorial·Day·(month=5,·day=31,·offset=<DateOffset:·weekday=MO(-1)>),	1757	·Holiday:·Memorial·Day·(month=5,·day=31,·offset=<DateOffset:·weekday=MO(-1)>),
1758	·Holiday:·July·4th·(month=7,·day=4,·observance=<function·nearest_workday·at	1758	·Holiday:·July·4th·(month=7,·day=4,·observance=<function·nearest_workday·at
1759	0x7f~~b711~~5d60d0>),	1759	0x7f4af05090d0>),
1760	·Holiday:·Columbus·Day·(month=10,·day=1,·offset=<DateOffset:·weekday=MO(+2)>)]	1760	·Holiday:·Columbus·Day·(month=10,·day=1,·offset=<DateOffset:·weekday=MO(+2)>)]
1761	***·Time·series-related·instance·methodsÂ¶·***	1761	***·Time·series-related·instance·methodsÂ¶·***
1762	**·Shifting·/·laggingÂ¶·**	1762	**·Shifting·/·laggingÂ¶·**
1763	One·may·want·to·shift·or·lag·the·values·in·a·time·series·back·and·forward·in	1763	One·may·want·to·shift·or·lag·the·values·in·a·time·series·back·and·forward·in
1764	time.·The·method·for·this·is·shift(),·which·is·available·on·all·of·the·pandas	1764	time.·The·method·for·this·is·shift(),·which·is·available·on·all·of·the·pandas
1765	objects.	1765	objects.
1766	In·[275]:·ts·=·pd.Series(range(len(rng)),·index=rng)	1766	In·[275]:·ts·=·pd.Series(range(len(rng)),·index=rng)


Offset 856, 15 lines modified		Offset 856, 15 lines modified
856	<div·class="section"·id="plotting-on-a-secondary-y-axis">	856	<div·class="section"·id="plotting-on-a-secondary-y-axis">
857	<h3>Plotting·on·a·secondary·y-axis<a·class="headerlink"·href="#plotting-on-a-secondary-y-axis"·title="Permalink·to·this·headline">¶</a></h3>	857	<h3>Plotting·on·a·secondary·y-axis<a·class="headerlink"·href="#plotting-on-a-secondary-y-axis"·title="Permalink·to·this·headline">¶</a></h3>
858	<p>To·plot·data·on·a·secondary·y-axis,·use·the·<code·class="docutils·literal·notranslate"><span·class="pre">secondary_y</span></code>·keyword:</p>	858	<p>To·plot·data·on·a·secondary·y-axis,·use·the·<code·class="docutils·literal·notranslate"><span·class="pre">secondary_y</span></code>·keyword:</p>
859	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[123]:·</span><span·class="n">df</span><span·class="p">[</span><span·class="s1">'A'</span><span·class="p">]</span><span·class="o">.</span><span·class="n">plot</span><span·class="p">()</span>	859	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[123]:·</span><span·class="n">df</span><span·class="p">[</span><span·class="s1">'A'</span><span·class="p">]</span><span·class="o">.</span><span·class="n">plot</span><span·class="p">()</span>
860	<span·class="gh">Out[123]:·</span><span·class="go"><AxesSubplot:></span>	860	<span·class="gh">Out[123]:·</span><span·class="go"><AxesSubplot:></span>

861	<span·class="gp">In·[124]:·</span><span·class="n">df</span><span·class="p">[</span><span·class="s1">'B'</span><span·class="p">]</span><span·class="o">.</span><span·class="n">plot</span><span·class="p">(</span><span·class="n">secondary_y</span><span·class="o">=</span><span·class="kc">True</span><span·class="p">,</span>·<span·class="n">style</span><span·class="o">=</span><span·class="s1">'g'</span><span·class="p">)</span>	861	<span·class="gp">In·[124]:·</span><span·class="n">df</span><span·class="p">[</span><span·class="s1">'B'</span><span·class="p">]</span><span·class="o">.</span><span·class="n">plot</span><span·class="p">(</span><span·class="n">secondary_y</span><span·class="o">=</span><span·class="kc">True</span><span·class="p">,</span>·<span·class="n">style</span><span·class="o">=</span><span·class="s1">'g'</span><span·class="p">)</span>
862	<span·class="gh">Out[124]:·</span><span·class="go"><AxesSubplot:label='a~~76bb1d4~~-3~~543~~-4~~97c~~-bce~~0-f~~b6~~6d4~~b5d03b'></span>	862	<span·class="gh">Out[124]:·</span><span·class="go"><AxesSubplot:label='ab0a2e6a-3895-4a57-9e7c-bd15b5e3b899'></span>
863	</pre></div>	863	</pre></div>
864	</div>	864	</div>
865	<img·alt="../_images/series_plot_secondary_y.png"·src="../_images/series_plot_secondary_y.png"·/>	865	<img·alt="../_images/series_plot_secondary_y.png"·src="../_images/series_plot_secondary_y.png"·/>
866	<p>To·plot·some·columns·in·a·<code·class="docutils·literal·notranslate"><span·class="pre">DataFrame</span></code>,·give·the·column·names·to·the·<code·class="docutils·literal·notranslate"><span·class="pre">secondary_y</span></code>	866	<p>To·plot·some·columns·in·a·<code·class="docutils·literal·notranslate"><span·class="pre">DataFrame</span></code>,·give·the·column·names·to·the·<code·class="docutils·literal·notranslate"><span·class="pre">secondary_y</span></code>
867	keyword:</p>	867	keyword:</p>
868	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[125]:·</span><span·class="n">plt</span><span·class="o">.</span><span·class="n">figure</span><span·class="p">()</span>	868	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[125]:·</span><span·class="n">plt</span><span·class="o">.</span><span·class="n">figure</span><span·class="p">()</span>
869	<span·class="gh">Out[125]:·</span><span·class="go"><Figure·size·640x480·with·0·Axes></span>	869	<span·class="gh">Out[125]:·</span><span·class="go"><Figure·size·640x480·with·0·Axes></span>
Offset 1116, 15 lines modified		Offset 1116, 15 lines modified
1116	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[172]:·</span><span·class="kn">from</span>·<span·class="nn">pandas.plotting</span>·<span·class="kn">import</span>·<span·class="n">table</span>	1116	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[172]:·</span><span·class="kn">from</span>·<span·class="nn">pandas.plotting</span>·<span·class="kn">import</span>·<span·class="n">table</span>

1117	<span·class="gp">In·[173]:·</span><span·class="n">fig</span><span·class="p">,</span>·<span·class="n">ax</span>·<span·class="o">=</span>·<span·class="n">plt</span><span·class="o">.</span><span·class="n">subplots</span><span·class="p">(</span><span·class="mi">1</span><span·class="p">,</span>·<span·class="mi">1</span><span·class="p">)</span>	1117	<span·class="gp">In·[173]:·</span><span·class="n">fig</span><span·class="p">,</span>·<span·class="n">ax</span>·<span·class="o">=</span>·<span·class="n">plt</span><span·class="o">.</span><span·class="n">subplots</span><span·class="p">(</span><span·class="mi">1</span><span·class="p">,</span>·<span·class="mi">1</span><span·class="p">)</span>

