![\"../_images/frame_plot_basic.png\"](\"../_images/frame_plot_basic.png\")
\n \n In [144]: ser = pd.Series(arr[:, 0])\n \n In [145]: %timeit ser.iloc[indexer]\n .....: %timeit ser.take(indexer)\n .....: \n-173 us +- 7.61 us per loop (mean +- std. dev. of 7 runs, 10000 loops each)\n-159 us +- 6.36 us per loop (mean +- std. dev. of 7 runs, 10000 loops each)\n+107 us +- 78.5 ns per loop (mean +- std. dev. of 7 runs, 10000 loops each)\n+98 us +- 85 ns per loop (mean +- std. dev. of 7 runs, 10000 loops each)\n We have discussed MultiIndex in the previous sections pretty extensively.\n Documentation about DatetimeIndex and PeriodIndex are shown here,\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -1113,23 +1113,23 @@\n In [141]: indexer = np.arange(10000)\n \n In [142]: random.shuffle(indexer)\n \n In [143]: %timeit arr[indexer]\n .....: %timeit arr.take(indexer, axis=0)\n .....:\n-370 us +- 5.65 us per loop (mean +- std. dev. of 7 runs, 1000 loops each)\n-165 us +- 2.77 us per loop (mean +- std. dev. of 7 runs, 10000 loops each)\n+302 us +- 112 ns per loop (mean +- std. dev. of 7 runs, 1000 loops each)\n+131 us +- 75.7 ns per loop (mean +- std. dev. of 7 runs, 10000 loops each)\n In [144]: ser = pd.Series(arr[:, 0])\n \n In [145]: %timeit ser.iloc[indexer]\n .....: %timeit ser.take(indexer)\n .....:\n-173 us +- 7.61 us per loop (mean +- std. dev. of 7 runs, 10000 loops each)\n-159 us +- 6.36 us per loop (mean +- std. dev. of 7 runs, 10000 loops each)\n+107 us +- 78.5 ns per loop (mean +- std. dev. of 7 runs, 10000 loops each)\n+98 us +- 85 ns per loop (mean +- std. dev. of 7 runs, 10000 loops each)\n ***** Index types\u00c2\u00b6 *****\n We have discussed MultiIndex in the previous sections pretty extensively.\n Documentation about DatetimeIndex and PeriodIndex are shown here, and\n documentation about TimedeltaIndex is found here.\n In the following sub-sections we will highlight some other index types.\n **** CategoricalIndex\u00c2\u00b6 ****\n CategoricalIndex is a type of index that is useful for supporting indexing with\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/user_guide/basics.html", "source2": "./usr/share/doc/python-pandas-doc/html/user_guide/basics.html", "comments": ["Ordering differences only"], "unified_diff": "@@ -3550,21 +3550,21 @@\n dtype: object\n
The number of columns of each type in a DataFrame can be found by calling\n DataFrame.dtypes.value_counts().
In [352]: dft.dtypes.value_counts()\n Out[352]: \n-bool 1\n-float64 1\n-int64 1\n datetime64[ns] 1\n object 1\n-float32 1\n+bool 1\n+int64 1\n+float64 1\n int8 1\n+float32 1\n dtype: int64\n Numeric dtypes will propagate and can coexist in DataFrames.\n If a dtype is passed (either directly via the dtype keyword, a passed ndarray,\n or a passed Series), then it will be preserved in DataFrame operations. Furthermore,\n different numeric dtypes will NOT be combined. The following example will give you a taste.
But clearly this isn\u2019t fast enough for us. Let\u2019s take a look and see where the\n time is spent during this operation (limited to the most time consuming\n four calls) using the prun ipython magic function:
\nIn [5]: %prun -l 4 df.apply(lambda x: integrate_f(x['a'], x['b'], x['N']), axis=1) # noqa E999\n- 622830 function calls (622809 primitive calls) in 1.375 seconds\n+ 622830 function calls (622809 primitive calls) in 0.257 seconds\n \n Ordered by: internal time\n List reduced from 214 to 4 due to restriction <4>\n \n ncalls tottime percall cumtime percall filename:lineno(function)\n- 1000 0.670 0.001 1.185 0.001 <ipython-input-4-c2a74e076cf0>:1(integrate_f)\n- 552423 0.515 0.000 0.515 0.000 <ipython-input-3-c138bdd570e3>:1(f)\n- 3000 0.024 0.000 0.143 0.000 series.py:868(__getitem__)\n- 3000 0.017 0.000 0.101 0.000 series.py:973(_get_value)\n+ 1000 0.142 0.000 0.219 0.000 <ipython-input-4-c2a74e076cf0>:1(integrate_f)\n+ 552423 0.077 0.000 0.077 0.000 <ipython-input-3-c138bdd570e3>:1(f)\n+ 3000 0.005 0.000 0.027 0.000 series.py:868(__getitem__)\n+ 3000 0.004 0.000 0.020 0.000 series.py:973(_get_value)\n By far the majority of time is spend inside either integrate_f or f,\n hence we\u2019ll concentrate our efforts cythonizing these two functions.
Now, we\u2019re talking! It\u2019s now over ten times faster than the original python\n implementation, and we haven\u2019t really modified the code. Let\u2019s have another\n look at what\u2019s eating up time:
\nIn [9]: %prun -l 4 df.apply(lambda x: integrate_f_typed(x['a'], x['b'], x['N']), axis=1)\n- 70396 function calls (70375 primitive calls) in 0.175 seconds\n+ 70396 function calls (70375 primitive calls) in 0.039 seconds\n \n Ordered by: internal time\n List reduced from 208 to 4 due to restriction <4>\n \n ncalls tottime percall cumtime percall filename:lineno(function)\n- 3000 0.022 0.000 0.130 0.000 series.py:868(__getitem__)\n- 3000 0.015 0.000 0.092 0.000 series.py:973(_get_value)\n- 3000 0.011 0.000 0.040 0.000 base.py:2854(get_loc)\n- 3000 0.011 0.000 0.037 0.000 base.py:4626(_get_values_for_loc)\n+ 3000 0.005 0.000 0.027 0.000 series.py:868(__getitem__)\n+ 3000 0.003 0.000 0.019 0.000 series.py:973(_get_value)\n+ 3000 0.002 0.000 0.008 0.000 base.py:2854(get_loc)\n+ 3000 0.002 0.000 0.007 0.000 base.py:4626(_get_values_for_loc)\n It\u2019s calling series\u2026 a lot! It\u2019s creating a Series from each row, and get-ting from both\n the index and the series (three times for each row). Function calls are expensive\n@@ -277,24 +277,24 @@\n
We\u2019ve gotten another big improvement. Let\u2019s check again where the time is spent:
\nIn [11]: %%prun -l 4 apply_integrate_f(df['a'].to_numpy(),\n ....: df['b'].to_numpy(),\n ....: df['N'].to_numpy())\n ....: \n- 218 function calls in 0.004 seconds\n+ 218 function calls in 0.002 seconds\n \n Ordered by: internal time\n List reduced from 59 to 4 due to restriction <4>\n \n ncalls tottime percall cumtime percall filename:lineno(function)\n- 1 0.003 0.003 0.003 0.003 {built-in method _cython_magic_7dc7064016b351e6f537d7542a365f15.apply_integrate_f}\n+ 1 0.002 0.002 0.002 0.002 {built-in method _cython_magic_7dc7064016b351e6f537d7542a365f15.apply_integrate_f}\n+ 3 0.000 0.000 0.000 0.000 frame.py:2869(__getitem__)\n 3 0.000 0.000 0.000 0.000 managers.py:993(iget)\n- 3 0.000 0.000 0.001 0.000 frame.py:2869(__getitem__)\n- 1 0.000 0.000 0.004 0.004 {built-in method builtins.exec}\n+ 1 0.000 0.000 0.002 0.002 {built-in method builtins.exec}\n As one might expect, the majority of the time is now spent in apply_integrate_f,\n so if we wanted to make anymore efficiencies we must continue to concentrate our\n efforts here.
Now let\u2019s compare adding them together using plain ol\u2019 Python versus\n eval():
In [15]: %timeit df1 + df2 + df3 + df4\n-43.5 ms +- 1.3 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n+11.9 ms +- 114 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n In [16]: %timeit pd.eval('df1 + df2 + df3 + df4')\n-26.1 ms +- 1.26 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n+7.94 ms +- 34.5 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n Now let\u2019s do the same thing but with comparisons:
\nIn [17]: %timeit (df1 > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)\n-54.3 ms +- 1.68 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n+25.7 ms +- 65 us per loop (mean +- std. dev. of 7 runs, 10 loops each)\n In [18]: %timeit pd.eval('(df1 > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)')\n-15.8 ms +- 166 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n+8.97 ms +- 38.3 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n eval() also works with unaligned pandas objects:
In [19]: s = pd.Series(np.random.randn(50))\n \n In [20]: %timeit df1 + df2 + df3 + df4 + s\n-72.9 ms +- 1.7 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n+42.9 ms +- 42.4 us per loop (mean +- std. dev. of 7 runs, 10 loops each)\n In [21]: %timeit pd.eval('df1 + df2 + df3 + df4 + s')\n-27.9 ms +- 795 us per loop (mean +- std. dev. of 7 runs, 10 loops each)\n+28.6 ms +- 62.4 us per loop (mean +- std. dev. of 7 runs, 10 loops each)\n Note
\nOperations such as
\n\n\n\n\n1 and 2 # would parse to 1 & 2, but should evaluate to 2\n@@ -834,19 +834,19 @@\n other evaluation engines against it. You will achieve no performance\n benefits usingeval()withengine='python'and in fact may\n incur a performance hit.\nYou can see this by using
\npandas.eval()with the'python'engine. It\n is a bit slower (not by much) than evaluating the same expression in Python\n\nIn [62]: %timeit df1 + df2 + df3 + df4\n-46.7 ms +- 2.02 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n+12 ms +- 77.8 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n\n\nIn [63]: %timeit pd.eval('df1 + df2 + df3 + df4', engine='python')\n-44.9 ms +- 1.95 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n+12.8 ms +- 11 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n\n\n\n
pandas.eval()performance\u00b6
eval()is intended to speed up certain kinds of operations. In\n particular, those operations involving complex expressions with large\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -78,26 +78,26 @@\n In [7]: %timeit df.apply(lambda x: integrate_f(x['a'], x['b'], x['N']), axis=1)\n 10 loops, best of 3: 174 ms per loop\n But clearly this isn\u00e2\u0080\u0099t fast enough for us. Let\u00e2\u0080\u0099s take a look and see where\n the time is spent during this operation (limited to the most time consuming\n four calls) using the prun_ipython_magic_function:\n In [5]: %prun -l 4 df.apply(lambda x: integrate_f(x['a'], x['b'], x['N']),\n axis=1) # noqa E999\n- 622830 function calls (622809 primitive calls) in 1.375 seconds\n+ 622830 function calls (622809 primitive calls) in 0.257 seconds\n \n Ordered by: internal time\n List reduced from 214 to 4 due to restriction <4>\n \n ncalls tottime percall cumtime percall filename:lineno(function)\n- 1000 0.670 0.001 1.185 0.001:1\n+ 1000 0.142 0.000 0.219 0.000 :1\n (integrate_f)\n- 552423 0.515 0.000 0.515 0.000 :1\n+ 552423 0.077 0.000 0.077 0.000 :1\n (f)\n- 3000 0.024 0.000 0.143 0.000 series.py:868(__getitem__)\n- 3000 0.017 0.000 0.101 0.000 series.py:973(_get_value)\n+ 3000 0.005 0.000 0.027 0.000 series.py:868(__getitem__)\n+ 3000 0.004 0.000 0.020 0.000 series.py:973(_get_value)\n By far the majority of time is spend inside either integrate_f or f, hence\n we\u00e2\u0080\u0099ll concentrate our efforts cythonizing these two functions.\n **** Plain Cython\u00c2\u00b6 ****\n First we\u00e2\u0080\u0099re going to need to import the Cython magic function to ipython:\n In [6]: %load_ext Cython\n Now, let\u00e2\u0080\u0099s simply copy our functions over to Cython as is (the suffix is here\n to distinguish between function versions):\n@@ -136,24 +136,24 @@\n axis=1)\n 10 loops, best of 3: 20.3 ms per loop\n Now, we\u00e2\u0080\u0099re talking! It\u00e2\u0080\u0099s now over ten times faster than the original python\n implementation, and we haven\u00e2\u0080\u0099t really modified the code. Let\u00e2\u0080\u0099s have another\n look at what\u00e2\u0080\u0099s eating up time:\n In [9]: %prun -l 4 df.apply(lambda x: integrate_f_typed(x['a'], x['b'], x\n ['N']), axis=1)\n- 70396 function calls (70375 primitive calls) in 0.175 seconds\n+ 70396 function calls (70375 primitive calls) in 0.039 seconds\n \n Ordered by: internal time\n List reduced from 208 to 4 due to restriction <4>\n \n ncalls tottime percall cumtime percall filename:lineno(function)\n- 3000 0.022 0.000 0.130 0.000 series.py:868(__getitem__)\n- 3000 0.015 0.000 0.092 0.000 series.py:973(_get_value)\n- 3000 0.011 0.000 0.040 0.000 base.py:2854(get_loc)\n- 3000 0.011 0.000 0.037 0.000 base.py:4626(_get_values_for_loc)\n+ 3000 0.005 0.000 0.027 0.000 series.py:868(__getitem__)\n+ 3000 0.003 0.000 0.019 0.000 series.py:973(_get_value)\n+ 3000 0.002 0.000 0.008 0.000 base.py:2854(get_loc)\n+ 3000 0.002 0.000 0.007 0.000 base.py:4626(_get_values_for_loc)\n **** Using ndarray\u00c2\u00b6 ****\n It\u00e2\u0080\u0099s calling series\u00e2\u0080\u00a6 a lot! It\u00e2\u0080\u0099s creating a Series from each row, and\n get-ting from both the index and the series (three times for each row).\n Function calls are expensive in Python, so maybe we could minimize these by\n cythonizing the apply part.\n Note\n We are now passing ndarrays into the Cython function, fortunately Cython plays\n@@ -205,25 +205,25 @@\n 1000 loops, best of 3: 1.25 ms per loop\n We\u00e2\u0080\u0099ve gotten another big improvement. Let\u00e2\u0080\u0099s check again where the time is\n spent:\n In [11]: %%prun -l 4 apply_integrate_f(df['a'].to_numpy(),\n ....: df['b'].to_numpy(),\n ....: df['N'].to_numpy())\n ....:\n- 218 function calls in 0.004 seconds\n+ 218 function calls in 0.002 seconds\n \n Ordered by: internal time\n List reduced from 59 to 4 due to restriction <4>\n \n ncalls tottime percall cumtime percall filename:lineno(function)\n- 1 0.003 0.003 0.003 0.003 {built-in method\n+ 1 0.002 0.002 0.002 0.002 {built-in method\n _cython_magic_7dc7064016b351e6f537d7542a365f15.apply_integrate_f}\n+ 3 0.000 0.000 0.000 0.000 frame.py:2869(__getitem__)\n 3 0.000 0.000 0.000 0.000 managers.py:993(iget)\n- 3 0.000 0.000 0.001 0.000 frame.py:2869(__getitem__)\n- 1 0.000 0.000 0.004 0.004 {built-in method builtins.exec}\n+ 1 0.000 0.000 0.002 0.002 {built-in method builtins.exec}\n As one might expect, the majority of the time is now spent in\n apply_integrate_f, so if we wanted to make anymore efficiencies we must\n continue to concentrate our efforts here.\n **** More advanced techniques\u00c2\u00b6 ****\n There is still hope for improvement. Here\u00e2\u0080\u0099s an example of using some more\n advanced Cython techniques:\n In [12]: %%cython\n@@ -420,29 +420,29 @@\n In [13]: nrows, ncols = 20000, 100\n \n In [14]: df1, df2, df3, df4 = [pd.DataFrame(np.random.randn(nrows, ncols)) for\n _ in range(4)]\n Now let\u00e2\u0080\u0099s compare adding them together using plain ol\u00e2\u0080\u0099 Python versus eval\n ():\n In [15]: %timeit df1 + df2 + df3 + df4\n-43.5 ms +- 1.3 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n+11.9 ms +- 114 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n In [16]: %timeit pd.eval('df1 + df2 + df3 + df4')\n-26.1 ms +- 1.26 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n+7.94 ms +- 34.5 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n Now let\u00e2\u0080\u0099s do the same thing but with comparisons:\n In [17]: %timeit (df1 > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)\n-54.3 ms +- 1.68 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n+25.7 ms +- 65 us per loop (mean +- std. dev. of 7 runs, 10 loops each)\n In [18]: %timeit pd.eval('(df1 > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)')\n-15.8 ms +- 166 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n+8.97 ms +- 38.3 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n eval() also works with unaligned pandas objects:\n In [19]: s = pd.Series(np.random.randn(50))\n \n In [20]: %timeit df1 + df2 + df3 + df4 + s\n-72.9 ms +- 1.7 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n+42.9 ms +- 42.4 us per loop (mean +- std. dev. of 7 runs, 10 loops each)\n In [21]: %timeit pd.eval('df1 + df2 + df3 + df4 + s')\n-27.9 ms +- 795 us per loop (mean +- std. dev. of 7 runs, 10 loops each)\n+28.6 ms +- 62.4 us per loop (mean +- std. dev. of 7 runs, 10 loops each)\n Note\n Operations such as\n 1 and 2 # would parse to 1 & 2, but should evaluate to 2\n 3 or 4 # would parse to 3 | 4, but should evaluate to 3\n ~1 # this is okay, but slower when using eval\n should be performed in Python. An exception will be raised if you try to\n perform any boolean/bitwise operations with scalar operands that are not of\n@@ -667,17 +667,17 @@\n Note\n Using the 'python' engine is generally not useful, except for testing other\n evaluation engines against it. You will achieve no performance benefits using\n eval() with engine='python' and in fact may incur a performance hit.\n You can see this by using pandas.eval() with the 'python' engine. It is a bit\n slower (not by much) than evaluating the same expression in Python\n In [62]: %timeit df1 + df2 + df3 + df4\n-46.7 ms +- 2.02 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n+12 ms +- 77.8 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n In [63]: %timeit pd.eval('df1 + df2 + df3 + df4', engine='python')\n-44.9 ms +- 1.95 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n+12.8 ms +- 11 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n **** pandas.eval() performance\u00c2\u00b6 ****\n eval() is intended to speed up certain kinds of operations. In particular,\n those operations involving complex expressions with large DataFrame/Series\n objects should see a significant performance benefit. Here is a plot showing\n the running time of pandas.eval() as function of the size of the frame involved\n in the computation. The two lines are two different engines.\n [../_images/eval-perf.png]\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/user_guide/groupby.html", "source2": "./usr/share/doc/python-pandas-doc/html/user_guide/groupby.html", "unified_diff": "@@ -559,15 +559,15 @@\n In [59]: grouped_C = grouped['C']\n \n In [60]: grouped_D = grouped['D']\n This is mainly syntactic sugar for the alternative and much more verbose:
\n\n\nIn [61]: df['C'].groupby(df['A'])\n-Out[61]: <pandas.core.groupby.generic.SeriesGroupBy object at 0x9d0c82e0>\n+Out[61]: <pandas.core.groupby.generic.SeriesGroupBy object at 0xd8eb3298>\nAdditionally this method avoids recomputing the internal grouping information\n derived from the passed key.
\n
query() Python versus pandas Syntax Comparison\u00b6Full numpy-like syntax:
\nIn [232]: df = pd.DataFrame(np.random.randint(n, size=(n, 3)), columns=list('abc'))\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -1773,15 +1773,15 @@\n 9 0.732206 0.419540 0.604675\n 10 0.604466 0.848974 0.896165\n 11 0.589168 0.920046 0.732716\n \n In [230]: expr = '0.0 <= a <= c <= 0.5'\n \n In [231]: map(lambda frame: frame.query(expr), [df, df2])\n-Out[231]: By specifying a chunksize to read_csv, the return\n value will be an iterable object of type TextFileReader:
In [190]: reader = pd.read_csv('tmp.sv', sep='|', chunksize=4)\n \n In [191]: reader\n-Out[191]: <pandas.io.parsers.TextFileReader at 0x9dd3d3d0>\n+Out[191]: <pandas.io.parsers.TextFileReader at 0xdcaadfd0>\n \n In [192]: for chunk in reader:\n .....: print(chunk)\n .....: \n Unnamed: 0 0 1 2 3\n 0 0 0.469112 -0.282863 -1.509059 -1.135632\n 1 1 1.212112 -0.173215 0.119209 -1.044236\n@@ -2679,31 +2679,31 @@\n \n In [249]: dffloats = pd.DataFrame(randfloats, columns=list('ABCDEFGHIJ'))\n \n In [250]: jsonfloats = dffloats.to_json()\n In [251]: %timeit pd.read_json(jsonfloats)\n-19.9 ms +- 439 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n+12.7 ms +- 22.2 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n In [252]: %timeit pd.read_json(jsonfloats, numpy=True)\n-12.7 ms +- 279 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n+8.47 ms +- 39.2 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n The speedup is less noticeable for smaller datasets:
\nIn [253]: jsonfloats = dffloats.head(100).to_json()\n In [254]: %timeit pd.read_json(jsonfloats)\n-9.95 ms +- 193 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n+6.81 ms +- 10.8 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n In [255]: %timeit pd.read_json(jsonfloats, numpy=True)\n-8.29 ms +- 178 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n+5.83 ms +- 5.65 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n Warning
\nDirect NumPy decoding makes a number of assumptions and may fail or produce\n unexpected output if these assumptions are not satisfied:
\n\n@@ -2800,15 +2800,15 @@\n In [265]: df.to_json(orient='records', lines=True)\n Out[265]: '{"a":1,"b":2}\\n{"a":3,"b":4}'\n \n # reader is an iterator that returns `chunksize` lines each iteration\n In [266]: reader = pd.read_json(StringIO(jsonl), lines=True, chunksize=1)\n \n In [267]: reader\n-Out[267]: <pandas.io.json._json.JsonReader at 0x9e225aa8>\n+Out[267]: <pandas.io.json._json.JsonReader at 0xdbdf8028>\n \n In [268]: for chunk in reader:\n .....: print(chunk)\n .....: \n Empty DataFrame\n Columns: []\n Index: []\n@@ -4869,18 +4869,18 @@\n 5 0.852727 0.463819 0.146262 string 1 True 2001-01-02\n 6 -1.177365 0.793644 -0.131959 string 1 True 2001-01-02\n 7 1.236988 0.221252 0.089012 string 1 True 2001-01-02\n \n In [393]: df_mixed1.dtypes.value_counts()\n Out[393]: \n float64 2\n-int64 1\n-bool 1\n-object 1\n datetime64[ns] 1\n+object 1\n+bool 1\n+int64 1\n float32 1\n dtype: int64\n \n # we have provided a minimum string column size\n In [394]: store.root.df_mixed.table\n Out[394]: \n /df_mixed/table (Table(8,)) ''\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -1717,15 +1717,15 @@\n 8 8 1.075770 -0.109050 1.643563 -1.469388\n 9 9 0.357021 -0.674600 -1.776904 -0.968914\n By specifying a chunksize to read_csv, the return value will be an iterable\n object of type TextFileReader:\n In [190]: reader = pd.read_csv('tmp.sv', sep='|', chunksize=4)\n \n In [191]: reader\n-Out[191]:\n+Out[191]: \n \n In [192]: for chunk in reader:\n .....: print(chunk)\n .....:\n Unnamed: 0 0 1 2 3\n 0 0 0.469112 -0.282863 -1.509059 -1.135632\n 1 1 1.212112 -0.173215 0.119209 -1.044236\n@@ -2220,23 +2220,23 @@\n \n In [248]: randfloats.shape = (1000, 10)\n \n In [249]: dffloats = pd.DataFrame(randfloats, columns=list('ABCDEFGHIJ'))\n \n In [250]: jsonfloats = dffloats.to_json()\n In [251]: %timeit pd.read_json(jsonfloats)\n-19.9 ms +- 439 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n+12.7 ms +- 22.2 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n In [252]: %timeit pd.read_json(jsonfloats, numpy=True)\n-12.7 ms +- 279 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n+8.47 ms +- 39.2 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n The speedup is less noticeable for smaller datasets:\n In [253]: jsonfloats = dffloats.head(100).to_json()\n In [254]: %timeit pd.read_json(jsonfloats)\n-9.95 ms +- 193 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n+6.81 ms +- 10.8 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n In [255]: %timeit pd.read_json(jsonfloats, numpy=True)\n-8.29 ms +- 178 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n+5.83 ms +- 5.65 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n Warning\n Direct NumPy decoding makes a number of assumptions and may fail or produce\n unexpected output if these assumptions are not satisfied:\n * data is numeric.\n * data is uniform. The dtype is sniffed from the first value\n decoded. A ValueError may be raised, or incorrect output may be\n produced if this condition is not satisfied.\n@@ -2319,15 +2319,15 @@\n In [265]: df.to_json(orient='records', lines=True)\n Out[265]: '{\"a\":1,\"b\":2}\\n{\"a\":3,\"b\":4}'\n \n # reader is an iterator that returns `chunksize` lines each iteration\n In [266]: reader = pd.read_json(StringIO(jsonl), lines=True, chunksize=1)\n \n In [267]: reader\n-Out[267]: \n+Out[267]: \n \n In [268]: for chunk in reader:\n .....: print(chunk)\n .....:\n Empty DataFrame\n Columns: []\n Index: []\n@@ -3948,18 +3948,18 @@\n 5 0.852727 0.463819 0.146262 string 1 True 2001-01-02\n 6 -1.177365 0.793644 -0.131959 string 1 True 2001-01-02\n 7 1.236988 0.221252 0.089012 string 1 True 2001-01-02\n \n In [393]: df_mixed1.dtypes.value_counts()\n Out[393]:\n float64 2\n-int64 1\n-bool 1\n-object 1\n datetime64[ns] 1\n+object 1\n+bool 1\n+int64 1\n float32 1\n dtype: int64\n \n # we have provided a minimum string column size\n In [394]: store.root.df_mixed.table\n Out[394]:\n /df_mixed/table (Table(8,)) ''\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/user_guide/missing_data.html", "source2": "./usr/share/doc/python-pandas-doc/html/user_guide/missing_data.html", "comments": ["Ordering differences only"], "unified_diff": "@@ -232,17 +232,17 @@\n e 0.119209 -1.044236 -0.861849 bar True 2012-01-01\n f -2.104569 -0.494929 1.071804 bar False 2012-01-01\n h NaN -0.706771 -1.039575 bar True NaT\n \n In [20]: df2.dtypes.value_counts()\n Out[20]: \n float64 3\n-bool 1\n-object 1\n datetime64[ns] 1\n+object 1\n+bool 1\n dtype: int64\n
Some readers, like pandas.read_csv(), offer parameters to control the\n chunksize when reading a single file.
Manually chunking is an OK option for workflows that don\u2019t\n require too sophisticated of operations. Some operations, like groupby, are\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -279,16 +279,16 @@\n ....: files = pathlib.Path(\"data/timeseries/\").glob(\"ts*.parquet\")\n ....: counts = pd.Series(dtype=int)\n ....: for path in files:\n ....: df = pd.read_parquet(path)\n ....: counts = counts.add(df['name'].value_counts(), fill_value=0)\n ....: counts.astype(int)\n ....:\n-CPU times: user 1.13 ms, sys: 0 ns, total: 1.13 ms\n-Wall time: 1.13 ms\n+CPU times: user 776 us, sys: 0 ns, total: 776 us\n+Wall time: 782 us\n Out[19]: Series([], dtype: int32)\n Some readers, like pandas.read_csv(), offer parameters to control the chunksize\n when reading a single file.\n Manually chunking is an OK option for workflows that don\u00e2\u0080\u0099t require too\n sophisticated of operations. Some operations, like groupby, are much harder to\n do chunkwise. In these cases, you may be better switching to a different\n library that implements these out-of-core algorithms for you.\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/user_guide/style.html", "source2": "./usr/share/doc/python-pandas-doc/html/user_guide/style.html", "unified_diff": "@@ -341,94 +341,94 @@\n \n
[3]:\n | A | B | C | D | E |
|---|
| A | B | C | D | E | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n-1.000000 | \n-1.329212 | \n-nan | \n--0.316280 | \n--0.990810 | \n-||||||
| 1 | \n-2.000000 | \n--1.070816 | \n--1.438713 | \n-0.564417 | \n-0.295722 | \n-||||||
| 2 | \n-3.000000 | \n--1.626404 | \n-0.219565 | \n-0.678805 | \n-1.889273 | \n-||||||
| 3 | \n-4.000000 | \n-0.961538 | \n-0.104011 | \n-nan | \n-0.850229 | \n-||||||
| 4 | \n-5.000000 | \n-1.453425 | \n-1.057737 | \n-0.165562 | \n-0.515018 | \n-||||||
| 5 | \n-6.000000 | \n--1.336936 | \n-0.562861 | \n-1.392855 | \n--0.063328 | \n-||||||
| 6 | \n-7.000000 | \n-0.121668 | \n-1.207603 | \n--0.002040 | \n-1.627796 | \n-||||||
| 7 | \n-8.000000 | \n-0.354493 | \n-1.037528 | \n--0.385684 | \n-0.519818 | \n-||||||
| 8 | \n-9.000000 | \n-1.686583 | \n--1.325963 | \n-1.428984 | \n--2.089354 | \n-||||||
| 9 | \n-10.000000 | \n--0.129820 | \n-0.631523 | \n--0.586538 | \n-0.290720 | \n+0 | \n+1.000000 | \n+1.329212 | \n+nan | \n+-0.316280 | \n+-0.990810 | \n+
| 1 | \n+2.000000 | \n+-1.070816 | \n+-1.438713 | \n+0.564417 | \n+0.295722 | \n+||||||
| 2 | \n+3.000000 | \n+-1.626404 | \n+0.219565 | \n+0.678805 | \n+1.889273 | \n+||||||
| 3 | \n+4.000000 | \n+0.961538 | \n+0.104011 | \n+nan | \n+0.850229 | \n+||||||
| 4 | \n+5.000000 | \n+1.453425 | \n+1.057737 | \n+0.165562 | \n+0.515018 | \n+||||||
| 5 | \n+6.000000 | \n+-1.336936 | \n+0.562861 | \n+1.392855 | \n+-0.063328 | \n+||||||
| 6 | \n+7.000000 | \n+0.121668 | \n+1.207603 | \n+-0.002040 | \n+1.627796 | \n+||||||
| 7 | \n+8.000000 | \n+0.354493 | \n+1.037528 | \n+-0.385684 | \n+0.519818 | \n+||||||
| 8 | \n+9.000000 | \n+1.686583 | \n+-1.325963 | \n+1.428984 | \n+-2.089354 | \n+||||||
| 9 | \n+10.000000 | \n+-0.129820 | \n+0.631523 | \n+-0.586538 | \n+0.290720 | \n
Note: The DataFrame.style attribute is a property that returns a Styler object. Styler has a _repr_html_ method defined on it so they are rendered automatically. If you want the actual HTML back for further processing or for writing to file call the .render() method which returns a string.
The above output looks very similar to the standard DataFrame HTML representation. But we\u2019ve done some work behind the scenes to attach CSS classes to each cell. We can view these by calling the .render method.