1118	<span·class="gp">In·[174]:·</span><span·class="n">table</span><span·class="p">(</span><span·class="n">ax</span><span·class="p">,</span>·<span·class="n">np</span><span·class="o">.</span><span·class="n">round</span><span·class="p">(</span><span·class="n">df</span><span·class="o">.</span><span·class="n">describe</span><span·class="p">(),</span>·<span·class="mi">2</span><span·class="p">),</span>	1118	<span·class="gp">In·[174]:·</span><span·class="n">table</span><span·class="p">(</span><span·class="n">ax</span><span·class="p">,</span>·<span·class="n">np</span><span·class="o">.</span><span·class="n">round</span><span·class="p">(</span><span·class="n">df</span><span·class="o">.</span><span·class="n">describe</span><span·class="p">(),</span>·<span·class="mi">2</span><span·class="p">),</span>
1119	<span·class="gp">···.....:·</span>······<span·class="n">loc</span><span·class="o">=</span><span·class="s1">'upper·right'</span><span·class="p">,</span>·<span·class="n">colWidths</span><span·class="o">=</span><span·class="p">[</span><span·class="mf">0.2</span><span·class="p">,</span>·<span·class="mf">0.2</span><span·class="p">,</span>·<span·class="mf">0.2</span><span·class="p">])</span>	1119	<span·class="gp">···.....:·</span>······<span·class="n">loc</span><span·class="o">=</span><span·class="s1">'upper·right'</span><span·class="p">,</span>·<span·class="n">colWidths</span><span·class="o">=</span><span·class="p">[</span><span·class="mf">0.2</span><span·class="p">,</span>·<span·class="mf">0.2</span><span·class="p">,</span>·<span·class="mf">0.2</span><span·class="p">])</span>
1120	<span·class="gp">···.....:·</span>	1120	<span·class="gp">···.....:·</span>
1121	<span·class="gh">Out[174]:·</span><span·class="go"><matplotlib.table.Table·at·0x7fb~~718~~3b5820></span>	1121	<span·class="gh">Out[174]:·</span><span·class="go"><matplotlib.table.Table·at·0x7f4af3454eb0></span>

1122	<span·class="gp">In·[175]:·</span><span·class="n">df</span><span·class="o">.</span><span·class="n">plot</span><span·class="p">(</span><span·class="n">ax</span><span·class="o">=</span><span·class="n">ax</span><span·class="p">,</span>·<span·class="n">ylim</span><span·class="o">=</span><span·class="p">(</span><span·class="mi">0</span><span·class="p">,</span>·<span·class="mi">2</span><span·class="p">),</span>·<span·class="n">legend</span><span·class="o">=</span><span·class="kc">None</span><span·class="p">)</span>	1122	<span·class="gp">In·[175]:·</span><span·class="n">df</span><span·class="o">.</span><span·class="n">plot</span><span·class="p">(</span><span·class="n">ax</span><span·class="o">=</span><span·class="n">ax</span><span·class="p">,</span>·<span·class="n">ylim</span><span·class="o">=</span><span·class="p">(</span><span·class="mi">0</span><span·class="p">,</span>·<span·class="mi">2</span><span·class="p">),</span>·<span·class="n">legend</span><span·class="o">=</span><span·class="kc">None</span><span·class="p">)</span>
1123	<span·class="gh">Out[175]:·</span><span·class="go"><AxesSubplot:></span>	1123	<span·class="gh">Out[175]:·</span><span·class="go"><AxesSubplot:></span>
1124	</pre></div>	1124	</pre></div>
1125	</div>	1125	</div>
1126	<img·alt="../_images/line_plot_table_describe.png"·src="../_images/line_plot_table_describe.png"·/>	1126	<img·alt="../_images/line_plot_table_describe.png"·src="../_images/line_plot_table_describe.png"·/>
1127	<p><strong>Note</strong>:·You·can·get·table·instances·on·the·axes·using·<code·class="docutils·literal·notranslate"><span·class="pre">axes.tables</span></code>·property·for·further·decorations.·See·the·<a·class="reference·external"·href="https://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes.table">matplotlib·table·documentation</a>·for·more.</p>	1127	<p><strong>Note</strong>:·You·can·get·table·instances·on·the·axes·using·<code·class="docutils·literal·notranslate"><span·class="pre">axes.tables</span></code>·property·for·further·decorations.·See·the·<a·class="reference·external"·href="https://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes.table">matplotlib·table·documentation</a>·for·more.</p>
Offset 1218, 23 lines modified		Offset 1218, 23 lines modified

1218	<span·class="gp">In·[193]:·</span><span·class="n">mstd</span>·<span·class="o">=</span>·<span·class="n">price</span><span·class="o">.</span><span·class="n">rolling</span><span·class="p">(</span><span·class="mi">20</span><span·class="p">)</span><span·class="o">.</span><span·class="n">std</span><span·class="p">()</span>	1218	<span·class="gp">In·[193]:·</span><span·class="n">mstd</span>·<span·class="o">=</span>·<span·class="n">price</span><span·class="o">.</span><span·class="n">rolling</span><span·class="p">(</span><span·class="mi">20</span><span·class="p">)</span><span·class="o">.</span><span·class="n">std</span><span·class="p">()</span>

1219	<span·class="gp">In·[194]:·</span><span·class="n">plt</span><span·class="o">.</span><span·class="n">figure</span><span·class="p">()</span>	1219	<span·class="gp">In·[194]:·</span><span·class="n">plt</span><span·class="o">.</span><span·class="n">figure</span><span·class="p">()</span>
1220	<span·class="gh">Out[194]:·</span><span·class="go"><Figure·size·640x480·with·0·Axes></span>	1220	<span·class="gh">Out[194]:·</span><span·class="go"><Figure·size·640x480·with·0·Axes></span>

1221	<span·class="gp">In·[195]:·</span><span·class="n">plt</span><span·class="o">.</span><span·class="n">plot</span><span·class="p">(</span><span·class="n">price</span><span·class="o">.</span><span·class="n">index</span><span·class="p">,</span>·<span·class="n">price</span><span·class="p">,</span>·<span·class="s1">'k'</span><span·class="p">)</span>	1221	<span·class="gp">In·[195]:·</span><span·class="n">plt</span><span·class="o">.</span><span·class="n">plot</span><span·class="p">(</span><span·class="n">price</span><span·class="o">.</span><span·class="n">index</span><span·class="p">,</span>·<span·class="n">price</span><span·class="p">,</span>·<span·class="s1">'k'</span><span·class="p">)</span>
1222	<span·class="gh">Out[195]:·</span><span·class="go">[<matplotlib.lines.Line2D·at·0x7f~~b710b~~8~~576~~0>]</span>	1222	<span·class="gh">Out[195]:·</span><span·class="go">[<matplotlib.lines.Line2D·at·0x7f4aeba89bb0>]</span>

1223	<span·class="gp">In·[196]:·</span><span·class="n">plt</span><span·class="o">.</span><span·class="n">plot</span><span·class="p">(</span><span·class="n">ma</span><span·class="o">.</span><span·class="n">index</span><span·class="p">,</span>·<span·class="n">ma</span><span·class="p">,</span>·<span·class="s1">'b'</span><span·class="p">)</span>	1223	<span·class="gp">In·[196]:·</span><span·class="n">plt</span><span·class="o">.</span><span·class="n">plot</span><span·class="p">(</span><span·class="n">ma</span><span·class="o">.</span><span·class="n">index</span><span·class="p">,</span>·<span·class="n">ma</span><span·class="p">,</span>·<span·class="s1">'b'</span><span·class="p">)</span>
1224	<span·class="gh">Out[196]:·</span><span·class="go">[<matplotlib.lines.Line2D·at·0x7fb~~710~~ba~~de5~~0>]</span>	1224	<span·class="gh">Out[196]:·</span><span·class="go">[<matplotlib.lines.Line2D·at·0x7f4aebab5730>]</span>