[4]:\n@@ -442,23 +442,23 @@\n \n [4]:\n
\n \n \n \n ['<style type="text/css" >',\n- '#T_1d4562b2_b114_11ee_8b43_59a83b27c78brow0_col2,#T_1d4562b2_b114_11ee_8b43_59a83b27c78brow3_col3{',\n+ '#T_4056c92e_ea13_11ef_835c_39a090989f27row0_col2,#T_4056c92e_ea13_11ef_835c_39a090989f27row3_col3{',\n ' background-color: red;',\n- ' }</style><table id="T_1d4562b2_b114_11ee_8b43_59a83b27c78b" ><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</th> </tr></thead><tbody>',\n+ ' }</style><table id="T_4056c92e_ea13_11ef_835c_39a090989f27" ><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</th> </tr></thead><tbody>',\n ' <tr>',\n- ' <th id="T_1d4562b2_b114_11ee_8b43_59a83b27c78blevel0_row0" class="row_heading level0 row0" >0</th>',\n- ' <td id="T_1d4562b2_b114_11ee_8b43_59a83b27c78brow0_col0" class="data row0 col0" >1.000000</td>',\n- ' <td id="T_1d4562b2_b114_11ee_8b43_59a83b27c78brow0_col1" class="data row0 col1" >1.329212</td>',\n- ' <td id="T_1d4562b2_b114_11ee_8b43_59a83b27c78brow0_col2" class="data row0 col2" >nan</td>',\n- ' <td id="T_1d4562b2_b114_11ee_8b43_59a83b27c78brow0_col3" class="data row0 col3" >-0.316280</td>']\n+ ' <th id="T_4056c92e_ea13_11ef_835c_39a090989f27level0_row0" class="row_heading level0 row0" >0</th>',\n+ ' <td id="T_4056c92e_ea13_11ef_835c_39a090989f27row0_col0" class="data row0 col0" >1.000000</td>',\n+ ' <td id="T_4056c92e_ea13_11ef_835c_39a090989f27row0_col1" class="data row0 col1" >1.329212</td>',\n+ ' <td id="T_4056c92e_ea13_11ef_835c_39a090989f27row0_col2" class="data row0 col2" >nan</td>',\n+ ' <td id="T_4056c92e_ea13_11ef_835c_39a090989f27row0_col3" class="data row0 col3" >-0.316280</td>']\n \n \n The row0_col2 is the identifier for that particular cell. We\u2019ve also prepended each row/column identifier with a UUID unique to each DataFrame so that the style from one doesn\u2019t collide with the styling from another within the same notebook or page (you can set the uuid if you\u2019d like to tie together the styling of two DataFrames).
\n When writing style functions, you take care of producing the CSS attribute / value pairs you want. Pandas matches those up with the CSS classes that identify each cell.
\n Let\u2019s write a simple style function that will color negative numbers red and positive numbers black.
\n \n [5]:\n@@ -489,98 +489,98 @@\n
\n \n [6]:\n
\n \n \n A B C D E \n+ } A B C D E \n \n- 0 \n- 1.000000 \n- 1.329212 \n- nan \n- -0.316280 \n- -0.990810 \n- \n- \n- 1 \n- 2.000000 \n- -1.070816 \n- -1.438713 \n- 0.564417 \n- 0.295722 \n- \n- \n- 2 \n- 3.000000 \n- -1.626404 \n- 0.219565 \n- 0.678805 \n- 1.889273 \n- \n- \n- 3 \n- 4.000000 \n- 0.961538 \n- 0.104011 \n- nan \n- 0.850229 \n- \n- \n- 4 \n- 5.000000 \n- 1.453425 \n- 1.057737 \n- 0.165562 \n- 0.515018 \n- \n- \n- 5 \n- 6.000000 \n- -1.336936 \n- 0.562861 \n- 1.392855 \n- -0.063328 \n- \n- \n- 6 \n- 7.000000 \n- 0.121668 \n- 1.207603 \n- -0.002040 \n- 1.627796 \n- \n- \n- 7 \n- 8.000000 \n- 0.354493 \n- 1.037528 \n- -0.385684 \n- 0.519818 \n- \n- \n- 8 \n- 9.000000 \n- 1.686583 \n- -1.325963 \n- 1.428984 \n- -2.089354 \n- \n- \n- 9 \n- 10.000000 \n- -0.129820 \n- 0.631523 \n- -0.586538 \n- 0.290720 \n+ 0 \n+ 1.000000 \n+ 1.329212 \n+ nan \n+ -0.316280 \n+ -0.990810 \n+ \n+ \n+ 1 \n+ 2.000000 \n+ -1.070816 \n+ -1.438713 \n+ 0.564417 \n+ 0.295722 \n+ \n+ \n+ 2 \n+ 3.000000 \n+ -1.626404 \n+ 0.219565 \n+ 0.678805 \n+ 1.889273 \n+ \n+ \n+ 3 \n+ 4.000000 \n+ 0.961538 \n+ 0.104011 \n+ nan \n+ 0.850229 \n+ \n+ \n+ 4 \n+ 5.000000 \n+ 1.453425 \n+ 1.057737 \n+ 0.165562 \n+ 0.515018 \n+ \n+ \n+ 5 \n+ 6.000000 \n+ -1.336936 \n+ 0.562861 \n+ 1.392855 \n+ -0.063328 \n+ \n+ \n+ 6 \n+ 7.000000 \n+ 0.121668 \n+ 1.207603 \n+ -0.002040 \n+ 1.627796 \n+ \n+ \n+ 7 \n+ 8.000000 \n+ 0.354493 \n+ 1.037528 \n+ -0.385684 \n+ 0.519818 \n+ \n+ \n+ 8 \n+ 9.000000 \n+ 1.686583 \n+ -1.325963 \n+ 1.428984 \n+ -2.089354 \n+ \n+ \n+ 9 \n+ 10.000000 \n+ -0.129820 \n+ 0.631523 \n+ -0.586538 \n+ 0.290720 \n \n
\n \n Notice the similarity with the standard df.applymap, which operates on DataFrames elementwise. We want you to be able to reuse your existing knowledge of how to interact with DataFrames.
\n Notice also that our function returned a string containing the CSS attribute and value, separated by a colon just like in a <style> tag. This will be a common theme.
\n Finally, the input shapes matched. Styler.applymap calls the function on each scalar input, and the function returns a scalar output.
\n Now suppose you wanted to highlight the maximum value in each column. We can\u2019t use .applymap anymore since that operated elementwise. Instead, we\u2019ll turn to .apply which operates columnwise (or rowwise using the axis keyword). Later on we\u2019ll see that something like highlight_max is already defined on Styler so you wouldn\u2019t need to write this yourself.
\n@@ -609,96 +609,96 @@\n \n \n [8]:\n
\n \n \n A B C D E \n+ } A B C D E \n \n- 0 \n- 1.000000 \n- 1.329212 \n- nan \n- -0.316280 \n- -0.990810 \n- \n- \n- 1 \n- 2.000000 \n- -1.070816 \n- -1.438713 \n- 0.564417 \n- 0.295722 \n- \n- \n- 2 \n- 3.000000 \n- -1.626404 \n- 0.219565 \n- 0.678805 \n- 1.889273 \n- \n- \n- 3 \n- 4.000000 \n- 0.961538 \n- 0.104011 \n- nan \n- 0.850229 \n- \n- \n- 4 \n- 5.000000 \n- 1.453425 \n- 1.057737 \n- 0.165562 \n- 0.515018 \n- \n- \n- 5 \n- 6.000000 \n- -1.336936 \n- 0.562861 \n- 1.392855 \n- -0.063328 \n- \n- \n- 6 \n- 7.000000 \n- 0.121668 \n- 1.207603 \n- -0.002040 \n- 1.627796 \n- \n- \n- 7 \n- 8.000000 \n- 0.354493 \n- 1.037528 \n- -0.385684 \n- 0.519818 \n- \n- \n- 8 \n- 9.000000 \n- 1.686583 \n- -1.325963 \n- 1.428984 \n- -2.089354 \n- \n- \n- 9 \n- 10.000000 \n- -0.129820 \n- 0.631523 \n- -0.586538 \n- 0.290720 \n+ 0 \n+ 1.000000 \n+ 1.329212 \n+ nan \n+ -0.316280 \n+ -0.990810 \n+ \n+ \n+ 1 \n+ 2.000000 \n+ -1.070816 \n+ -1.438713 \n+ 0.564417 \n+ 0.295722 \n+ \n+ \n+ 2 \n+ 3.000000 \n+ -1.626404 \n+ 0.219565 \n+ 0.678805 \n+ 1.889273 \n+ \n+ \n+ 3 \n+ 4.000000 \n+ 0.961538 \n+ 0.104011 \n+ nan \n+ 0.850229 \n+ \n+ \n+ 4 \n+ 5.000000 \n+ 1.453425 \n+ 1.057737 \n+ 0.165562 \n+ 0.515018 \n+ \n+ \n+ 5 \n+ 6.000000 \n+ -1.336936 \n+ 0.562861 \n+ 1.392855 \n+ -0.063328 \n+ \n+ \n+ 6 \n+ 7.000000 \n+ 0.121668 \n+ 1.207603 \n+ -0.002040 \n+ 1.627796 \n+ \n+ \n+ 7 \n+ 8.000000 \n+ 0.354493 \n+ 1.037528 \n+ -0.385684 \n+ 0.519818 \n+ \n+ \n+ 8 \n+ 9.000000 \n+ 1.686583 \n+ -1.325963 \n+ 1.428984 \n+ -2.089354 \n+ \n+ \n+ 9 \n+ 10.000000 \n+ -0.129820 \n+ 0.631523 \n+ -0.586538 \n+ 0.290720 \n \n
\n \n In this case the input is a Series, one column at a time. Notice that the output shape of highlight_max matches the input shape, an array with len(s) items.
\n We encourage you to use method chains to build up a style piecewise, before finally rending at the end of the chain.
\n \n [9]:\n@@ -713,101 +713,101 @@\n
\n \n [9]:\n
\n \n \n A B C D E \n+ } A B C D E \n \n- 0 \n- 1.000000 \n- 1.329212 \n- nan \n- -0.316280 \n- -0.990810 \n- \n- \n- 1 \n- 2.000000 \n- -1.070816 \n- -1.438713 \n- 0.564417 \n- 0.295722 \n- \n- \n- 2 \n- 3.000000 \n- -1.626404 \n- 0.219565 \n- 0.678805 \n- 1.889273 \n- \n- \n- 3 \n- 4.000000 \n- 0.961538 \n- 0.104011 \n- nan \n- 0.850229 \n- \n- \n- 4 \n- 5.000000 \n- 1.453425 \n- 1.057737 \n- 0.165562 \n- 0.515018 \n- \n- \n- 5 \n- 6.000000 \n- -1.336936 \n- 0.562861 \n- 1.392855 \n- -0.063328 \n- \n- \n- 6 \n- 7.000000 \n- 0.121668 \n- 1.207603 \n- -0.002040 \n- 1.627796 \n- \n- \n- 7 \n- 8.000000 \n- 0.354493 \n- 1.037528 \n- -0.385684 \n- 0.519818 \n- \n- \n- 8 \n- 9.000000 \n- 1.686583 \n- -1.325963 \n- 1.428984 \n- -2.089354 \n- \n- \n- 9 \n- 10.000000 \n- -0.129820 \n- 0.631523 \n- -0.586538 \n- 0.290720 \n+ 0 \n+ 1.000000 \n+ 1.329212 \n+ nan \n+ -0.316280 \n+ -0.990810 \n+ \n+ \n+ 1 \n+ 2.000000 \n+ -1.070816 \n+ -1.438713 \n+ 0.564417 \n+ 0.295722 \n+ \n+ \n+ 2 \n+ 3.000000 \n+ -1.626404 \n+ 0.219565 \n+ 0.678805 \n+ 1.889273 \n+ \n+ \n+ 3 \n+ 4.000000 \n+ 0.961538 \n+ 0.104011 \n+ nan \n+ 0.850229 \n+ \n+ \n+ 4 \n+ 5.000000 \n+ 1.453425 \n+ 1.057737 \n+ 0.165562 \n+ 0.515018 \n+ \n+ \n+ 5 \n+ 6.000000 \n+ -1.336936 \n+ 0.562861 \n+ 1.392855 \n+ -0.063328 \n+ \n+ \n+ 6 \n+ 7.000000 \n+ 0.121668 \n+ 1.207603 \n+ -0.002040 \n+ 1.627796 \n+ \n+ \n+ 7 \n+ 8.000000 \n+ 0.354493 \n+ 1.037528 \n+ -0.385684 \n+ 0.519818 \n+ \n+ \n+ 8 \n+ 9.000000 \n+ 1.686583 \n+ -1.325963 \n+ 1.428984 \n+ -2.089354 \n+ \n+ \n+ 9 \n+ 10.000000 \n+ -0.129820 \n+ 0.631523 \n+ -0.586538 \n+ 0.290720 \n \n
\n \n Above we used Styler.apply to pass in each column one at a time.
\n Debugging Tip: If you\u2019re having trouble writing your style function, try just passing it into DataFrame.apply. Internally, Styler.apply uses DataFrame.apply so the result should be the same.
\n What if you wanted to highlight just the maximum value in the entire table? Use .apply(function, axis=None) to indicate that your function wants the entire table, not one column or row at a time. Let\u2019s try that next.
\n We\u2019ll rewrite our highlight-max to handle either Series (from .apply(axis=0 or 1)) or DataFrames (from .apply(axis=None)). We\u2019ll also allow the color to be adjustable, to demonstrate that .apply, and .applymap pass along keyword arguments.