1225	<span·class="gp">In·[197]:·</span><span·class="n">plt</span><span·class="o">.</span><span·class="n">fill_between</span><span·class="p">(</span><span·class="n">mstd</span><span·class="o">.</span><span·class="n">index</span><span·class="p">,</span>·<span·class="n">ma</span>·<span·class="o">-</span>·<span·class="mi">2</span>·<span·class="o"></span>·<span·class="n">mstd</span><span·class="p">,</span>·<span·class="n">ma</span>·<span·class="o">+</span>·<span·class="mi">2</span>·<span·class="o"></span>·<span·class="n">mstd</span><span·class="p">,</span>	1225	<span·class="gp">In·[197]:·</span><span·class="n">plt</span><span·class="o">.</span><span·class="n">fill_between</span><span·class="p">(</span><span·class="n">mstd</span><span·class="o">.</span><span·class="n">index</span><span·class="p">,</span>·<span·class="n">ma</span>·<span·class="o">-</span>·<span·class="mi">2</span>·<span·class="o"></span>·<span·class="n">mstd</span><span·class="p">,</span>·<span·class="n">ma</span>·<span·class="o">+</span>·<span·class="mi">2</span>·<span·class="o"></span>·<span·class="n">mstd</span><span·class="p">,</span>
1226	<span·class="gp">···.....:·</span>·················<span·class="n">color</span><span·class="o">=</span><span·class="s1">'b'</span><span·class="p">,</span>·<span·class="n">alpha</span><span·class="o">=</span><span·class="mf">0.2</span><span·class="p">)</span>	1226	<span·class="gp">···.....:·</span>·················<span·class="n">color</span><span·class="o">=</span><span·class="s1">'b'</span><span·class="p">,</span>·<span·class="n">alpha</span><span·class="o">=</span><span·class="mf">0.2</span><span·class="p">)</span>
1227	<span·class="gp">···.....:·</span>	1227	<span·class="gp">···.....:·</span>
1228	<span·class="gh">Out[197]:·</span><span·class="go"><matplotlib.collections.PolyCollection·at·0x7f~~b710b~~b~~c10~~0></span>	1228	<span·class="gh">Out[197]:·</span><span·class="go"><matplotlib.collections.PolyCollection·at·0x7f4aebab59a0></span>
1229	</pre></div>	1229	</pre></div>
1230	</div>	1230	</div>
1231	<img·alt="../_images/bollinger.png"·src="../_images/bollinger.png"·/>	1231	<img·alt="../_images/bollinger.png"·src="../_images/bollinger.png"·/>
1232	</div>	1232	</div>
1233	<div·class="section"·id="plotting-backends">	1233	<div·class="section"·id="plotting-backends">
1234	<h2>Plotting·backends<a·class="headerlink"·href="#plotting-backends"·title="Permalink·to·this·headline">¶</a></h2>	1234	<h2>Plotting·backends<a·class="headerlink"·href="#plotting-backends"·title="Permalink·to·this·headline">¶</a></h2>
1235	<p>Starting·in·version·0.25,·pandas·can·be·extended·with·third-party·plotting·backends.·The	1235	<p>Starting·in·version·0.25,·pandas·can·be·extended·with·third-party·plotting·backends.·The


Offset 602, 15 lines modified		Offset 602, 15 lines modified
602	See·also·the·logx·and·loglog·keyword·arguments.	602	See·also·the·logx·and·loglog·keyword·arguments.
603	**·Plotting·on·a·secondary·y-axisÂ¶·**	603	**·Plotting·on·a·secondary·y-axisÂ¶·**
604	To·plot·data·on·a·secondary·y-axis,·use·the·secondary_y·keyword:	604	To·plot·data·on·a·secondary·y-axis,·use·the·secondary_y·keyword:
605	In·[123]:·df['A'].plot()	605	In·[123]:·df['A'].plot()
606	Out[123]:·<AxesSubplot:>	606	Out[123]:·<AxesSubplot:>

607	In·[124]:·df['B'].plot(secondary_y=True,·style='g')	607	In·[124]:·df['B'].plot(secondary_y=True,·style='g')
608	Out[124]:·<AxesSubplot:label='a~~76bb1d4~~-3~~543~~-4~~97c~~-bce~~0-f~~b6~~6d4~~b5d03b'>	608	Out[124]:·<AxesSubplot:label='ab0a2e6a-3895-4a57-9e7c-bd15b5e3b899'>
609	[../_images/series_plot_secondary_y.png]	609	[../_images/series_plot_secondary_y.png]
610	To·plot·some·columns·in·a·DataFrame,·give·the·column·names·to·the·secondary_y	610	To·plot·some·columns·in·a·DataFrame,·give·the·column·names·to·the·secondary_y
611	keyword:	611	keyword:
612	In·[125]:·plt.figure()	612	In·[125]:·plt.figure()
613	Out[125]:·<Figure·size·640x480·with·0·Axes>	613	Out[125]:·<Figure·size·640x480·with·0·Axes>

614	In·[126]:·ax·=·df.plot(secondary_y=['A',·'B'])	614	In·[126]:·ax·=·df.plot(secondary_y=['A',·'B'])
Offset 827, 15 lines modified		Offset 827, 15 lines modified
827	In·[172]:·from·pandas.plotting·import·table	827	In·[172]:·from·pandas.plotting·import·table

828	In·[173]:·fig,·ax·=·plt.subplots(1,·1)	828	In·[173]:·fig,·ax·=·plt.subplots(1,·1)

829	In·[174]:·table(ax,·np.round(df.describe(),·2),	829	In·[174]:·table(ax,·np.round(df.describe(),·2),
830	···.....:·······loc='upper·right',·colWidths=[0.2,·0.2,·0.2])	830	···.....:·······loc='upper·right',·colWidths=[0.2,·0.2,·0.2])
831	···.....:	831	···.....:
832	Out[174]:·<matplotlib.table.Table·at·0x7fb~~718~~3b5820>	832	Out[174]:·<matplotlib.table.Table·at·0x7f4af3454eb0>

833	In·[175]:·df.plot(ax=ax,·ylim=(0,·2),·legend=None)	833	In·[175]:·df.plot(ax=ax,·ylim=(0,·2),·legend=None)
834	Out[175]:·<AxesSubplot:>	834	Out[175]:·<AxesSubplot:>
835	[../_images/line_plot_table_describe.png]	835	[../_images/line_plot_table_describe.png]
836	Note:·You·can·get·table·instances·on·the·axes·using·axes.tables·property·for	836	Note:·You·can·get·table·instances·on·the·axes·using·axes.tables·property·for
837	further·decorations.·See·the·matplotlib_table_documentation·for·more.	837	further·decorations.·See·the·matplotlib_table_documentation·for·more.
838	**·ColormapsÂ¶·**	838	**·ColormapsÂ¶·**
Offset 914, 23 lines modified		Offset 914, 23 lines modified

914	In·[193]:·mstd·=·price.rolling(20).std()	914	In·[193]:·mstd·=·price.rolling(20).std()

915	In·[194]:·plt.figure()	915	In·[194]:·plt.figure()
916	Out[194]:·<Figure·size·640x480·with·0·Axes>	916	Out[194]:·<Figure·size·640x480·with·0·Axes>

917	In·[195]:·plt.plot(price.index,·price,·'k')	917	In·[195]:·plt.plot(price.index,·price,·'k')
918	Out[195]:·[<matplotlib.lines.Line2D·at·0x7f~~b710b~~8~~576~~0>]	918	Out[195]:·[<matplotlib.lines.Line2D·at·0x7f4aeba89bb0>]

919	In·[196]:·plt.plot(ma.index,·ma,·'b')	919	In·[196]:·plt.plot(ma.index,·ma,·'b')
920	Out[196]:·[<matplotlib.lines.Line2D·at·0x7fb~~710~~ba~~de5~~0>]	920	Out[196]:·[<matplotlib.lines.Line2D·at·0x7f4aebab5730>]