\n@@ -843,96 +843,96 @@\n \n \n [11]:\n
\n \n \n A B C D E \n+ } A B C D E \n \n- 0 \n- 1.000000 \n- 1.329212 \n- nan \n- -0.316280 \n- -0.990810 \n- \n- \n- 1 \n- 2.000000 \n- -1.070816 \n- -1.438713 \n- 0.564417 \n- 0.295722 \n- \n- \n- 2 \n- 3.000000 \n- -1.626404 \n- 0.219565 \n- 0.678805 \n- 1.889273 \n- \n- \n- 3 \n- 4.000000 \n- 0.961538 \n- 0.104011 \n- nan \n- 0.850229 \n- \n- \n- 4 \n- 5.000000 \n- 1.453425 \n- 1.057737 \n- 0.165562 \n- 0.515018 \n- \n- \n- 5 \n- 6.000000 \n- -1.336936 \n- 0.562861 \n- 1.392855 \n- -0.063328 \n- \n- \n- 6 \n- 7.000000 \n- 0.121668 \n- 1.207603 \n- -0.002040 \n- 1.627796 \n- \n- \n- 7 \n- 8.000000 \n- 0.354493 \n- 1.037528 \n- -0.385684 \n- 0.519818 \n- \n- \n- 8 \n- 9.000000 \n- 1.686583 \n- -1.325963 \n- 1.428984 \n- -2.089354 \n- \n- \n- 9 \n- 10.000000 \n- -0.129820 \n- 0.631523 \n- -0.586538 \n- 0.290720 \n+ 0 \n+ 1.000000 \n+ 1.329212 \n+ nan \n+ -0.316280 \n+ -0.990810 \n+ \n+ \n+ 1 \n+ 2.000000 \n+ -1.070816 \n+ -1.438713 \n+ 0.564417 \n+ 0.295722 \n+ \n+ \n+ 2 \n+ 3.000000 \n+ -1.626404 \n+ 0.219565 \n+ 0.678805 \n+ 1.889273 \n+ \n+ \n+ 3 \n+ 4.000000 \n+ 0.961538 \n+ 0.104011 \n+ nan \n+ 0.850229 \n+ \n+ \n+ 4 \n+ 5.000000 \n+ 1.453425 \n+ 1.057737 \n+ 0.165562 \n+ 0.515018 \n+ \n+ \n+ 5 \n+ 6.000000 \n+ -1.336936 \n+ 0.562861 \n+ 1.392855 \n+ -0.063328 \n+ \n+ \n+ 6 \n+ 7.000000 \n+ 0.121668 \n+ 1.207603 \n+ -0.002040 \n+ 1.627796 \n+ \n+ \n+ 7 \n+ 8.000000 \n+ 0.354493 \n+ 1.037528 \n+ -0.385684 \n+ 0.519818 \n+ \n+ \n+ 8 \n+ 9.000000 \n+ 1.686583 \n+ -1.325963 \n+ 1.428984 \n+ -2.089354 \n+ \n+ \n+ 9 \n+ 10.000000 \n+ -0.129820 \n+ 0.631523 \n+ -0.586538 \n+ 0.290720 \n \n
\n \n \n Building Styles Summary\u00b6
\n Style functions should return strings with one or more CSS attribute: value delimited by semicolons. Use
\n \n@@ -965,96 +965,96 @@\n
\n \n [12]:\n
\n \n \n A B C D E \n+ } A B C D E \n \n- 0 \n- 1.000000 \n- 1.329212 \n- nan \n- -0.316280 \n- -0.990810 \n- \n- \n- 1 \n- 2.000000 \n- -1.070816 \n- -1.438713 \n- 0.564417 \n- 0.295722 \n- \n- \n- 2 \n- 3.000000 \n- -1.626404 \n- 0.219565 \n- 0.678805 \n- 1.889273 \n- \n- \n- 3 \n- 4.000000 \n- 0.961538 \n- 0.104011 \n- nan \n- 0.850229 \n- \n- \n- 4 \n- 5.000000 \n- 1.453425 \n- 1.057737 \n- 0.165562 \n- 0.515018 \n- \n- \n- 5 \n- 6.000000 \n- -1.336936 \n- 0.562861 \n- 1.392855 \n- -0.063328 \n- \n- \n- 6 \n- 7.000000 \n- 0.121668 \n- 1.207603 \n- -0.002040 \n- 1.627796 \n- \n- \n- 7 \n- 8.000000 \n- 0.354493 \n- 1.037528 \n- -0.385684 \n- 0.519818 \n- \n- \n- 8 \n- 9.000000 \n- 1.686583 \n- -1.325963 \n- 1.428984 \n- -2.089354 \n- \n- \n- 9 \n- 10.000000 \n- -0.129820 \n- 0.631523 \n- -0.586538 \n- 0.290720 \n+ 0 \n+ 1.000000 \n+ 1.329212 \n+ nan \n+ -0.316280 \n+ -0.990810 \n+ \n+ \n+ 1 \n+ 2.000000 \n+ -1.070816 \n+ -1.438713 \n+ 0.564417 \n+ 0.295722 \n+ \n+ \n+ 2 \n+ 3.000000 \n+ -1.626404 \n+ 0.219565 \n+ 0.678805 \n+ 1.889273 \n+ \n+ \n+ 3 \n+ 4.000000 \n+ 0.961538 \n+ 0.104011 \n+ nan \n+ 0.850229 \n+ \n+ \n+ 4 \n+ 5.000000 \n+ 1.453425 \n+ 1.057737 \n+ 0.165562 \n+ 0.515018 \n+ \n+ \n+ 5 \n+ 6.000000 \n+ -1.336936 \n+ 0.562861 \n+ 1.392855 \n+ -0.063328 \n+ \n+ \n+ 6 \n+ 7.000000 \n+ 0.121668 \n+ 1.207603 \n+ -0.002040 \n+ 1.627796 \n+ \n+ \n+ 7 \n+ 8.000000 \n+ 0.354493 \n+ 1.037528 \n+ -0.385684 \n+ 0.519818 \n+ \n+ \n+ 8 \n+ 9.000000 \n+ 1.686583 \n+ -1.325963 \n+ 1.428984 \n+ -2.089354 \n+ \n+ \n+ 9 \n+ 10.000000 \n+ -0.129820 \n+ 0.631523 \n+ -0.586538 \n+ 0.290720 \n \n
\n \n For row and column slicing, any valid indexer to .loc will work.
\n \n [13]:\n
\n@@ -1067,98 +1067,98 @@\n \n \n [13]:\n
\n \n \n A B C D E \n+ } A B C D E \n \n- 0 \n- 1.000000 \n- 1.329212 \n- nan \n- -0.316280 \n- -0.990810 \n- \n- \n- 1 \n- 2.000000 \n- -1.070816 \n- -1.438713 \n- 0.564417 \n- 0.295722 \n- \n- \n- 2 \n- 3.000000 \n- -1.626404 \n- 0.219565 \n- 0.678805 \n- 1.889273 \n- \n- \n- 3 \n- 4.000000 \n- 0.961538 \n- 0.104011 \n- nan \n- 0.850229 \n- \n- \n- 4 \n- 5.000000 \n- 1.453425 \n- 1.057737 \n- 0.165562 \n- 0.515018 \n- \n- \n- 5 \n- 6.000000 \n- -1.336936 \n- 0.562861 \n- 1.392855 \n- -0.063328 \n- \n- \n- 6 \n- 7.000000 \n- 0.121668 \n- 1.207603 \n- -0.002040 \n- 1.627796 \n- \n- \n- 7 \n- 8.000000 \n- 0.354493 \n- 1.037528 \n- -0.385684 \n- 0.519818 \n- \n- \n- 8 \n- 9.000000 \n- 1.686583 \n- -1.325963 \n- 1.428984 \n- -2.089354 \n- \n- \n- 9 \n- 10.000000 \n- -0.129820 \n- 0.631523 \n- -0.586538 \n- 0.290720 \n+ 0 \n+ 1.000000 \n+ 1.329212 \n+ nan \n+ -0.316280 \n+ -0.990810 \n+ \n+ \n+ 1 \n+ 2.000000 \n+ -1.070816 \n+ -1.438713 \n+ 0.564417 \n+ 0.295722 \n+ \n+ \n+ 2 \n+ 3.000000 \n+ -1.626404 \n+ 0.219565 \n+ 0.678805 \n+ 1.889273 \n+ \n+ \n+ 3 \n+ 4.000000 \n+ 0.961538 \n+ 0.104011 \n+ nan \n+ 0.850229 \n+ \n+ \n+ 4 \n+ 5.000000 \n+ 1.453425 \n+ 1.057737 \n+ 0.165562 \n+ 0.515018 \n+ \n+ \n+ 5 \n+ 6.000000 \n+ -1.336936 \n+ 0.562861 \n+ 1.392855 \n+ -0.063328 \n+ \n+ \n+ 6 \n+ 7.000000 \n+ 0.121668 \n+ 1.207603 \n+ -0.002040 \n+ 1.627796 \n+ \n+ \n+ 7 \n+ 8.000000 \n+ 0.354493 \n+ 1.037528 \n+ -0.385684 \n+ 0.519818 \n+ \n+ \n+ 8 \n+ 9.000000 \n+ 1.686583 \n+ -1.325963 \n+ 1.428984 \n+ -2.089354 \n+ \n+ \n+ 9 \n+ 10.000000 \n+ -0.129820 \n+ 0.631523 \n+ -0.586538 \n+ 0.290720 \n \n
\n \n Only label-based slicing is supported right now, not positional.
\n If your style function uses a subset or axis keyword argument, consider wrapping your function in a functools.partial, partialing out that keyword.
\n my_func2 = functools.partial(my_func, subset=42)\n
\n@@ -1178,94 +1178,94 @@\n \n \n [14]:\n
\n \n \n A B C D E \n+ A B C D E \n \n- 0 \n- 100.00% \n- 132.92% \n- nan% \n- -31.63% \n- -99.08% \n- \n- \n- 1 \n- 200.00% \n- -107.08% \n- -143.87% \n- 56.44% \n- 29.57% \n- \n- \n- 2 \n- 300.00% \n- -162.64% \n- 21.96% \n- 67.88% \n- 188.93% \n- \n- \n- 3 \n- 400.00% \n- 96.15% \n- 10.40% \n- nan% \n- 85.02% \n- \n- \n- 4 \n- 500.00% \n- 145.34% \n- 105.77% \n- 16.56% \n- 51.50% \n- \n- \n- 5 \n- 600.00% \n- -133.69% \n- 56.29% \n- 139.29% \n- -6.33% \n- \n- \n- 6 \n- 700.00% \n- 12.17% \n- 120.76% \n- -0.20% \n- 162.78% \n- \n- \n- 7 \n- 800.00% \n- 35.45% \n- 103.75% \n- -38.57% \n- 51.98% \n- \n- \n- 8 \n- 900.00% \n- 168.66% \n- -132.60% \n- 142.90% \n- -208.94% \n- \n- \n- 9 \n- 1000.00% \n- -12.98% \n- 63.15% \n- -58.65% \n- 29.07% \n+ 0 \n+ 100.00% \n+ 132.92% \n+ nan% \n+ -31.63% \n+ -99.08% \n+ \n+ \n+ 1 \n+ 200.00% \n+ -107.08% \n+ -143.87% \n+ 56.44% \n+ 29.57% \n+ \n+ \n+ 2 \n+ 300.00% \n+ -162.64% \n+ 21.96% \n+ 67.88% \n+ 188.93% \n+ \n+ \n+ 3 \n+ 400.00% \n+ 96.15% \n+ 10.40% \n+ nan% \n+ 85.02% \n+ \n+ \n+ 4 \n+ 500.00% \n+ 145.34% \n+ 105.77% \n+ 16.56% \n+ 51.50% \n+ \n+ \n+ 5 \n+ 600.00% \n+ -133.69% \n+ 56.29% \n+ 139.29% \n+ -6.33% \n+ \n+ \n+ 6 \n+ 700.00% \n+ 12.17% \n+ 120.76% \n+ -0.20% \n+ 162.78% \n+ \n+ \n+ 7 \n+ 800.00% \n+ 35.45% \n+ 103.75% \n+ -38.57% \n+ 51.98% \n+ \n+ \n+ 8 \n+ 900.00% \n+ 168.66% \n+ -132.60% \n+ 142.90% \n+ -208.94% \n+ \n+ \n+ 9 \n+ 1000.00% \n+ -12.98% \n+ 63.15% \n+ -58.65% \n+ 29.07% \n \n
\n \n Use a dictionary to format specific columns.
\n \n [15]:\n
\n@@ -1277,94 +1277,94 @@\n \n \n [15]:\n
\n \n \n A B C D E \n+ A B C D E \n \n- 0 \n- 1.000000 \n- 1000 \n- nan \n- -0.32 \n- -0.990810 \n- \n- \n- 1 \n- 2.000000 \n- -100 \n- -1.438713 \n- +0.56 \n- 0.295722 \n- \n- \n- 2 \n- 3.000000 \n- -200 \n- 0.219565 \n- +0.68 \n- 1.889273 \n- \n- \n- 3 \n- 4.000000 \n- 1000 \n- 0.104011 \n- +nan \n- 0.850229 \n- \n- \n- 4 \n- 5.000000 \n- 1000 \n- 1.057737 \n- +0.17 \n- 0.515018 \n- \n- \n- 5 \n- 6.000000 \n- -100 \n- 0.562861 \n- +1.39 \n- -0.063328 \n- \n- \n- 6 \n- 7.000000 \n- 0000 \n- 1.207603 \n- -0.00 \n- 1.627796 \n- \n- \n- 7 \n- 8.000000 \n- 0000 \n- 1.037528 \n- -0.39 \n- 0.519818 \n- \n- \n- 8 \n- 9.000000 \n- 2000 \n- -1.325963 \n- +1.43 \n- -2.089354 \n- \n- \n- 9 \n- 10.000000 \n- -000 \n- 0.631523 \n- -0.59 \n- 0.290720 \n+ 0 \n+ 1.000000 \n+ 1000 \n+ nan \n+ -0.32 \n+ -0.990810 \n+ \n+ \n+ 1 \n+ 2.000000 \n+ -100 \n+ -1.438713 \n+ +0.56 \n+ 0.295722 \n+ \n+ \n+ 2 \n+ 3.000000 \n+ -200 \n+ 0.219565 \n+ +0.68 \n+ 1.889273 \n+ \n+ \n+ 3 \n+ 4.000000 \n+ 1000 \n+ 0.104011 \n+ +nan \n+ 0.850229 \n+ \n+ \n+ 4 \n+ 5.000000 \n+ 1000 \n+ 1.057737 \n+ +0.17 \n+ 0.515018 \n+ \n+ \n+ 5 \n+ 6.000000 \n+ -100 \n+ 0.562861 \n+ +1.39 \n+ -0.063328 \n+ \n+ \n+ 6 \n+ 7.000000 \n+ 0000 \n+ 1.207603 \n+ -0.00 \n+ 1.627796 \n+ \n+ \n+ 7 \n+ 8.000000 \n+ 0000 \n+ 1.037528 \n+ -0.39 \n+ 0.519818 \n+ \n+ \n+ 8 \n+ 9.000000 \n+ 2000 \n+ -1.325963 \n+ +1.43 \n+ -2.089354 \n+ \n+ \n+ 9 \n+ 10.000000 \n+ -000 \n+ 0.631523 \n+ -0.59 \n+ 0.290720 \n \n
\n \n Or pass in a callable (or dictionary of callables) for more flexible handling.
\n \n [16]:\n
\n@@ -1376,94 +1376,94 @@\n \n \n [16]:\n
\n \n \n A B C D E \n+ A B C D E \n \n- 0 \n- 1.000000 \n- \u00b11.33 \n- nan \n- -0.316280 \n- -0.990810 \n- \n- \n- 1 \n- 2.000000 \n- \u00b11.07 \n- -1.438713 \n- 0.564417 \n- 0.295722 \n- \n- \n- 2 \n- 3.000000 \n- \u00b11.63 \n- 0.219565 \n- 0.678805 \n- 1.889273 \n- \n- \n- 3 \n- 4.000000 \n- \u00b10.96 \n- 0.104011 \n- nan \n- 0.850229 \n- \n- \n- 4 \n- 5.000000 \n- \u00b11.45 \n- 1.057737 \n- 0.165562 \n- 0.515018 \n- \n- \n- 5 \n- 6.000000 \n- \u00b11.34 \n- 0.562861 \n- 1.392855 \n- -0.063328 \n- \n- \n- 6 \n- 7.000000 \n- \u00b10.12 \n- 1.207603 \n- -0.002040 \n- 1.627796 \n- \n- \n- 7 \n- 8.000000 \n- \u00b10.35 \n- 1.037528 \n- -0.385684 \n- 0.519818 \n- \n- \n- 8 \n- 9.000000 \n- \u00b11.69 \n- -1.325963 \n- 1.428984 \n- -2.089354 \n- \n- \n- 9 \n- 10.000000 \n- \u00b10.13 \n- 0.631523 \n- -0.586538 \n- 0.290720 \n+ 0 \n+ 1.000000 \n+ \u00b11.33 \n+ nan \n+ -0.316280 \n+ -0.990810 \n+ \n+ \n+ 1 \n+ 2.000000 \n+ \u00b11.07 \n+ -1.438713 \n+ 0.564417 \n+ 0.295722 \n+ \n+ \n+ 2 \n+ 3.000000 \n+ \u00b11.63 \n+ 0.219565 \n+ 0.678805 \n+ 1.889273 \n+ \n+ \n+ 3 \n+ 4.000000 \n+ \u00b10.96 \n+ 0.104011 \n+ nan \n+ 0.850229 \n+ \n+ \n+ 4 \n+ 5.000000 \n+ \u00b11.45 \n+ 1.057737 \n+ 0.165562 \n+ 0.515018 \n+ \n+ \n+ 5 \n+ 6.000000 \n+ \u00b11.34 \n+ 0.562861 \n+ 1.392855 \n+ -0.063328 \n+ \n+ \n+ 6 \n+ 7.000000 \n+ \u00b10.12 \n+ 1.207603 \n+ -0.002040 \n+ 1.627796 \n+ \n+ \n+ 7 \n+ 8.000000 \n+ \u00b10.35 \n+ 1.037528 \n+ -0.385684 \n+ 0.519818 \n+ \n+ \n+ 8 \n+ 9.000000 \n+ \u00b11.69 \n+ -1.325963 \n+ 1.428984 \n+ -2.089354 \n+ \n+ \n+ 9 \n+ 10.000000 \n+ \u00b10.13 \n+ 0.631523 \n+ -0.586538 \n+ 0.290720 \n \n
\n \n You can format the text displayed for missing values by na_rep.