921	In·[197]:·plt.fill_between(mstd.index,·ma·-·2··mstd,·ma·+·2··mstd,	921	In·[197]:·plt.fill_between(mstd.index,·ma·-·2··mstd,·ma·+·2··mstd,
922	···.....:··················color='b',·alpha=0.2)	922	···.....:··················color='b',·alpha=0.2)
923	···.....:	923	···.....:
924	Out[197]:·<matplotlib.collections.PolyCollection·at·0x7f~~b710b~~b~~c10~~0>	924	Out[197]:·<matplotlib.collections.PolyCollection·at·0x7f4aebab59a0>
925	[../_images/bollinger.png]	925	[../_images/bollinger.png]
926	***·Plotting·backendsÂ¶·***	926	***·Plotting·backendsÂ¶·***
927	Starting·in·version·0.25,·pandas·can·be·extended·with·third-party·plotting	927	Starting·in·version·0.25,·pandas·can·be·extended·with·third-party·plotting
928	backends.·The·main·idea·is·letting·users·select·a·plotting·backend·different	928	backends.·The·main·idea·is·letting·users·select·a·plotting·backend·different
929	than·the·provided·one·based·on·Matplotlib.	929	than·the·provided·one·based·on·Matplotlib.
930	This·can·be·done·by·passsing·âbackend.moduleâ·as·the·argument·backend·in	930	This·can·be·done·by·passsing·âbackend.moduleâ·as·the·argument·backend·in
931	plot·function.·For·example:	931	plot·function.·For·example:


Offset 1010, 20 lines modified		Offset 1010, 20 lines modified
1010	<span·class="gp">In·[103]:·</span><span·class="n">df1</span><span·class="p">,</span>·<span·class="n">df2</span><span·class="p">,</span>·<span·class="n">df3</span><span·class="p">,</span>·<span·class="n">df4</span>·<span·class="o">=</span>·<span·class="p">[</span><span·class="n">pd</span><span·class="o">.</span><span·class="n">DataFrame</span><span·class="p">(</span><span·class="n">np</span><span·class="o">.</span><span·class="n">random</span><span·class="o">.</span><span·class="n">randn</span><span·class="p">(</span><span·class="n">nrows</span><span·class="p">,</span>·<span·class="n">ncols</span><span·class="p">))</span>	1010	<span·class="gp">In·[103]:·</span><span·class="n">df1</span><span·class="p">,</span>·<span·class="n">df2</span><span·class="p">,</span>·<span·class="n">df3</span><span·class="p">,</span>·<span·class="n">df4</span>·<span·class="o">=</span>·<span·class="p">[</span><span·class="n">pd</span><span·class="o">.</span><span·class="n">DataFrame</span><span·class="p">(</span><span·class="n">np</span><span·class="o">.</span><span·class="n">random</span><span·class="o">.</span><span·class="n">randn</span><span·class="p">(</span><span·class="n">nrows</span><span·class="p">,</span>·<span·class="n">ncols</span><span·class="p">))</span>
1011	<span·class="gp">···.....:·</span>······················<span·class="k">for</span>·<span·class="n">_</span>·<span·class="ow">in</span>·<span·class="nb">range</span><span·class="p">(</span><span·class="mi">4</span><span·class="p">)]</span>	1011	<span·class="gp">···.....:·</span>······················<span·class="k">for</span>·<span·class="n">_</span>·<span·class="ow">in</span>·<span·class="nb">range</span><span·class="p">(</span><span·class="mi">4</span><span·class="p">)]</span>
1012	<span·class="gp">···.....:·</span>	1012	<span·class="gp">···.....:·</span>
1013	</pre></div>	1013	</pre></div>
1014	</div>	1014	</div>
1015	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="go">#·eval·with·NumExpr·backend</span>	1015	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="go">#·eval·with·NumExpr·backend</span>
1016	<span·class="gp">In·[104]:·</span><span·class="o">%</span><span·class="k">timeit</span>·pd.eval('df1·+·df2·+·df3·+·df4')	1016	<span·class="gp">In·[104]:·</span><span·class="o">%</span><span·class="k">timeit</span>·pd.eval('df1·+·df2·+·df3·+·df4')
1017	<span·class="go">23.8·ms·+-·1.16·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)</span>	1017	<span·class="go">24.4·ms·+-·2.31·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)</span>
1018	</pre></div>	1018	</pre></div>
1019	</div>	1019	</div>
1020	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="go">#·pure·Python·evaluation</span>	1020	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="go">#·pure·Python·evaluation</span>
1021	<span·class="gp">In·[105]:·</span><span·class="o">%</span><span·class="k">timeit</span>·df1·+·df2·+·df3·+·df4	1021	<span·class="gp">In·[105]:·</span><span·class="o">%</span><span·class="k">timeit</span>·df1·+·df2·+·df3·+·df4
1022	<span·class="go">51.1·ms·+-·4.21·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)</span>	1022	<span·class="go">55.6·ms·+-·5.98·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)</span>
1023	</pre></div>	1023	</pre></div>
1024	</div>	1024	</div>
1025	<p>For·more·details,·see·the·<a·class="reference·internal"·href="../user_guide/enhancingperf.html#enhancingperf-eval"><span·class="std·std-ref">the·docs</span></a></p>	1025	<p>For·more·details,·see·the·<a·class="reference·internal"·href="../user_guide/enhancingperf.html#enhancingperf-eval"><span·class="std·std-ref">the·docs</span></a></p>
1026	</li>	1026	</li>
1027	<li><p>Similar·to·<code·class="docutils·literal·notranslate"><span·class="pre">pandas.eval</span></code>,·<a·class="reference·internal"·href="../reference/api/pandas.DataFrame.html#pandas.DataFrame"·title="pandas.DataFrame"><code·class="xref·py·py-class·docutils·literal·notranslate"><span·class="pre">DataFrame</span></code></a>·has·a·new	1027	<li><p>Similar·to·<code·class="docutils·literal·notranslate"><span·class="pre">pandas.eval</span></code>,·<a·class="reference·internal"·href="../reference/api/pandas.DataFrame.html#pandas.DataFrame"·title="pandas.DataFrame"><code·class="xref·py·py-class·docutils·literal·notranslate"><span·class="pre">DataFrame</span></code></a>·has·a·new
1028	<code·class="docutils·literal·notranslate"><span·class="pre">DataFrame.eval</span></code>·method·that·evaluates·an·expression·in·the·context·of	1028	<code·class="docutils·literal·notranslate"><span·class="pre">DataFrame.eval</span></code>·method·that·evaluates·an·expression·in·the·context·of
1029	the·<code·class="docutils·literal·notranslate"><span·class="pre">DataFrame</span></code>.·For·example,</p>	1029	the·<code·class="docutils·literal·notranslate"><span·class="pre">DataFrame</span></code>.·For·example,</p>


Offset 864, 18 lines modified		Offset 864, 18 lines modified

864	······In·[103]:·df1,·df2,·df3,·df4·=·[pd.DataFrame(np.random.randn(nrows,	864	······In·[103]:·df1,·df2,·df3,·df4·=·[pd.DataFrame(np.random.randn(nrows,
865	······ncols))	865	······ncols))
866	·········.....:·······················for·_·in·range(4)]	866	·········.....:·······················for·_·in·range(4)]
867	·········.....:	867	·········.....:
868	······#·eval·with·NumExpr·backend	868	······#·eval·with·NumExpr·backend
869	······In·[104]:·%timeit·pd.eval('df1·+·df2·+·df3·+·df4')	869	······In·[104]:·%timeit·pd.eval('df1·+·df2·+·df3·+·df4')
870	······23.8·ms·+-·1.16·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)	870	······24.4·ms·+-·2.31·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)
871	······#·pure·Python·evaluation	871	······#·pure·Python·evaluation
872	······In·[105]:·%timeit·df1·+·df2·+·df3·+·df4	872	······In·[105]:·%timeit·df1·+·df2·+·df3·+·df4
873	······51.1·ms·+-·4.21·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)	873	······55.6·ms·+-·5.98·ms·per·loop·(mean·+-·std.·dev.·of·7·runs,·10·loops·each)
874	······For·more·details,·see·the·the_docs	874	······For·more·details,·see·the·the_docs
875	····*·Similar·to·pandas.eval,·DataFrame·has·a·new·DataFrame.eval·method·that	875	····*·Similar·to·pandas.eval,·DataFrame·has·a·new·DataFrame.eval·method·that
876	······evaluates·an·expression·in·the·context·of·the·DataFrame.·For·example,	876	······evaluates·an·expression·in·the·context·of·the·DataFrame.·For·example,
877	······In·[106]:·df·=·pd.DataFrame(np.random.randn(10,·2),·columns=['a',·'b'])	877	······In·[106]:·df·=·pd.DataFrame(np.random.randn(10,·2),·columns=['a',·'b'])