\n \n [17]:\n
\n@@ -1475,94 +1475,94 @@\n \n \n [17]:\n
\n \n \n A B C D E \n+ A B C D E \n \n- 0 \n- 100.00% \n- 132.92% \n- - \n- -31.63% \n- -99.08% \n- \n- \n- 1 \n- 200.00% \n- -107.08% \n- -143.87% \n- 56.44% \n- 29.57% \n- \n- \n- 2 \n- 300.00% \n- -162.64% \n- 21.96% \n- 67.88% \n- 188.93% \n- \n- \n- 3 \n- 400.00% \n- 96.15% \n- 10.40% \n- - \n- 85.02% \n- \n- \n- 4 \n- 500.00% \n- 145.34% \n- 105.77% \n- 16.56% \n- 51.50% \n- \n- \n- 5 \n- 600.00% \n- -133.69% \n- 56.29% \n- 139.29% \n- -6.33% \n- \n- \n- 6 \n- 700.00% \n- 12.17% \n- 120.76% \n- -0.20% \n- 162.78% \n- \n- \n- 7 \n- 800.00% \n- 35.45% \n- 103.75% \n- -38.57% \n- 51.98% \n- \n- \n- 8 \n- 900.00% \n- 168.66% \n- -132.60% \n- 142.90% \n- -208.94% \n- \n- \n- 9 \n- 1000.00% \n- -12.98% \n- 63.15% \n- -58.65% \n- 29.07% \n+ 0 \n+ 100.00% \n+ 132.92% \n+ - \n+ -31.63% \n+ -99.08% \n+ \n+ \n+ 1 \n+ 200.00% \n+ -107.08% \n+ -143.87% \n+ 56.44% \n+ 29.57% \n+ \n+ \n+ 2 \n+ 300.00% \n+ -162.64% \n+ 21.96% \n+ 67.88% \n+ 188.93% \n+ \n+ \n+ 3 \n+ 400.00% \n+ 96.15% \n+ 10.40% \n+ - \n+ 85.02% \n+ \n+ \n+ 4 \n+ 500.00% \n+ 145.34% \n+ 105.77% \n+ 16.56% \n+ 51.50% \n+ \n+ \n+ 5 \n+ 600.00% \n+ -133.69% \n+ 56.29% \n+ 139.29% \n+ -6.33% \n+ \n+ \n+ 6 \n+ 700.00% \n+ 12.17% \n+ 120.76% \n+ -0.20% \n+ 162.78% \n+ \n+ \n+ 7 \n+ 800.00% \n+ 35.45% \n+ 103.75% \n+ -38.57% \n+ 51.98% \n+ \n+ \n+ 8 \n+ 900.00% \n+ 168.66% \n+ -132.60% \n+ 142.90% \n+ -208.94% \n+ \n+ \n+ 9 \n+ 1000.00% \n+ -12.98% \n+ 63.15% \n+ -58.65% \n+ 29.07% \n \n
\n \n These formatting techniques can be used in combination with styling.
\n \n [18]:\n
\n@@ -1574,96 +1574,96 @@\n \n \n [18]:\n
\n \n \n A B C D E \n+ } A B C D E \n \n- 0 \n- 1.000000 \n- 1.329212 \n- - \n- -0.316280 \n- -0.990810 \n- \n- \n- 1 \n- 2.000000 \n- -1.070816 \n- -1.438713 \n- 0.564417 \n- 0.295722 \n- \n- \n- 2 \n- 3.000000 \n- -1.626404 \n- 0.219565 \n- 0.678805 \n- 1.889273 \n- \n- \n- 3 \n- 4.000000 \n- 0.961538 \n- 0.104011 \n- - \n- 0.850229 \n- \n- \n- 4 \n- 5.000000 \n- 1.453425 \n- 1.057737 \n- 0.165562 \n- 0.515018 \n- \n- \n- 5 \n- 6.000000 \n- -1.336936 \n- 0.562861 \n- 1.392855 \n- -0.063328 \n- \n- \n- 6 \n- 7.000000 \n- 0.121668 \n- 1.207603 \n- -0.002040 \n- 1.627796 \n- \n- \n- 7 \n- 8.000000 \n- 0.354493 \n- 1.037528 \n- -0.385684 \n- 0.519818 \n- \n- \n- 8 \n- 9.000000 \n- 1.686583 \n- -1.325963 \n- 1.428984 \n- -2.089354 \n- \n- \n- 9 \n- 10.000000 \n- -0.129820 \n- 0.631523 \n- -0.586538 \n- 0.290720 \n+ 0 \n+ 1.000000 \n+ 1.329212 \n+ - \n+ -0.316280 \n+ -0.990810 \n+ \n+ \n+ 1 \n+ 2.000000 \n+ -1.070816 \n+ -1.438713 \n+ 0.564417 \n+ 0.295722 \n+ \n+ \n+ 2 \n+ 3.000000 \n+ -1.626404 \n+ 0.219565 \n+ 0.678805 \n+ 1.889273 \n+ \n+ \n+ 3 \n+ 4.000000 \n+ 0.961538 \n+ 0.104011 \n+ - \n+ 0.850229 \n+ \n+ \n+ 4 \n+ 5.000000 \n+ 1.453425 \n+ 1.057737 \n+ 0.165562 \n+ 0.515018 \n+ \n+ \n+ 5 \n+ 6.000000 \n+ -1.336936 \n+ 0.562861 \n+ 1.392855 \n+ -0.063328 \n+ \n+ \n+ 6 \n+ 7.000000 \n+ 0.121668 \n+ 1.207603 \n+ -0.002040 \n+ 1.627796 \n+ \n+ \n+ 7 \n+ 8.000000 \n+ 0.354493 \n+ 1.037528 \n+ -0.385684 \n+ 0.519818 \n+ \n+ \n+ 8 \n+ 9.000000 \n+ 1.686583 \n+ -1.325963 \n+ 1.428984 \n+ -2.089354 \n+ \n+ \n+ 9 \n+ 10.000000 \n+ -0.129820 \n+ 0.631523 \n+ -0.586538 \n+ 0.290720 \n \n
\n \n \n \n Builtin styles\u00b6
\n Finally, we expect certain styling functions to be common enough that we\u2019ve included a few \u201cbuilt-in\u201d to the Styler, so you don\u2019t have to write them yourself.
\n@@ -1678,96 +1678,96 @@\n \n \n [19]:\n
\n \n \n A B C D E \n+ } A B C D E \n \n- 0 \n- 1.000000 \n- 1.329212 \n- nan \n- -0.316280 \n- -0.990810 \n- \n- \n- 1 \n- 2.000000 \n- -1.070816 \n- -1.438713 \n- 0.564417 \n- 0.295722 \n- \n- \n- 2 \n- 3.000000 \n- -1.626404 \n- 0.219565 \n- 0.678805 \n- 1.889273 \n- \n- \n- 3 \n- 4.000000 \n- 0.961538 \n- 0.104011 \n- nan \n- 0.850229 \n- \n- \n- 4 \n- 5.000000 \n- 1.453425 \n- 1.057737 \n- 0.165562 \n- 0.515018 \n- \n- \n- 5 \n- 6.000000 \n- -1.336936 \n- 0.562861 \n- 1.392855 \n- -0.063328 \n- \n- \n- 6 \n- 7.000000 \n- 0.121668 \n- 1.207603 \n- -0.002040 \n- 1.627796 \n- \n- \n- 7 \n- 8.000000 \n- 0.354493 \n- 1.037528 \n- -0.385684 \n- 0.519818 \n- \n- \n- 8 \n- 9.000000 \n- 1.686583 \n- -1.325963 \n- 1.428984 \n- -2.089354 \n- \n- \n- 9 \n- 10.000000 \n- -0.129820 \n- 0.631523 \n- -0.586538 \n- 0.290720 \n+ 0 \n+ 1.000000 \n+ 1.329212 \n+ nan \n+ -0.316280 \n+ -0.990810 \n+ \n+ \n+ 1 \n+ 2.000000 \n+ -1.070816 \n+ -1.438713 \n+ 0.564417 \n+ 0.295722 \n+ \n+ \n+ 2 \n+ 3.000000 \n+ -1.626404 \n+ 0.219565 \n+ 0.678805 \n+ 1.889273 \n+ \n+ \n+ 3 \n+ 4.000000 \n+ 0.961538 \n+ 0.104011 \n+ nan \n+ 0.850229 \n+ \n+ \n+ 4 \n+ 5.000000 \n+ 1.453425 \n+ 1.057737 \n+ 0.165562 \n+ 0.515018 \n+ \n+ \n+ 5 \n+ 6.000000 \n+ -1.336936 \n+ 0.562861 \n+ 1.392855 \n+ -0.063328 \n+ \n+ \n+ 6 \n+ 7.000000 \n+ 0.121668 \n+ 1.207603 \n+ -0.002040 \n+ 1.627796 \n+ \n+ \n+ 7 \n+ 8.000000 \n+ 0.354493 \n+ 1.037528 \n+ -0.385684 \n+ 0.519818 \n+ \n+ \n+ 8 \n+ 9.000000 \n+ 1.686583 \n+ -1.325963 \n+ 1.428984 \n+ -2.089354 \n+ \n+ \n+ 9 \n+ 10.000000 \n+ -0.129820 \n+ 0.631523 \n+ -0.586538 \n+ 0.290720 \n \n
\n \n You can create \u201cheatmaps\u201d with the background_gradient method. These require matplotlib, and we\u2019ll use Seaborn to get a nice colormap.
\n \n [20]:\n
\n@@ -1784,199 +1784,199 @@\n \n \n [20]:\n
\n \n \n A B C D E \n+ } A B C D E \n \n- 0 \n- 1.000000 \n- 1.329212 \n- nan \n- -0.316280 \n- -0.990810 \n- \n- \n- 1 \n- 2.000000 \n- -1.070816 \n- -1.438713 \n- 0.564417 \n- 0.295722 \n- \n- \n- 2 \n- 3.000000 \n- -1.626404 \n- 0.219565 \n- 0.678805 \n- 1.889273 \n- \n- \n- 3 \n- 4.000000 \n- 0.961538 \n- 0.104011 \n- nan \n- 0.850229 \n- \n- \n- 4 \n- 5.000000 \n- 1.453425 \n- 1.057737 \n- 0.165562 \n- 0.515018 \n- \n- \n- 5 \n- 6.000000 \n- -1.336936 \n- 0.562861 \n- 1.392855 \n- -0.063328 \n- \n- \n- 6 \n- 7.000000 \n- 0.121668 \n- 1.207603 \n- -0.002040 \n- 1.627796 \n- \n- \n- 7 \n- 8.000000 \n- 0.354493 \n- 1.037528 \n- -0.385684 \n- 0.519818 \n- \n- \n- 8 \n- 9.000000 \n- 1.686583 \n- -1.325963 \n- 1.428984 \n- -2.089354 \n- \n- \n- 9 \n- 10.000000 \n- -0.129820 \n- 0.631523 \n- -0.586538 \n- 0.290720 \n+ 0 \n+ 1.000000 \n+ 1.329212 \n+ nan \n+ -0.316280 \n+ -0.990810 \n+ \n+ \n+ 1 \n+ 2.000000 \n+ -1.070816 \n+ -1.438713 \n+ 0.564417 \n+ 0.295722 \n+ \n+ \n+ 2 \n+ 3.000000 \n+ -1.626404 \n+ 0.219565 \n+ 0.678805 \n+ 1.889273 \n+ \n+ \n+ 3 \n+ 4.000000 \n+ 0.961538 \n+ 0.104011 \n+ nan \n+ 0.850229 \n+ \n+ \n+ 4 \n+ 5.000000 \n+ 1.453425 \n+ 1.057737 \n+ 0.165562 \n+ 0.515018 \n+ \n+ \n+ 5 \n+ 6.000000 \n+ -1.336936 \n+ 0.562861 \n+ 1.392855 \n+ -0.063328 \n+ \n+ \n+ 6 \n+ 7.000000 \n+ 0.121668 \n+ 1.207603 \n+ -0.002040 \n+ 1.627796 \n+ \n+ \n+ 7 \n+ 8.000000 \n+ 0.354493 \n+ 1.037528 \n+ -0.385684 \n+ 0.519818 \n+ \n+ \n+ 8 \n+ 9.000000 \n+ 1.686583 \n+ -1.325963 \n+ 1.428984 \n+ -2.089354 \n+ \n+ \n+ 9 \n+ 10.000000 \n+ -0.129820 \n+ 0.631523 \n+ -0.586538 \n+ 0.290720 \n \n
\n \n Styler.background_gradient takes the keyword arguments low and high. Roughly speaking these extend the range of your data by low and high percent so that when we convert the colors, the colormap\u2019s entire range isn\u2019t used. This is useful so that you can actually read the text still.
\n \n [21]:\n
\n@@ -1989,102 +1989,102 @@\n \n \n [21]:\n
\n \n \n A B C D E \n+ } A B C D E \n \n- 0 \n- 1.000000 \n- 1.329212 \n- nan \n- -0.316280 \n- -0.990810 \n- \n- \n- 1 \n- 2.000000 \n- -1.070816 \n- -1.438713 \n- 0.564417 \n- 0.295722 \n- \n- \n- 2 \n- 3.000000 \n- -1.626404 \n- 0.219565 \n- 0.678805 \n- 1.889273 \n- \n- \n- 3 \n- 4.000000 \n- 0.961538 \n- 0.104011 \n- nan \n- 0.850229 \n- \n- \n- 4 \n- 5.000000 \n- 1.453425 \n- 1.057737 \n- 0.165562 \n- 0.515018 \n+ 0 \n+ 1.000000 \n+ 1.329212 \n+ nan \n+ -0.316280 \n+ -0.990810 \n+ \n+ \n+ 1 \n+ 2.000000 \n+ -1.070816 \n+ -1.438713 \n+ 0.564417 \n+ 0.295722 \n+ \n+ \n+ 2 \n+ 3.000000 \n+ -1.626404 \n+ 0.219565 \n+ 0.678805 \n+ 1.889273 \n+ \n+ \n+ 3 \n+ 4.000000 \n+ 0.961538 \n+ 0.104011 \n+ nan \n+ 0.850229 \n+ \n+ \n+ 4 \n+ 5.000000 \n+ 1.453425 \n+ 1.057737 \n+ 0.165562 \n+ 0.515018 \n \n
\n \n \n [22]:\n
\n \n@@ -2099,103 +2099,103 @@\n \n \n [22]:\n
\n \n \n A B C D E \n+ } A B C D E \n \n- 0 \n- 1.000000 \n- 1.329212 \n- nan \n- -0.316280 \n- -0.990810 \n- \n- \n- 1 \n- 2.000000 \n- -1.070816 \n- -1.438713 \n- 0.564417 \n- 0.295722 \n- \n- \n- 2 \n- 3.000000 \n- -1.626404 \n- 0.219565 \n- 0.678805 \n- 1.889273 \n- \n- \n- 3 \n- 4.000000 \n- 0.961538 \n- 0.104011 \n- nan \n- 0.850229 \n- \n- \n- 4 \n- 5.000000 \n- 1.453425 \n- 1.057737 \n- 0.165562 \n- 0.515018 \n+ 0 \n+ 1.000000 \n+ 1.329212 \n+ nan \n+ -0.316280 \n+ -0.990810 \n+ \n+ \n+ 1 \n+ 2.000000 \n+ -1.070816 \n+ -1.438713 \n+ 0.564417 \n+ 0.295722 \n+ \n+ \n+ 2 \n+ 3.000000 \n+ -1.626404 \n+ 0.219565 \n+ 0.678805 \n+ 1.889273 \n+ \n+ \n+ 3 \n+ 4.000000 \n+ 0.961538 \n+ 0.104011 \n+ nan \n+ 0.850229 \n+ \n+ \n+ 4 \n+ 5.000000 \n+ 1.453425 \n+ 1.057737 \n+ 0.165562 \n+ 0.515018 \n \n
\n \n There\u2019s also .highlight_min and .highlight_max.
\n \n [23]:\n
\n@@ -2207,96 +2207,96 @@\n \n \n [23]:\n
\n \n \n A B C D E \n+ } A B C D E \n \n- 0 \n- 1.000000 \n- 1.329212 \n- nan \n- -0.316280 \n- -0.990810 \n- \n- \n- 1 \n- 2.000000 \n- -1.070816 \n- -1.438713 \n- 0.564417 \n- 0.295722 \n- \n- \n- 2 \n- 3.000000 \n- -1.626404 \n- 0.219565 \n- 0.678805 \n- 1.889273 \n- \n- \n- 3 \n- 4.000000 \n- 0.961538 \n- 0.104011 \n- nan \n- 0.850229 \n- \n- \n- 4 \n- 5.000000 \n- 1.453425 \n- 1.057737 \n- 0.165562 \n- 0.515018 \n- \n- \n- 5 \n- 6.000000 \n- -1.336936 \n- 0.562861 \n- 1.392855 \n- -0.063328 \n- \n- \n- 6 \n- 7.000000 \n- 0.121668 \n- 1.207603 \n- -0.002040 \n- 1.627796 \n- \n- \n- 7 \n- 8.000000 \n- 0.354493 \n- 1.037528 \n- -0.385684 \n- 0.519818 \n- \n- \n- 8 \n- 9.000000 \n- 1.686583 \n- -1.325963 \n- 1.428984 \n- -2.089354 \n- \n- \n- 9 \n- 10.000000 \n- -0.129820 \n- 0.631523 \n- -0.586538 \n- 0.290720 \n+ 0 \n+ 1.000000 \n+ 1.329212 \n+ nan \n+ -0.316280 \n+ -0.990810 \n+ \n+ \n+ 1 \n+ 2.000000 \n+ -1.070816 \n+ -1.438713 \n+ 0.564417 \n+ 0.295722 \n+ \n+ \n+ 2 \n+ 3.000000 \n+ -1.626404 \n+ 0.219565 \n+ 0.678805 \n+ 1.889273 \n+ \n+ \n+ 3 \n+ 4.000000 \n+ 0.961538 \n+ 0.104011 \n+ nan \n+ 0.850229 \n+ \n+ \n+ 4 \n+ 5.000000 \n+ 1.453425 \n+ 1.057737 \n+ 0.165562 \n+ 0.515018 \n+ \n+ \n+ 5 \n+ 6.000000 \n+ -1.336936 \n+ 0.562861 \n+ 1.392855 \n+ -0.063328 \n+ \n+ \n+ 6 \n+ 7.000000 \n+ 0.121668 \n+ 1.207603 \n+ -0.002040 \n+ 1.627796 \n+ \n+ \n+ 7 \n+ 8.000000 \n+ 0.354493 \n+ 1.037528 \n+ -0.385684 \n+ 0.519818 \n+ \n+ \n+ 8 \n+ 9.000000 \n+ 1.686583 \n+ -1.325963 \n+ 1.428984 \n+ -2.089354 \n+ \n+ \n+ 9 \n+ 10.000000 \n+ -0.129820 \n+ 0.631523 \n+ -0.586538 \n+ 0.290720 \n \n
\n \n Use Styler.set_properties when the style doesn\u2019t actually depend on the values.
\n \n [24]:\n
\n@@ -2310,98 +2310,98 @@\n \n \n [24]:\n
\n \n \n A B C D E \n+ } A B C D E \n \n- 0 \n- 1.000000 \n- 1.329212 \n- nan \n- -0.316280 \n- -0.990810 \n- \n- \n- 1 \n- 2.000000 \n- -1.070816 \n- -1.438713 \n- 0.564417 \n- 0.295722 \n- \n- \n- 2 \n- 3.000000 \n- -1.626404 \n- 0.219565 \n- 0.678805 \n- 1.889273 \n- \n- \n- 3 \n- 4.000000 \n- 0.961538 \n- 0.104011 \n- nan \n- 0.850229 \n- \n- \n- 4 \n- 5.000000 \n- 1.453425 \n- 1.057737 \n- 0.165562 \n- 0.515018 \n- \n- \n- 5 \n- 6.000000 \n- -1.336936 \n- 0.562861 \n- 1.392855 \n- -0.063328 \n- \n- \n- 6 \n- 7.000000 \n- 0.121668 \n- 1.207603 \n- -0.002040 \n- 1.627796 \n- \n- \n- 7 \n- 8.000000 \n- 0.354493 \n- 1.037528 \n- -0.385684 \n- 0.519818 \n- \n- \n- 8 \n- 9.000000 \n- 1.686583 \n- -1.325963 \n- 1.428984 \n- -2.089354 \n- \n- \n- 9 \n- 10.000000 \n- -0.129820 \n- 0.631523 \n- -0.586538 \n- 0.290720 \n+ 0 \n+ 1.000000 \n+ 1.329212 \n+ nan \n+ -0.316280 \n+ -0.990810 \n+ \n+ \n+ 1 \n+ 2.000000 \n+ -1.070816 \n+ -1.438713 \n+ 0.564417 \n+ 0.295722 \n+ \n+ \n+ 2 \n+ 3.000000 \n+ -1.626404 \n+ 0.219565 \n+ 0.678805 \n+ 1.889273 \n+ \n+ \n+ 3 \n+ 4.000000 \n+ 0.961538 \n+ 0.104011 \n+ nan \n+ 0.850229 \n+ \n+ \n+ 4 \n+ 5.000000 \n+ 1.453425 \n+ 1.057737 \n+ 0.165562 \n+ 0.515018 \n+ \n+ \n+ 5 \n+ 6.000000 \n+ -1.336936 \n+ 0.562861 \n+ 1.392855 \n+ -0.063328 \n+ \n+ \n+ 6 \n+ 7.000000 \n+ 0.121668 \n+ 1.207603 \n+ -0.002040 \n+ 1.627796 \n+ \n+ \n+ 7 \n+ 8.000000 \n+ 0.354493 \n+ 1.037528 \n+ -0.385684 \n+ 0.519818 \n+ \n+ \n+ 8 \n+ 9.000000 \n+ 1.686583 \n+ -1.325963 \n+ 1.428984 \n+ -2.089354 \n+ \n+ \n+ 9 \n+ 10.000000 \n+ -0.129820 \n+ 0.631523 \n+ -0.586538 \n+ 0.290720 \n \n
\n \n \n Bar charts\u00b6
\n You can include \u201cbar charts\u201d in your DataFrame.
\n \n@@ -2415,165 +2415,165 @@\n \n \n [25]:\n
\n \n \n A B C D E \n+ } A B C D E \n \n- 0 \n- 1.000000 \n- 1.329212 \n- nan \n- -0.316280 \n- -0.990810 \n- \n- \n- 1 \n- 2.000000 \n- -1.070816 \n- -1.438713 \n- 0.564417 \n- 0.295722 \n- \n- \n- 2 \n- 3.000000 \n- -1.626404 \n- 0.219565 \n- 0.678805 \n- 1.889273 \n- \n- \n- 3 \n- 4.000000 \n- 0.961538 \n- 0.104011 \n- nan \n- 0.850229 \n- \n- \n- 4 \n- 5.000000 \n- 1.453425 \n- 1.057737 \n- 0.165562 \n- 0.515018 \n- \n- \n- 5 \n- 6.000000 \n- -1.336936 \n- 0.562861 \n- 1.392855 \n- -0.063328 \n- \n- \n- 6 \n- 7.000000 \n- 0.121668 \n- 1.207603 \n- -0.002040 \n- 1.627796 \n- \n- \n- 7 \n- 8.000000 \n- 0.354493 \n- 1.037528 \n- -0.385684 \n- 0.519818 \n- \n- \n- 8 \n- 9.000000 \n- 1.686583 \n- -1.325963 \n- 1.428984 \n- -2.089354 \n- \n- \n- 9 \n- 10.000000 \n- -0.129820 \n- 0.631523 \n- -0.586538 \n- 0.290720 \n+ 0 \n+ 1.000000 \n+ 1.329212 \n+ nan \n+ -0.316280 \n+ -0.990810 \n+ \n+ \n+ 1 \n+ 2.000000 \n+ -1.070816 \n+ -1.438713 \n+ 0.564417 \n+ 0.295722 \n+ \n+ \n+ 2 \n+ 3.000000 \n+ -1.626404 \n+ 0.219565 \n+ 0.678805 \n+ 1.889273 \n+ \n+ \n+ 3 \n+ 4.000000 \n+ 0.961538 \n+ 0.104011 \n+ nan \n+ 0.850229 \n+ \n+ \n+ 4 \n+ 5.000000 \n+ 1.453425 \n+ 1.057737 \n+ 0.165562 \n+ 0.515018 \n+ \n+ \n+ 5 \n+ 6.000000 \n+ -1.336936 \n+ 0.562861 \n+ 1.392855 \n+ -0.063328 \n+ \n+ \n+ 6 \n+ 7.000000 \n+ 0.121668 \n+ 1.207603 \n+ -0.002040 \n+ 1.627796 \n+ \n+ \n+ 7 \n+ 8.000000 \n+ 0.354493 \n+ 1.037528 \n+ -0.385684 \n+ 0.519818 \n+ \n+ \n+ 8 \n+ 9.000000 \n+ 1.686583 \n+ -1.325963 \n+ 1.428984 \n+ -2.089354 \n+ \n+ \n+ 9 \n+ 10.000000 \n+ -0.129820 \n+ 0.631523 \n+ -0.586538 \n+ 0.290720 \n \n
\n \n New in version 0.20.0 is the ability to customize further the bar chart: You can now have the df.style.bar be centered on zero or midpoint value (in addition to the already existing way of having the min value at the left side of the cell), and you can pass a list of [color_negative, color_positive].
\n Here\u2019s how you can change the above with the new align='mid' option:
\n \n [26]:\n@@ -2586,174 +2586,174 @@\n
\n \n [26]:\n
\n \n \n A B C D E \n+ } A B C D E \n \n- 0 \n- 1.000000 \n- 1.329212 \n- nan \n- -0.316280 \n- -0.990810 \n- \n- \n- 1 \n- 2.000000 \n- -1.070816 \n- -1.438713 \n- 0.564417 \n- 0.295722 \n- \n- \n- 2 \n- 3.000000 \n- -1.626404 \n- 0.219565 \n- 0.678805 \n- 1.889273 \n- \n- \n- 3 \n- 4.000000 \n- 0.961538 \n- 0.104011 \n- nan \n- 0.850229 \n- \n- \n- 4 \n- 5.000000 \n- 1.453425 \n- 1.057737 \n- 0.165562 \n- 0.515018 \n- \n- \n- 5 \n- 6.000000 \n- -1.336936 \n- 0.562861 \n- 1.392855 \n- -0.063328 \n- \n- \n- 6 \n- 7.000000 \n- 0.121668 \n- 1.207603 \n- -0.002040 \n- 1.627796 \n- \n- \n- 7 \n- 8.000000 \n- 0.354493 \n- 1.037528 \n- -0.385684 \n- 0.519818 \n- \n- \n- 8 \n- 9.000000 \n- 1.686583 \n- -1.325963 \n- 1.428984 \n- -2.089354 \n- \n- \n- 9 \n- 10.000000 \n- -0.129820 \n- 0.631523 \n- -0.586538 \n- 0.290720 \n+ 0 \n+ 1.000000 \n+ 1.329212 \n+ nan \n+ -0.316280 \n+ -0.990810 \n+ \n+ \n+ 1 \n+ 2.000000 \n+ -1.070816 \n+ -1.438713 \n+ 0.564417 \n+ 0.295722 \n+ \n+ \n+ 2 \n+ 3.000000 \n+ -1.626404 \n+ 0.219565 \n+ 0.678805 \n+ 1.889273 \n+ \n+ \n+ 3 \n+ 4.000000 \n+ 0.961538 \n+ 0.104011 \n+ nan \n+ 0.850229 \n+ \n+ \n+ 4 \n+ 5.000000 \n+ 1.453425 \n+ 1.057737 \n+ 0.165562 \n+ 0.515018 \n+ \n+ \n+ 5 \n+ 6.000000 \n+ -1.336936 \n+ 0.562861 \n+ 1.392855 \n+ -0.063328 \n+ \n+ \n+ 6 \n+ 7.000000 \n+ 0.121668 \n+ 1.207603 \n+ -0.002040 \n+ 1.627796 \n+ \n+ \n+ 7 \n+ 8.000000 \n+ 0.354493 \n+ 1.037528 \n+ -0.385684 \n+ 0.519818 \n+ \n+ \n+ 8 \n+ 9.000000 \n+ 1.686583 \n+ -1.325963 \n+ 1.428984 \n+ -2.089354 \n+ \n+ \n+ 9 \n+ 10.000000 \n+ -0.129820 \n+ 0.631523 \n+ -0.586538 \n+ 0.290720 \n \n
\n \n The following example aims to give a highlight of the behavior of the new align options:
\n \n [27]:\n
\n@@ -2811,313 +2811,313 @@\n All Negative \n All Positive \n Both Neg and Pos \n \n \n \n left \n+ } \n \n- 0 \n- -100 \n+ 0 \n+ -100 \n \n \n- 1 \n- -60 \n+ 1 \n+ -60 \n \n \n- 2 \n- -30 \n+ 2 \n+ -30 \n \n \n- 3 \n- -20 \n+ 3 \n+ -20 \n \n
\n+ } \n \n- 0 \n- 10 \n+ 0 \n+ 10 \n \n \n- 1 \n- 20 \n+ 1 \n+ 20 \n \n \n- 2 \n- 50 \n+ 2 \n+ 50 \n \n \n- 3 \n- 100 \n+ 3 \n+ 100 \n \n
\n+ } \n \n- 0 \n- -10 \n+ 0 \n+ -10 \n \n \n- 1 \n- -5 \n+ 1 \n+ -5 \n \n \n- 2 \n- 0 \n+ 2 \n+ 0 \n \n \n- 3 \n- 90 \n+ 3 \n+ 90 \n \n
zero \n+ } \n \n- 0 \n- -100 \n+ 0 \n+ -100 \n \n \n- 1 \n- -60 \n+ 1 \n+ -60 \n \n \n- 2 \n- -30 \n+ 2 \n+ -30 \n \n \n- 3 \n- -20 \n+ 3 \n+ -20 \n \n
\n+ } \n \n- 0 \n- 10 \n+ 0 \n+ 10 \n \n \n- 1 \n- 20 \n+ 1 \n+ 20 \n \n \n- 2 \n- 50 \n+ 2 \n+ 50 \n \n \n- 3 \n- 100 \n+ 3 \n+ 100 \n \n
\n+ } \n \n- 0 \n- -10 \n+ 0 \n+ -10 \n \n \n- 1 \n- -5 \n+ 1 \n+ -5 \n \n \n- 2 \n- 0 \n+ 2 \n+ 0 \n \n \n- 3 \n- 90 \n+ 3 \n+ 90 \n \n
mid \n+ } \n \n- 0 \n- -100 \n+ 0 \n+ -100 \n \n \n- 1 \n- -60 \n+ 1 \n+ -60 \n \n \n- 2 \n- -30 \n+ 2 \n+ -30 \n \n \n- 3 \n- -20 \n+ 3 \n+ -20 \n \n
\n+ } \n \n- 0 \n- 10 \n+ 0 \n+ 10 \n \n \n- 1 \n- 20 \n+ 1 \n+ 20 \n \n \n- 2 \n- 50 \n+ 2 \n+ 50 \n \n \n- 3 \n- 100 \n+ 3 \n+ 100 \n \n