878	······In·[107]:·df.eval('a·+·b')	878	······In·[107]:·df.eval('a·+·b')
879	······Out[107]:	879	······Out[107]:


Offset 1217, 25 lines modified		Offset 1217, 25 lines modified
1217	<span·class="gp">···....:·</span>	1217	<span·class="gp">···....:·</span>

1218	<span·class="gp">In·[96]:·</span><span·class="n">df</span><span·class="o">.</span><span·class="n">describe</span><span·class="p">(</span><span·class="n">include</span><span·class="o">=</span><span·class="p">[</span><span·class="s2">"object"</span><span·class="p">])</span>	1218	<span·class="gp">In·[96]:·</span><span·class="n">df</span><span·class="o">.</span><span·class="n">describe</span><span·class="p">(</span><span·class="n">include</span><span·class="o">=</span><span·class="p">[</span><span·class="s2">"object"</span><span·class="p">])</span>
1219	<span·class="gh">Out[96]:·</span><span·class="go"></span>	1219	<span·class="gh">Out[96]:·</span><span·class="go"></span>
1220	<span·class="go">·······catA·catB</span>	1220	<span·class="go">·······catA·catB</span>
1221	<span·class="go">count····24···24</span>	1221	<span·class="go">count····24···24</span>
1222	<span·class="go">unique····2····4</span>	1222	<span·class="go">unique····2····4</span>
1223	<span·class="go">top·····foo····a</span>	1223	<span·class="go">top·····foo····c</span>
1224	<span·class="go">freq·····16····6</span>	1224	<span·class="go">freq·····16····6</span>

1225	<span·class="go">[4·rows·x·2·columns]</span>	1225	<span·class="go">[4·rows·x·2·columns]</span>

1226	<span·class="gp">In·[97]:·</span><span·class="n">df</span><span·class="o">.</span><span·class="n">describe</span><span·class="p">(</span><span·class="n">include</span><span·class="o">=</span><span·class="p">[</span><span·class="s2">"number"</span><span·class="p">,</span>·<span·class="s2">"object"</span><span·class="p">],</span>·<span·class="n">exclude</span><span·class="o">=</span><span·class="p">[</span><span·class="s2">"float"</span><span·class="p">])</span>	1226	<span·class="gp">In·[97]:·</span><span·class="n">df</span><span·class="o">.</span><span·class="n">describe</span><span·class="p">(</span><span·class="n">include</span><span·class="o">=</span><span·class="p">[</span><span·class="s2">"number"</span><span·class="p">,</span>·<span·class="s2">"object"</span><span·class="p">],</span>·<span·class="n">exclude</span><span·class="o">=</span><span·class="p">[</span><span·class="s2">"float"</span><span·class="p">])</span>
1227	<span·class="gh">Out[97]:·</span><span·class="go"></span>	1227	<span·class="gh">Out[97]:·</span><span·class="go"></span>
1228	<span·class="go">·······catA·catB·······numC</span>	1228	<span·class="go">·······catA·catB·······numC</span>
1229	<span·class="go">count····24···24··24.000000</span>	1229	<span·class="go">count····24···24··24.000000</span>
1230	<span·class="go">unique····2····4········NaN</span>	1230	<span·class="go">unique····2····4········NaN</span>
1231	<span·class="go">top·····foo····a········NaN</span>	1231	<span·class="go">top·····foo····c········NaN</span>
1232	<span·class="go">freq·····16····6········NaN</span>	1232	<span·class="go">freq·····16····6········NaN</span>
1233	<span·class="go">mean····NaN··NaN··11.500000</span>	1233	<span·class="go">mean····NaN··NaN··11.500000</span>
1234	<span·class="go">std·····NaN··NaN···7.071068</span>	1234	<span·class="go">std·····NaN··NaN···7.071068</span>
1235	<span·class="go">min·····NaN··NaN···0.000000</span>	1235	<span·class="go">min·····NaN··NaN···0.000000</span>
1236	<span·class="go">25%·····NaN··NaN···5.750000</span>	1236	<span·class="go">25%·····NaN··NaN···5.750000</span>
1237	<span·class="go">50%·····NaN··NaN··11.500000</span>	1237	<span·class="go">50%·····NaN··NaN··11.500000</span>
1238	<span·class="go">75%·····NaN··NaN··17.250000</span>	1238	<span·class="go">75%·····NaN··NaN··17.250000</span>
Offset 1246, 15 lines modified		Offset 1246, 15 lines modified
1246	</div>	1246	</div>
1247	<p>Requesting·all·columns·is·possible·with·the·shorthand·‘all’</p>	1247	<p>Requesting·all·columns·is·possible·with·the·shorthand·‘all’</p>
1248	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[98]:·</span><span·class="n">df</span><span·class="o">.</span><span·class="n">describe</span><span·class="p">(</span><span·class="n">include</span><span·class="o">=</span><span·class="s1">'all'</span><span·class="p">)</span>	1248	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[98]:·</span><span·class="n">df</span><span·class="o">.</span><span·class="n">describe</span><span·class="p">(</span><span·class="n">include</span><span·class="o">=</span><span·class="s1">'all'</span><span·class="p">)</span>
1249	<span·class="gh">Out[98]:·</span><span·class="go"></span>	1249	<span·class="gh">Out[98]:·</span><span·class="go"></span>
1250	<span·class="go">·······catA·catB·······numC·······numD</span>	1250	<span·class="go">·······catA·catB·······numC·······numD</span>
1251	<span·class="go">count····24···24··24.000000··24.000000</span>	1251	<span·class="go">count····24···24··24.000000··24.000000</span>
1252	<span·class="go">unique····2····4········NaN········NaN</span>	1252	<span·class="go">unique····2····4········NaN········NaN</span>
1253	<span·class="go">top·····foo····a········NaN········NaN</span>	1253	<span·class="go">top·····foo····c········NaN········NaN</span>
1254	<span·class="go">freq·····16····6········NaN········NaN</span>	1254	<span·class="go">freq·····16····6········NaN········NaN</span>
1255	<span·class="go">mean····NaN··NaN··11.500000··12.000000</span>	1255	<span·class="go">mean····NaN··NaN··11.500000··12.000000</span>
1256	<span·class="go">std·····NaN··NaN···7.071068···7.071068</span>	1256	<span·class="go">std·····NaN··NaN···7.071068···7.071068</span>
1257	<span·class="go">min·····NaN··NaN···0.000000···0.500000</span>	1257	<span·class="go">min·····NaN··NaN···0.000000···0.500000</span>
1258	<span·class="go">25%·····NaN··NaN···5.750000···6.250000</span>	1258	<span·class="go">25%·····NaN··NaN···5.750000···6.250000</span>
1259	<span·class="go">50%·····NaN··NaN··11.500000··12.000000</span>	1259	<span·class="go">50%·····NaN··NaN··11.500000··12.000000</span>
1260	<span·class="go">75%·····NaN··NaN··17.250000··17.750000</span>	1260	<span·class="go">75%·····NaN··NaN··17.250000··17.750000</span>


Offset 1091, 25 lines modified		Offset 1091, 25 lines modified
1091	·········....:	1091	·········....:

1092	······In·[96]:·df.describe(include=["object"])	1092	······In·[96]:·df.describe(include=["object"])
1093	······Out[96]:	1093	······Out[96]:
1094	·············catA·catB	1094	·············catA·catB
1095	······count····24···24	1095	······count····24···24
1096	······unique····2····4	1096	······unique····2····4
1097	······top·····foo····a	1097	······top·····foo····c
1098	······freq·····16····6	1098	······freq·····16····6

1099	······[4·rows·x·2·columns]	1099	······[4·rows·x·2·columns]

1100	······In·[97]:·df.describe(include=["number",·"object"],·exclude=["float"])	1100	······In·[97]:·df.describe(include=["number",·"object"],·exclude=["float"])
1101	······Out[97]:	1101	······Out[97]:
1102	·············catA·catB·······numC	1102	·············catA·catB·······numC
1103	······count····24···24··24.000000	1103	······count····24···24··24.000000
1104	······unique····2····4········NaN	1104	······unique····2····4········NaN
1105	······top·····foo····a········NaN	1105	······top·····foo····c········NaN
1106	······freq·····16····6········NaN	1106	······freq·····16····6········NaN
1107	······mean····NaN··NaN··11.500000	1107	······mean····NaN··NaN··11.500000
1108	······std·····NaN··NaN···7.071068	1108	······std·····NaN··NaN···7.071068
1109	······min·····NaN··NaN···0.000000	1109	······min·····NaN··NaN···0.000000
1110	······25%·····NaN··NaN···5.750000	1110	······25%·····NaN··NaN···5.750000
1111	······50%·····NaN··NaN··11.500000	1111	······50%·····NaN··NaN··11.500000
1112	······75%·····NaN··NaN··17.250000	1112	······75%·····NaN··NaN··17.250000
Offset 1118, 15 lines modified		Offset 1118, 15 lines modified
1118	······[11·rows·x·3·columns]	1118	······[11·rows·x·3·columns]
1119	······Requesting·all·columns·is·possible·with·the·shorthand·âallâ	1119	······Requesting·all·columns·is·possible·with·the·shorthand·âallâ
1120	······In·[98]:·df.describe(include='all')	1120	······In·[98]:·df.describe(include='all')
1121	······Out[98]:	1121	······Out[98]:
1122	·············catA·catB·······numC·······numD	1122	·············catA·catB·······numC·······numD
1123	······count····24···24··24.000000··24.000000	1123	······count····24···24··24.000000··24.000000
1124	······unique····2····4········NaN········NaN	1124	······unique····2····4········NaN········NaN
1125	······top·····foo····a········NaN········NaN	1125	······top·····foo····c········NaN········NaN
1126	······freq·····16····6········NaN········NaN	1126	······freq·····16····6········NaN········NaN
1127	······mean····NaN··NaN··11.500000··12.000000	1127	······mean····NaN··NaN··11.500000··12.000000
1128	······std·····NaN··NaN···7.071068···7.071068	1128	······std·····NaN··NaN···7.071068···7.071068
1129	······min·····NaN··NaN···0.000000···0.500000	1129	······min·····NaN··NaN···0.000000···0.500000
1130	······25%·····NaN··NaN···5.750000···6.250000	1130	······25%·····NaN··NaN···5.750000···6.250000
1131	······50%·····NaN··NaN··11.500000··12.000000	1131	······50%·····NaN··NaN··11.500000··12.000000
1132	······75%·····NaN··NaN··17.250000··17.750000	1132	······75%·····NaN··NaN··17.250000··17.750000


Offset 854, 18 lines modified		Offset 854, 18 lines modified
854	</div>	854	</div>
855	<div·class="admonition·note">	855	<div·class="admonition·note">
856	<p·class="admonition-title">Note</p>	856	<p·class="admonition-title">Note</p>
857	<p>If·you·want·to·perform·calculations·based·on·today’s·date,·use·<code·class="docutils·literal·notranslate"><span·class="pre">Timestamp.now()</span></code>·and·<code·class="docutils·literal·notranslate"><span·class="pre">pandas.tseries.offsets</span></code>.</p>	857	<p>If·you·want·to·perform·calculations·based·on·today’s·date,·use·<code·class="docutils·literal·notranslate"><span·class="pre">Timestamp.now()</span></code>·and·<code·class="docutils·literal·notranslate"><span·class="pre">pandas.tseries.offsets</span></code>.</p>
858	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[66]:·</span><span·class="kn">import</span>·<span·class="nn">pandas.tseries.offsets</span>·<span·class="k">as</span>·<span·class="nn">offsets</span>	858	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[66]:·</span><span·class="kn">import</span>·<span·class="nn">pandas.tseries.offsets</span>·<span·class="k">as</span>·<span·class="nn">offsets</span>

859	<span·class="gp">In·[67]:·</span><span·class="n">pd</span><span·class="o">.</span><span·class="n">Timestamp</span><span·class="o">.</span><span·class="n">now</span><span·class="p">()</span>	859	<span·class="gp">In·[67]:·</span><span·class="n">pd</span><span·class="o">.</span><span·class="n">Timestamp</span><span·class="o">.</span><span·class="n">now</span><span·class="p">()</span>
860	<span·class="gh">Out[67]:·</span><span·class="go">Timestamp('2024-01-07·05:34:39.~~314052~~')</span>	860	<span·class="gh">Out[67]:·</span><span·class="go">Timestamp('2025-02-09·16:03:01.286217')</span>

861	<span·class="gp">In·[68]:·</span><span·class="n">pd</span><span·class="o">.</span><span·class="n">Timestamp</span><span·class="o">.</span><span·class="n">now</span><span·class="p">()</span>·<span·class="o">+</span>·<span·class="n">offsets</span><span·class="o">.</span><span·class="n">DateOffset</span><span·class="p">(</span><span·class="n">years</span><span·class="o">=</span><span·class="mi">1</span><span·class="p">)</span>	861	<span·class="gp">In·[68]:·</span><span·class="n">pd</span><span·class="o">.</span><span·class="n">Timestamp</span><span·class="o">.</span><span·class="n">now</span><span·class="p">()</span>·<span·class="o">+</span>·<span·class="n">offsets</span><span·class="o">.</span><span·class="n">DateOffset</span><span·class="p">(</span><span·class="n">years</span><span·class="o">=</span><span·class="mi">1</span><span·class="p">)</span>
862	<span·class="gh">Out[68]:·</span><span·class="go">Timestamp('2025-01-07·05:34:39.~~316255~~')</span>	862	<span·class="gh">Out[68]:·</span><span·class="go">Timestamp('2026-02-09·16:03:01.289022')</span>
863	</pre></div>	863	</pre></div>
864	</div>	864	</div>
865	</div>	865	</div>
866	</div>	866	</div>
867	</div>	867	</div>
868	<div·class="section"·id="changes-to-index-comparisons">	868	<div·class="section"·id="changes-to-index-comparisons">
869	<h3>Changes·to·Index·comparisons<a·class="headerlink"·href="#changes-to-index-comparisons"·title="Permalink·to·this·headline">¶</a></h3>	869	<h3>Changes·to·Index·comparisons<a·class="headerlink"·href="#changes-to-index-comparisons"·title="Permalink·to·this·headline">¶</a></h3>


Offset 698, 18 lines modified		Offset 698, 18 lines modified
698	freq=None)	698	freq=None)
699	Note	699	Note
700	If·you·want·to·perform·calculations·based·on·todayâs·date,·use·Timestamp.now	700	If·you·want·to·perform·calculations·based·on·todayâs·date,·use·Timestamp.now
701	()·and·pandas.tseries.offsets.	701	()·and·pandas.tseries.offsets.
702	In·[66]:·import·pandas.tseries.offsets·as·offsets	702	In·[66]:·import·pandas.tseries.offsets·as·offsets