\n+ } \n \n- 0 \n- -10 \n+ 0 \n+ -10 \n \n \n- 1 \n- -5 \n+ 1 \n+ -5 \n \n \n- 2 \n- 0 \n+ 2 \n+ 0 \n \n \n- 3 \n- 90 \n+ 3 \n+ 90 \n \n
\n \n
\n \n \n \n@@ -3137,98 +3137,98 @@\n \n \n [28]:\n
\n \n \n A B C D E \n+ } A B C D E \n \n- 0 \n- 1.000000 \n- 1.329212 \n- nan \n- -0.316280 \n- -0.990810 \n- \n- \n- 1 \n- 2.000000 \n- -1.070816 \n- -1.438713 \n- 0.564417 \n- 0.295722 \n- \n- \n- 2 \n- 3.000000 \n- -1.626404 \n- 0.219565 \n- 0.678805 \n- 1.889273 \n- \n- \n- 3 \n- 4.000000 \n- 0.961538 \n- 0.104011 \n- nan \n- 0.850229 \n- \n- \n- 4 \n- 5.000000 \n- 1.453425 \n- 1.057737 \n- 0.165562 \n- 0.515018 \n- \n- \n- 5 \n- 6.000000 \n- -1.336936 \n- 0.562861 \n- 1.392855 \n- -0.063328 \n- \n- \n- 6 \n- 7.000000 \n- 0.121668 \n- 1.207603 \n- -0.002040 \n- 1.627796 \n- \n- \n- 7 \n- 8.000000 \n- 0.354493 \n- 1.037528 \n- -0.385684 \n- 0.519818 \n- \n- \n- 8 \n- 9.000000 \n- 1.686583 \n- -1.325963 \n- 1.428984 \n- -2.089354 \n- \n- \n- 9 \n- 10.000000 \n- -0.129820 \n- 0.631523 \n- -0.586538 \n- 0.290720 \n+ 0 \n+ 1.000000 \n+ 1.329212 \n+ nan \n+ -0.316280 \n+ -0.990810 \n+ \n+ \n+ 1 \n+ 2.000000 \n+ -1.070816 \n+ -1.438713 \n+ 0.564417 \n+ 0.295722 \n+ \n+ \n+ 2 \n+ 3.000000 \n+ -1.626404 \n+ 0.219565 \n+ 0.678805 \n+ 1.889273 \n+ \n+ \n+ 3 \n+ 4.000000 \n+ 0.961538 \n+ 0.104011 \n+ nan \n+ 0.850229 \n+ \n+ \n+ 4 \n+ 5.000000 \n+ 1.453425 \n+ 1.057737 \n+ 0.165562 \n+ 0.515018 \n+ \n+ \n+ 5 \n+ 6.000000 \n+ -1.336936 \n+ 0.562861 \n+ 1.392855 \n+ -0.063328 \n+ \n+ \n+ 6 \n+ 7.000000 \n+ 0.121668 \n+ 1.207603 \n+ -0.002040 \n+ 1.627796 \n+ \n+ \n+ 7 \n+ 8.000000 \n+ 0.354493 \n+ 1.037528 \n+ -0.385684 \n+ 0.519818 \n+ \n+ \n+ 8 \n+ 9.000000 \n+ 1.686583 \n+ -1.325963 \n+ 1.428984 \n+ -2.089354 \n+ \n+ \n+ 9 \n+ 10.000000 \n+ -0.129820 \n+ 0.631523 \n+ -0.586538 \n+ 0.290720 \n \n
\n \n \n [29]:\n
\n \n@@ -3241,98 +3241,98 @@\n \n \n [29]:\n
\n \n \n A B C D E \n+ } A B C D E \n \n- 0 \n- -1.000000 \n- -1.329212 \n- nan \n- 0.316280 \n- 0.990810 \n- \n- \n- 1 \n- -2.000000 \n- 1.070816 \n- 1.438713 \n- -0.564417 \n- -0.295722 \n- \n- \n- 2 \n- -3.000000 \n- 1.626404 \n- -0.219565 \n- -0.678805 \n- -1.889273 \n- \n- \n- 3 \n- -4.000000 \n- -0.961538 \n- -0.104011 \n- nan \n- -0.850229 \n- \n- \n- 4 \n- -5.000000 \n- -1.453425 \n- -1.057737 \n- -0.165562 \n- -0.515018 \n- \n- \n- 5 \n- -6.000000 \n- 1.336936 \n- -0.562861 \n- -1.392855 \n- 0.063328 \n- \n- \n- 6 \n- -7.000000 \n- -0.121668 \n- -1.207603 \n- 0.002040 \n- -1.627796 \n- \n- \n- 7 \n- -8.000000 \n- -0.354493 \n- -1.037528 \n- 0.385684 \n- -0.519818 \n- \n- \n- 8 \n- -9.000000 \n- -1.686583 \n- 1.325963 \n- -1.428984 \n- 2.089354 \n- \n- \n- 9 \n- -10.000000 \n- 0.129820 \n- -0.631523 \n- 0.586538 \n- -0.290720 \n+ 0 \n+ -1.000000 \n+ -1.329212 \n+ nan \n+ 0.316280 \n+ 0.990810 \n+ \n+ \n+ 1 \n+ -2.000000 \n+ 1.070816 \n+ 1.438713 \n+ -0.564417 \n+ -0.295722 \n+ \n+ \n+ 2 \n+ -3.000000 \n+ 1.626404 \n+ -0.219565 \n+ -0.678805 \n+ -1.889273 \n+ \n+ \n+ 3 \n+ -4.000000 \n+ -0.961538 \n+ -0.104011 \n+ nan \n+ -0.850229 \n+ \n+ \n+ 4 \n+ -5.000000 \n+ -1.453425 \n+ -1.057737 \n+ -0.165562 \n+ -0.515018 \n+ \n+ \n+ 5 \n+ -6.000000 \n+ 1.336936 \n+ -0.562861 \n+ -1.392855 \n+ 0.063328 \n+ \n+ \n+ 6 \n+ -7.000000 \n+ -0.121668 \n+ -1.207603 \n+ 0.002040 \n+ -1.627796 \n+ \n+ \n+ 7 \n+ -8.000000 \n+ -0.354493 \n+ -1.037528 \n+ 0.385684 \n+ -0.519818 \n+ \n+ \n+ 8 \n+ -9.000000 \n+ -1.686583 \n+ 1.325963 \n+ -1.428984 \n+ 2.089354 \n+ \n+ \n+ 9 \n+ -10.000000 \n+ 0.129820 \n+ -0.631523 \n+ 0.586538 \n+ -0.290720 \n \n
\n \n Notice that you\u2019re able to share the styles even though they\u2019re data aware. The styles are re-evaluated on the new DataFrame they\u2019ve been used upon.
\n \n \n Other Options\u00b6
\n@@ -3368,101 +3368,101 @@\n \n \n [30]:\n
\n \n \n A B C D E \n+ } A B C D E \n \n- 0 \n- 1.00 \n- 1.33 \n- nan \n- -0.32 \n- -0.99 \n- \n- \n- 1 \n- 2.00 \n- -1.07 \n- -1.44 \n- 0.56 \n- 0.30 \n- \n- \n- 2 \n- 3.00 \n- -1.63 \n- 0.22 \n- 0.68 \n- 1.89 \n- \n- \n- 3 \n- 4.00 \n- 0.96 \n- 0.10 \n- nan \n- 0.85 \n- \n- \n- 4 \n- 5.00 \n- 1.45 \n- 1.06 \n- 0.17 \n- 0.52 \n- \n- \n- 5 \n- 6.00 \n- -1.34 \n- 0.56 \n- 1.39 \n- -0.06 \n- \n- \n- 6 \n- 7.00 \n- 0.12 \n- 1.21 \n- -0.00 \n- 1.63 \n- \n- \n- 7 \n- 8.00 \n- 0.35 \n- 1.04 \n- -0.39 \n- 0.52 \n- \n- \n- 8 \n- 9.00 \n- 1.69 \n- -1.33 \n- 1.43 \n- -2.09 \n- \n- \n- 9 \n- 10.00 \n- -0.13 \n- 0.63 \n- -0.59 \n- 0.29 \n+ 0 \n+ 1.00 \n+ 1.33 \n+ nan \n+ -0.32 \n+ -0.99 \n+ \n+ \n+ 1 \n+ 2.00 \n+ -1.07 \n+ -1.44 \n+ 0.56 \n+ 0.30 \n+ \n+ \n+ 2 \n+ 3.00 \n+ -1.63 \n+ 0.22 \n+ 0.68 \n+ 1.89 \n+ \n+ \n+ 3 \n+ 4.00 \n+ 0.96 \n+ 0.10 \n+ nan \n+ 0.85 \n+ \n+ \n+ 4 \n+ 5.00 \n+ 1.45 \n+ 1.06 \n+ 0.17 \n+ 0.52 \n+ \n+ \n+ 5 \n+ 6.00 \n+ -1.34 \n+ 0.56 \n+ 1.39 \n+ -0.06 \n+ \n+ \n+ 6 \n+ 7.00 \n+ 0.12 \n+ 1.21 \n+ -0.00 \n+ 1.63 \n+ \n+ \n+ 7 \n+ 8.00 \n+ 0.35 \n+ 1.04 \n+ -0.39 \n+ 0.52 \n+ \n+ \n+ 8 \n+ 9.00 \n+ 1.69 \n+ -1.33 \n+ 1.43 \n+ -2.09 \n+ \n+ \n+ 9 \n+ 10.00 \n+ -0.13 \n+ 0.63 \n+ -0.59 \n+ 0.29 \n \n
\n \n Or through a set_precision method.
\n \n [31]:\n
\n@@ -3477,101 +3477,101 @@\n \n \n [31]:\n
\n \n \n A B C D E \n+ } A B C D E \n \n- 0 \n- 1.00 \n- 1.33 \n- nan \n- -0.32 \n- -0.99 \n- \n- \n- 1 \n- 2.00 \n- -1.07 \n- -1.44 \n- 0.56 \n- 0.30 \n- \n- \n- 2 \n- 3.00 \n- -1.63 \n- 0.22 \n- 0.68 \n- 1.89 \n- \n- \n- 3 \n- 4.00 \n- 0.96 \n- 0.10 \n- nan \n- 0.85 \n- \n- \n- 4 \n- 5.00 \n- 1.45 \n- 1.06 \n- 0.17 \n- 0.52 \n- \n- \n- 5 \n- 6.00 \n- -1.34 \n- 0.56 \n- 1.39 \n- -0.06 \n- \n- \n- 6 \n- 7.00 \n- 0.12 \n- 1.21 \n- -0.00 \n- 1.63 \n- \n- \n- 7 \n- 8.00 \n- 0.35 \n- 1.04 \n- -0.39 \n- 0.52 \n- \n- \n- 8 \n- 9.00 \n- 1.69 \n- -1.33 \n- 1.43 \n- -2.09 \n- \n- \n- 9 \n- 10.00 \n- -0.13 \n- 0.63 \n- -0.59 \n- 0.29 \n+ 0 \n+ 1.00 \n+ 1.33 \n+ nan \n+ -0.32 \n+ -0.99 \n+ \n+ \n+ 1 \n+ 2.00 \n+ -1.07 \n+ -1.44 \n+ 0.56 \n+ 0.30 \n+ \n+ \n+ 2 \n+ 3.00 \n+ -1.63 \n+ 0.22 \n+ 0.68 \n+ 1.89 \n+ \n+ \n+ 3 \n+ 4.00 \n+ 0.96 \n+ 0.10 \n+ nan \n+ 0.85 \n+ \n+ \n+ 4 \n+ 5.00 \n+ 1.45 \n+ 1.06 \n+ 0.17 \n+ 0.52 \n+ \n+ \n+ 5 \n+ 6.00 \n+ -1.34 \n+ 0.56 \n+ 1.39 \n+ -0.06 \n+ \n+ \n+ 6 \n+ 7.00 \n+ 0.12 \n+ 1.21 \n+ -0.00 \n+ 1.63 \n+ \n+ \n+ 7 \n+ 8.00 \n+ 0.35 \n+ 1.04 \n+ -0.39 \n+ 0.52 \n+ \n+ \n+ 8 \n+ 9.00 \n+ 1.69 \n+ -1.33 \n+ 1.43 \n+ -2.09 \n+ \n+ \n+ 9 \n+ 10.00 \n+ -0.13 \n+ 0.63 \n+ -0.59 \n+ 0.29 \n \n
\n \n Setting the precision only affects the printed number; the full-precision values are always passed to your style functions. You can always use df.round(2).style if you\u2019d prefer to round from the start.
\n \n \n Captions\u00b6
\n@@ -3588,199 +3588,199 @@\n \n \n [32]:\n
\n \n \n Colormaps, with a caption. A B C D E \n+ }Colormaps, with a caption. A B C D E \n \n- 0 \n- 1.000000 \n- 1.329212 \n- nan \n- -0.316280 \n- -0.990810 \n- \n- \n- 1 \n- 2.000000 \n- -1.070816 \n- -1.438713 \n- 0.564417 \n- 0.295722 \n- \n- \n- 2 \n- 3.000000 \n- -1.626404 \n- 0.219565 \n- 0.678805 \n- 1.889273 \n- \n- \n- 3 \n- 4.000000 \n- 0.961538 \n- 0.104011 \n- nan \n- 0.850229 \n- \n- \n- 4 \n- 5.000000 \n- 1.453425 \n- 1.057737 \n- 0.165562 \n- 0.515018 \n- \n- \n- 5 \n- 6.000000 \n- -1.336936 \n- 0.562861 \n- 1.392855 \n- -0.063328 \n- \n- \n- 6 \n- 7.000000 \n- 0.121668 \n- 1.207603 \n- -0.002040 \n- 1.627796 \n- \n- \n- 7 \n- 8.000000 \n- 0.354493 \n- 1.037528 \n- -0.385684 \n- 0.519818 \n- \n- \n- 8 \n- 9.000000 \n- 1.686583 \n- -1.325963 \n- 1.428984 \n- -2.089354 \n- \n- \n- 9 \n- 10.000000 \n- -0.129820 \n- 0.631523 \n- -0.586538 \n- 0.290720 \n+ 0 \n+ 1.000000 \n+ 1.329212 \n+ nan \n+ -0.316280 \n+ -0.990810 \n+ \n+ \n+ 1 \n+ 2.000000 \n+ -1.070816 \n+ -1.438713 \n+ 0.564417 \n+ 0.295722 \n+ \n+ \n+ 2 \n+ 3.000000 \n+ -1.626404 \n+ 0.219565 \n+ 0.678805 \n+ 1.889273 \n+ \n+ \n+ 3 \n+ 4.000000 \n+ 0.961538 \n+ 0.104011 \n+ nan \n+ 0.850229 \n+ \n+ \n+ 4 \n+ 5.000000 \n+ 1.453425 \n+ 1.057737 \n+ 0.165562 \n+ 0.515018 \n+ \n+ \n+ 5 \n+ 6.000000 \n+ -1.336936 \n+ 0.562861 \n+ 1.392855 \n+ -0.063328 \n+ \n+ \n+ 6 \n+ 7.000000 \n+ 0.121668 \n+ 1.207603 \n+ -0.002040 \n+ 1.627796 \n+ \n+ \n+ 7 \n+ 8.000000 \n+ 0.354493 \n+ 1.037528 \n+ -0.385684 \n+ 0.519818 \n+ \n+ \n+ 8 \n+ 9.000000 \n+ 1.686583 \n+ -1.325963 \n+ 1.428984 \n+ -2.089354 \n+ \n+ \n+ 9 \n+ 10.000000 \n+ -0.129820 \n+ 0.631523 \n+ -0.586538 \n+ 0.290720 \n \n
\n \n \n \n Table styles\u00b6
\n The next option you have are \u201ctable styles\u201d. These are styles that apply to the table as a whole, but don\u2019t look at the data. Certain sytlings, including pseudo-selectors like :hover can only be used this way.