703	In·[67]:·pd.Timestamp.now()	703	In·[67]:·pd.Timestamp.now()
704	Out[67]:·Timestamp('2024-01-07·05:34:39.~~314052~~')	704	Out[67]:·Timestamp('2025-02-09·16:03:01.286217')

705	In·[68]:·pd.Timestamp.now()·+·offsets.DateOffset(years=1)	705	In·[68]:·pd.Timestamp.now()·+·offsets.DateOffset(years=1)
706	Out[68]:·Timestamp('2025-01-07·05:34:39.~~316255~~')	706	Out[68]:·Timestamp('2026-02-09·16:03:01.289022')
707	**·Changes·to·Index·comparisonsÂ¶·**	707	**·Changes·to·Index·comparisonsÂ¶·**
708	Operator·equal·on·Index·should·behavior·similarly·to·Series·(GH9947,·GH10637)	708	Operator·equal·on·Index·should·behavior·similarly·to·Series·(GH9947,·GH10637)
709	Starting·in·v0.17.0,·comparing·Index·objects·of·different·lengths·will·raise·a	709	Starting·in·v0.17.0,·comparing·Index·objects·of·different·lengths·will·raise·a
710	ValueError.·This·is·to·be·consistent·with·the·behavior·of·Series.	710	ValueError.·This·is·to·be·consistent·with·the·behavior·of·Series.
711	Previous·behavior:	711	Previous·behavior:
712	In·[2]:·pd.Index([1,·2,·3])·==·pd.Index([1,·4,·5])	712	In·[2]:·pd.Index([1,·2,·3])·==·pd.Index([1,·4,·5])
713	Out[2]:·array([·True,·False,·False],·dtype=bool)	713	Out[2]:·array([·True,·False,·False],·dtype=bool)


Offset 996, 15 lines modified		Offset 996, 15 lines modified
996	</div>	996	</div>
997	<p><strong>New·API</strong>:</p>	997	<p><strong>New·API</strong>:</p>
998	<p>Now,·you·can·write·<code·class="docutils·literal·notranslate"><span·class="pre">.resample(..)</span></code>·as·a·2-stage·operation·like·<code·class="docutils·literal·notranslate"><span·class="pre">.groupby(...)</span></code>,·which	998	<p>Now,·you·can·write·<code·class="docutils·literal·notranslate"><span·class="pre">.resample(..)</span></code>·as·a·2-stage·operation·like·<code·class="docutils·literal·notranslate"><span·class="pre">.groupby(...)</span></code>,·which
999	yields·a·<code·class="docutils·literal·notranslate"><span·class="pre">Resampler</span></code>.</p>	999	yields·a·<code·class="docutils·literal·notranslate"><span·class="pre">Resampler</span></code>.</p>
1000	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[82]:·</span><span·class="n">r</span>·<span·class="o">=</span>·<span·class="n">df</span><span·class="o">.</span><span·class="n">resample</span><span·class="p">(</span><span·class="s1">'2s'</span><span·class="p">)</span>	1000	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[82]:·</span><span·class="n">r</span>·<span·class="o">=</span>·<span·class="n">df</span><span·class="o">.</span><span·class="n">resample</span><span·class="p">(</span><span·class="s1">'2s'</span><span·class="p">)</span>

1001	<span·class="gp">In·[83]:·</span><span·class="n">r</span>	1001	<span·class="gp">In·[83]:·</span><span·class="n">r</span>
1002	<span·class="gh">Out[83]:·</span><span·class="go"><pandas.core.resample.DatetimeIndexResampler·object·at·0x7f~~b708~~ef60d0></span>	1002	<span·class="gh">Out[83]:·</span><span·class="go"><pandas.core.resample.DatetimeIndexResampler·object·at·0x7f4ae9e76970></span>
1003	</pre></div>	1003	</pre></div>
1004	</div>	1004	</div>
1005	<div·class="section"·id="downsampling">	1005	<div·class="section"·id="downsampling">
1006	<h4>Downsampling<a·class="headerlink"·href="#downsampling"·title="Permalink·to·this·headline">¶</a></h4>	1006	<h4>Downsampling<a·class="headerlink"·href="#downsampling"·title="Permalink·to·this·headline">¶</a></h4>
1007	<p>You·can·then·use·this·object·to·perform·operations.	1007	<p>You·can·then·use·this·object·to·perform·operations.
1008	These·are·downsampling·operations·(going·from·a·higher·frequency·to·a·lower·one).</p>	1008	These·are·downsampling·operations·(going·from·a·higher·frequency·to·a·lower·one).</p>
1009	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[84]:·</span><span·class="n">r</span><span·class="o">.</span><span·class="n">mean</span><span·class="p">()</span>	1009	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[84]:·</span><span·class="n">r</span><span·class="o">.</span><span·class="n">mean</span><span·class="p">()</span>


Offset 834, 15 lines modified		Offset 834, 15 lines modified
834	2010-01-01·09:00:08··1.020940··1.068634··1.146402··1.613897	834	2010-01-01·09:00:08··1.020940··1.068634··1.146402··1.613897
835	New·API:	835	New·API:
836	Now,·you·can·write·.resample(..)·as·a·2-stage·operation·like·.groupby(...),	836	Now,·you·can·write·.resample(..)·as·a·2-stage·operation·like·.groupby(...),
837	which·yields·a·Resampler.	837	which·yields·a·Resampler.
838	In·[82]:·r·=·df.resample('2s')	838	In·[82]:·r·=·df.resample('2s')

839	In·[83]:·r	839	In·[83]:·r
840	Out[83]:·<pandas.core.resample.DatetimeIndexResampler·object·at·0x7f~~b708~~ef60d0>	840	Out[83]:·<pandas.core.resample.DatetimeIndexResampler·object·at·0x7f4ae9e76970>
841	*·DownsamplingÂ¶·*	841	*·DownsamplingÂ¶·*
842	You·can·then·use·this·object·to·perform·operations.·These·are·downsampling	842	You·can·then·use·this·object·to·perform·operations.·These·are·downsampling
843	operations·(going·from·a·higher·frequency·to·a·lower·one).	843	operations·(going·from·a·higher·frequency·to·a·lower·one).
844	In·[84]:·r.mean()	844	In·[84]:·r.mean()
845	Out[84]:	845	Out[84]:
846	····························A·········B·········C·········D	846	····························A·········B·········C·········D
847	2010-01-01·09:00:00··0.485748··0.447351··0.357096··0.793615	847	2010-01-01·09:00:00··0.485748··0.447351··0.357096··0.793615


Offset 256, 17 lines modified		Offset 256, 17 lines modified
256	<span·class="go">2··3··6</span>	256	<span·class="go">2··3··6</span>

257	<span·class="go">[3·rows·x·2·columns]</span>	257	<span·class="go">[3·rows·x·2·columns]</span>

258	<span·class="gp">In·[13]:·</span><span·class="n">df</span><span·class="o">.</span><span·class="n">rename</span><span·class="p">(</span><span·class="nb">id</span><span·class="p">,</span>·<span·class="n">axis</span><span·class="o">=</span><span·class="s1">'index'</span><span·class="p">)</span>	258	<span·class="gp">In·[13]:·</span><span·class="n">df</span><span·class="o">.</span><span·class="n">rename</span><span·class="p">(</span><span·class="nb">id</span><span·class="p">,</span>·<span·class="n">axis</span><span·class="o">=</span><span·class="s1">'index'</span><span·class="p">)</span>
259	<span·class="gh">Out[13]:·</span><span·class="go"></span>	259	<span·class="gh">Out[13]:·</span><span·class="go"></span>
260	<span·class="go">·················A··B</span>	260	<span·class="go">·················A··B</span>
261	<span·class="go">1~~404253995~~2~~3600~~··1··4</span>	261	<span·class="go">139961021921552··1··4</span>
262	<span·class="go">1~~404253995~~2~~3632~~··2··5</span>	262	<span·class="go">139961021921584··2··5</span>
263	<span·class="go">1~~404253995~~23664··3··6</span>	263	<span·class="go">139961021921616··3··6</span>

264	<span·class="go">[3·rows·x·2·columns]</span>	264	<span·class="go">[3·rows·x·2·columns]</span>
265	</pre></div>	265	</pre></div>
266	</div>	266	</div>
267	<p>And·<code·class="docutils·literal·notranslate"><span·class="pre">reindex</span></code>:</p>	267	<p>And·<code·class="docutils·literal·notranslate"><span·class="pre">reindex</span></code>:</p>
268	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[14]:·</span><span·class="n">df</span><span·class="o">.</span><span·class="n">reindex</span><span·class="p">([</span><span·class="s1">'A'</span><span·class="p">,</span>·<span·class="s1">'B'</span><span·class="p">,</span>·<span·class="s1">'C'</span><span·class="p">],</span>·<span·class="n">axis</span><span·class="o">=</span><span·class="s1">'columns'</span><span·class="p">)</span>	268	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[14]:·</span><span·class="n">df</span><span·class="o">.</span><span·class="n">reindex</span><span·class="p">([</span><span·class="s1">'A'</span><span·class="p">,</span>·<span·class="s1">'B'</span><span·class="p">,</span>·<span·class="s1">'C'</span><span·class="p">],</span>·<span·class="n">axis</span><span·class="o">=</span><span·class="s1">'columns'</span><span·class="p">)</span>
269	<span·class="gh">Out[14]:·</span><span·class="go"></span>	269	<span·class="gh">Out[14]:·</span><span·class="go"></span>
Offset 287, 17 lines modified		Offset 287, 17 lines modified
287	<span·class="go">[3·rows·x·2·columns]</span>	287	<span·class="go">[3·rows·x·2·columns]</span>
288	</pre></div>	288	</pre></div>
289	</div>	289	</div>
290	<p>The·“index,·columns”·style·continues·to·work·as·before.</p>	290	<p>The·“index,·columns”·style·continues·to·work·as·before.</p>
291	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[16]:·</span><span·class="n">df</span><span·class="o">.</span><span·class="n">rename</span><span·class="p">(</span><span·class="n">index</span><span·class="o">=</span><span·class="nb">id</span><span·class="p">,</span>·<span·class="n">columns</span><span·class="o">=</span><span·class="nb">str</span><span·class="o">.</span><span·class="n">lower</span><span·class="p">)</span>	291	<div·class="highlight-ipython·notranslate"><div·class="highlight"><pre><span></span><span·class="gp">In·[16]:·</span><span·class="n">df</span><span·class="o">.</span><span·class="n">rename</span><span·class="p">(</span><span·class="n">index</span><span·class="o">=</span><span·class="nb">id</span><span·class="p">,</span>·<span·class="n">columns</span><span·class="o">=</span><span·class="nb">str</span><span·class="o">.</span><span·class="n">lower</span><span·class="p">)</span>
292	<span·class="gh">Out[16]:·</span><span·class="go"></span>	292	<span·class="gh">Out[16]:·</span><span·class="go"></span>
293	<span·class="go">·················a··b</span>	293	<span·class="go">·················a··b</span>
294	<span·class="go">1~~404253995~~2~~3600~~··1··4</span>	294	<span·class="go">139961021921552··1··4</span>
295	<span·class="go">1~~404253995~~2~~3632~~··2··5</span>	295	<span·class="go">139961021921584··2··5</span>
296	<span·class="go">1~~404253995~~23664··3··6</span>	296	<span·class="go">139961021921616··3··6</span>

297	<span·class="go">[3·rows·x·2·columns]</span>	297	<span·class="go">[3·rows·x·2·columns]</span>

298	<span·class="gp">In·[17]:·</span><span·class="n">df</span><span·class="o">.</span><span·class="n">reindex</span><span·class="p">(</span><span·class="n">index</span><span·class="o">=</span><span·class="p">[</span><span·class="mi">0</span><span·class="p">,</span>·<span·class="mi">1</span><span·class="p">,</span>·<span·class="mi">3</span><span·class="p">],</span>·<span·class="n">columns</span><span·class="o">=</span><span·class="p">[</span><span·class="s1">'A'</span><span·class="p">,</span>·<span·class="s1">'B'</span><span·class="p">,</span>·<span·class="s1">'C'</span><span·class="p">])</span>	298	<span·class="gp">In·[17]:·</span><span·class="n">df</span><span·class="o">.</span><span·class="n">reindex</span><span·class="p">(</span><span·class="n">index</span><span·class="o">=</span><span·class="p">[</span><span·class="mi">0</span><span·class="p">,</span>·<span·class="mi">1</span><span·class="p">,</span>·<span·class="mi">3</span><span·class="p">],</span>·<span·class="n">columns</span><span·class="o">=</span><span·class="p">[</span><span·class="s1">'A'</span><span·class="p">,</span>·<span·class="s1">'B'</span><span·class="p">,</span>·<span·class="s1">'C'</span><span·class="p">])</span>
299	<span·class="gh">Out[17]:·</span><span·class="go"></span>	299	<span·class="gh">Out[17]:·</span><span·class="go"></span>
300	<span·class="go">·····A····B···C</span>	300	<span·class="go">·····A····B···C</span>
301	<span·class="go">0··1.0··4.0·NaN</span>	301	<span·class="go">0··1.0··4.0·NaN</span>


Offset 195, 17 lines modified		Offset 195, 17 lines modified
195	2··3··6	195	2··3··6

196	[3·rows·x·2·columns]	196	[3·rows·x·2·columns]

197	In·[13]:·df.rename(id,·axis='index')	197	In·[13]:·df.rename(id,·axis='index')
198	Out[13]:	198	Out[13]:
199	·················A··B	199	·················A··B
200	140425399523600··1··4
201	140425399523632··2··5
202	1~~404253995~~2~~3664~~··3··6	200	139961021921552··1··4
		201	139961021921584··2··5
		202	139961021921616··3··6

203	[3·rows·x·2·columns]	203	[3·rows·x·2·columns]
204	And·reindex:	204	And·reindex:
205	In·[14]:·df.reindex(['A',·'B',·'C'],·axis='columns')	205	In·[14]:·df.reindex(['A',·'B',·'C'],·axis='columns')
206	Out[14]:	206	Out[14]:
207	···A··B···C	207	···A··B···C
208	0··1··4·NaN	208	0··1··4·NaN
Offset 222, 17 lines modified		Offset 222, 17 lines modified
222	3··NaN··NaN	222	3··NaN··NaN

223	[3·rows·x·2·columns]	223	[3·rows·x·2·columns]
224	The·âindex,·columnsâ·style·continues·to·work·as·before.	224	The·âindex,·columnsâ·style·continues·to·work·as·before.
225	In·[16]:·df.rename(index=id,·columns=str.lower)	225	In·[16]:·df.rename(index=id,·columns=str.lower)
226	Out[16]:	226	Out[16]:
227	·················a··b	227	·················a··b
228	140425399523600··1··4
229	140425399523632··2··5
230	1~~404253995~~2~~3664~~··3··6	228	139961021921552··1··4
		229	139961021921584··2··5
		230	139961021921616··3··6

231	[3·rows·x·2·columns]	231	[3·rows·x·2·columns]

232	In·[17]:·df.reindex(index=[0,·1,·3],·columns=['A',·'B',·'C'])	232	In·[17]:·df.reindex(index=[0,·1,·3],·columns=['A',·'B',·'C'])
233	Out[17]:	233	Out[17]:
234	·····A····B···C	234	·····A····B···C
235	0··1.0··4.0·NaN	235	0··1.0··4.0·NaN