\n@@ -3809,101 +3809,101 @@\n \n \n [33]:\n
\n \n \n Hover to highlight. A B C D E \n+ }Hover to highlight. A B C D E \n \n- 0 \n- 1.000000 \n- 1.329212 \n- nan \n- -0.316280 \n- -0.990810 \n- \n- \n- 1 \n- 2.000000 \n- -1.070816 \n- -1.438713 \n- 0.564417 \n- 0.295722 \n- \n- \n- 2 \n- 3.000000 \n- -1.626404 \n- 0.219565 \n- 0.678805 \n- 1.889273 \n- \n- \n- 3 \n- 4.000000 \n- 0.961538 \n- 0.104011 \n- nan \n- 0.850229 \n- \n- \n- 4 \n- 5.000000 \n- 1.453425 \n- 1.057737 \n- 0.165562 \n- 0.515018 \n- \n- \n- 5 \n- 6.000000 \n- -1.336936 \n- 0.562861 \n- 1.392855 \n- -0.063328 \n- \n- \n- 6 \n- 7.000000 \n- 0.121668 \n- 1.207603 \n- -0.002040 \n- 1.627796 \n- \n- \n- 7 \n- 8.000000 \n- 0.354493 \n- 1.037528 \n- -0.385684 \n- 0.519818 \n- \n- \n- 8 \n- 9.000000 \n- 1.686583 \n- -1.325963 \n- 1.428984 \n- -2.089354 \n- \n- \n- 9 \n- 10.000000 \n- -0.129820 \n- 0.631523 \n- -0.586538 \n- 0.290720 \n+ 0 \n+ 1.000000 \n+ 1.329212 \n+ nan \n+ -0.316280 \n+ -0.990810 \n+ \n+ \n+ 1 \n+ 2.000000 \n+ -1.070816 \n+ -1.438713 \n+ 0.564417 \n+ 0.295722 \n+ \n+ \n+ 2 \n+ 3.000000 \n+ -1.626404 \n+ 0.219565 \n+ 0.678805 \n+ 1.889273 \n+ \n+ \n+ 3 \n+ 4.000000 \n+ 0.961538 \n+ 0.104011 \n+ nan \n+ 0.850229 \n+ \n+ \n+ 4 \n+ 5.000000 \n+ 1.453425 \n+ 1.057737 \n+ 0.165562 \n+ 0.515018 \n+ \n+ \n+ 5 \n+ 6.000000 \n+ -1.336936 \n+ 0.562861 \n+ 1.392855 \n+ -0.063328 \n+ \n+ \n+ 6 \n+ 7.000000 \n+ 0.121668 \n+ 1.207603 \n+ -0.002040 \n+ 1.627796 \n+ \n+ \n+ 7 \n+ 8.000000 \n+ 0.354493 \n+ 1.037528 \n+ -0.385684 \n+ 0.519818 \n+ \n+ \n+ 8 \n+ 9.000000 \n+ 1.686583 \n+ -1.325963 \n+ 1.428984 \n+ -2.089354 \n+ \n+ \n+ 9 \n+ 10.000000 \n+ -0.129820 \n+ 0.631523 \n+ -0.586538 \n+ 0.290720 \n \n
\n \n table_styles should be a list of dictionaries. Each dictionary should have the selector and props keys. The value for selector should be a valid CSS selector. Recall that all the styles are already attached to an id, unique to each Styler. This selector is in addition to that id. The value for props should be a list of tuples of ('attribute', 'value').
\n table_styles are extremely flexible, but not as fun to type out by hand. We hope to collect some useful ones either in pandas, or preferable in a new package that builds on top the tools here.
\n \n \n@@ -3923,96 +3923,96 @@\n \n \n [34]:\n
\n \n \n A B C D E \n+ } A B C D E \n \n- 0 \n- 1.000000 \n- 1.329212 \n- FAIL \n- -0.316280 \n- -0.990810 \n- \n- \n- 1 \n- 2.000000 \n- -1.070816 \n- -1.438713 \n- 0.564417 \n- 0.295722 \n- \n- \n- 2 \n- 3.000000 \n- -1.626404 \n- 0.219565 \n- 0.678805 \n- 1.889273 \n- \n- \n- 3 \n- 4.000000 \n- 0.961538 \n- 0.104011 \n- PASS \n- 0.850229 \n- \n- \n- 4 \n- 5.000000 \n- 1.453425 \n- 1.057737 \n- 0.165562 \n- 0.515018 \n- \n- \n- 5 \n- 6.000000 \n- -1.336936 \n- 0.562861 \n- 1.392855 \n- -0.063328 \n- \n- \n- 6 \n- 7.000000 \n- 0.121668 \n- 1.207603 \n- -0.002040 \n- 1.627796 \n- \n- \n- 7 \n- 8.000000 \n- 0.354493 \n- 1.037528 \n- -0.385684 \n- 0.519818 \n- \n- \n- 8 \n- 9.000000 \n- 1.686583 \n- -1.325963 \n- 1.428984 \n- -2.089354 \n- \n- \n- 9 \n- 10.000000 \n- -0.129820 \n- 0.631523 \n- -0.586538 \n- 0.290720 \n+ 0 \n+ 1.000000 \n+ 1.329212 \n+ FAIL \n+ -0.316280 \n+ -0.990810 \n+ \n+ \n+ 1 \n+ 2.000000 \n+ -1.070816 \n+ -1.438713 \n+ 0.564417 \n+ 0.295722 \n+ \n+ \n+ 2 \n+ 3.000000 \n+ -1.626404 \n+ 0.219565 \n+ 0.678805 \n+ 1.889273 \n+ \n+ \n+ 3 \n+ 4.000000 \n+ 0.961538 \n+ 0.104011 \n+ PASS \n+ 0.850229 \n+ \n+ \n+ 4 \n+ 5.000000 \n+ 1.453425 \n+ 1.057737 \n+ 0.165562 \n+ 0.515018 \n+ \n+ \n+ 5 \n+ 6.000000 \n+ -1.336936 \n+ 0.562861 \n+ 1.392855 \n+ -0.063328 \n+ \n+ \n+ 6 \n+ 7.000000 \n+ 0.121668 \n+ 1.207603 \n+ -0.002040 \n+ 1.627796 \n+ \n+ \n+ 7 \n+ 8.000000 \n+ 0.354493 \n+ 1.037528 \n+ -0.385684 \n+ 0.519818 \n+ \n+ \n+ 8 \n+ 9.000000 \n+ 1.686583 \n+ -1.325963 \n+ 1.428984 \n+ -2.089354 \n+ \n+ \n+ 9 \n+ 10.000000 \n+ -0.129820 \n+ 0.631523 \n+ -0.586538 \n+ 0.290720 \n \n
\n \n \n \n Hiding the Index or Columns\u00b6
\n The index can be hidden from rendering by calling Styler.hide_index. Columns can be hidden from rendering by calling Styler.hide_columns and passing in the name of a column, or a slice of columns.
\n@@ -4027,84 +4027,84 @@\n \n \n [35]:\n
\n \n \n A B C D E \n+ A B C D E \n \n- 1.000000 \n- 1.329212 \n- nan \n- -0.316280 \n- -0.990810 \n+ 1.000000 \n+ 1.329212 \n+ nan \n+ -0.316280 \n+ -0.990810 \n \n \n- 2.000000 \n- -1.070816 \n- -1.438713 \n- 0.564417 \n- 0.295722 \n+ 2.000000 \n+ -1.070816 \n+ -1.438713 \n+ 0.564417 \n+ 0.295722 \n \n \n- 3.000000 \n- -1.626404 \n- 0.219565 \n- 0.678805 \n- 1.889273 \n+ 3.000000 \n+ -1.626404 \n+ 0.219565 \n+ 0.678805 \n+ 1.889273 \n \n \n- 4.000000 \n- 0.961538 \n- 0.104011 \n- nan \n- 0.850229 \n+ 4.000000 \n+ 0.961538 \n+ 0.104011 \n+ nan \n+ 0.850229 \n \n \n- 5.000000 \n- 1.453425 \n- 1.057737 \n- 0.165562 \n- 0.515018 \n+ 5.000000 \n+ 1.453425 \n+ 1.057737 \n+ 0.165562 \n+ 0.515018 \n \n \n- 6.000000 \n- -1.336936 \n- 0.562861 \n- 1.392855 \n- -0.063328 \n+ 6.000000 \n+ -1.336936 \n+ 0.562861 \n+ 1.392855 \n+ -0.063328 \n \n \n- 7.000000 \n- 0.121668 \n- 1.207603 \n- -0.002040 \n- 1.627796 \n+ 7.000000 \n+ 0.121668 \n+ 1.207603 \n+ -0.002040 \n+ 1.627796 \n \n \n- 8.000000 \n- 0.354493 \n- 1.037528 \n- -0.385684 \n- 0.519818 \n+ 8.000000 \n+ 0.354493 \n+ 1.037528 \n+ -0.385684 \n+ 0.519818 \n \n \n- 9.000000 \n- 1.686583 \n- -1.325963 \n- 1.428984 \n- -2.089354 \n+ 9.000000 \n+ 1.686583 \n+ -1.325963 \n+ 1.428984 \n+ -2.089354 \n \n \n- 10.000000 \n- -0.129820 \n- 0.631523 \n- -0.586538 \n- 0.290720 \n+ 10.000000 \n+ -0.129820 \n+ 0.631523 \n+ -0.586538 \n+ 0.290720 \n \n
\n \n \n [36]:\n
\n \n@@ -4115,74 +4115,74 @@\n \n \n [36]:\n
\n \n \n A B E \n+ A B E \n \n- 0 \n- 1.000000 \n- 1.329212 \n- -0.990810 \n+ 0 \n+ 1.000000 \n+ 1.329212 \n+ -0.990810 \n \n \n- 1 \n- 2.000000 \n- -1.070816 \n- 0.295722 \n+ 1 \n+ 2.000000 \n+ -1.070816 \n+ 0.295722 \n \n \n- 2 \n- 3.000000 \n- -1.626404 \n- 1.889273 \n+ 2 \n+ 3.000000 \n+ -1.626404 \n+ 1.889273 \n \n \n- 3 \n- 4.000000 \n- 0.961538 \n- 0.850229 \n+ 3 \n+ 4.000000 \n+ 0.961538 \n+ 0.850229 \n \n \n- 4 \n- 5.000000 \n- 1.453425 \n- 0.515018 \n+ 4 \n+ 5.000000 \n+ 1.453425 \n+ 0.515018 \n \n \n- 5 \n- 6.000000 \n- -1.336936 \n- -0.063328 \n+ 5 \n+ 6.000000 \n+ -1.336936 \n+ -0.063328 \n \n \n- 6 \n- 7.000000 \n- 0.121668 \n- 1.627796 \n+ 6 \n+ 7.000000 \n+ 0.121668 \n+ 1.627796 \n \n \n- 7 \n- 8.000000 \n- 0.354493 \n- 0.519818 \n+ 7 \n+ 8.000000 \n+ 0.354493 \n+ 0.519818 \n \n \n- 8 \n- 9.000000 \n- 1.686583 \n- -2.089354 \n+ 8 \n+ 9.000000 \n+ 1.686583 \n+ -2.089354 \n \n \n- 9 \n- 10.000000 \n- -0.129820 \n- 0.290720 \n+ 9 \n+ 10.000000 \n+ -0.129820 \n+ 0.290720 \n \n
\n \n \n \n CSS classes\u00b6
\n Certain CSS classes are attached to cells.
\n@@ -4246,181 +4246,181 @@\n \n \n \n \n \n \n A B C D E \n+ } A B C D E \n \n- 0 \n- 1.000000 \n- 1.329212 \n- nan \n- -0.316280 \n- -0.990810 \n- \n- \n- 1 \n- 2.000000 \n- -1.070816 \n- -1.438713 \n- 0.564417 \n- 0.295722 \n- \n- \n- 2 \n- 3.000000 \n- -1.626404 \n- 0.219565 \n- 0.678805 \n- 1.889273 \n- \n- \n- 3 \n- 4.000000 \n- 0.961538 \n- 0.104011 \n- nan \n- 0.850229 \n- \n- \n- 4 \n- 5.000000 \n- 1.453425 \n- 1.057737 \n- 0.165562 \n- 0.515018 \n- \n- \n- 5 \n- 6.000000 \n- -1.336936 \n- 0.562861 \n- 1.392855 \n- -0.063328 \n- \n- \n- 6 \n- 7.000000 \n- 0.121668 \n- 1.207603 \n- -0.002040 \n- 1.627796 \n- \n- \n- 7 \n- 8.000000 \n- 0.354493 \n- 1.037528 \n- -0.385684 \n- 0.519818 \n- \n- \n- 8 \n- 9.000000 \n- 1.686583 \n- -1.325963 \n- 1.428984 \n- -2.089354 \n- \n- \n- 9 \n- 10.000000 \n- -0.129820 \n- 0.631523 \n- -0.586538 \n- 0.290720 \n+ 0 \n+ 1.000000 \n+ 1.329212 \n+ nan \n+ -0.316280 \n+ -0.990810 \n+ \n+ \n+ 1 \n+ 2.000000 \n+ -1.070816 \n+ -1.438713 \n+ 0.564417 \n+ 0.295722 \n+ \n+ \n+ 2 \n+ 3.000000 \n+ -1.626404 \n+ 0.219565 \n+ 0.678805 \n+ 1.889273 \n+ \n+ \n+ 3 \n+ 4.000000 \n+ 0.961538 \n+ 0.104011 \n+ nan \n+ 0.850229 \n+ \n+ \n+ 4 \n+ 5.000000 \n+ 1.453425 \n+ 1.057737 \n+ 0.165562 \n+ 0.515018 \n+ \n+ \n+ 5 \n+ 6.000000 \n+ -1.336936 \n+ 0.562861 \n+ 1.392855 \n+ -0.063328 \n+ \n+ \n+ 6 \n+ 7.000000 \n+ 0.121668 \n+ 1.207603 \n+ -0.002040 \n+ 1.627796 \n+ \n+ \n+ 7 \n+ 8.000000 \n+ 0.354493 \n+ 1.037528 \n+ -0.385684 \n+ 0.519818 \n+ \n+ \n+ 8 \n+ 9.000000 \n+ 1.686583 \n+ -1.325963 \n+ 1.428984 \n+ -2.089354 \n+ \n+ \n+ 9 \n+ 10.000000 \n+ -0.129820 \n+ 0.631523 \n+ -0.586538 \n+ 0.290720 \n \n
\n \n \n [38]:\n
\n \n@@ -4458,1583 +4458,1583 @@\n \n \n [39]:\n
\n \n \n Hover to magnify 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 \n+ }Hover to magnify 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 \n \n- 0 \n- 0.23 \n- 1.03 \n- -0.84 \n- -0.59 \n- -0.96 \n- -0.22 \n- -0.62 \n- 1.84 \n- -2.05 \n- 0.87 \n- -0.92 \n- -0.23 \n- 2.15 \n- -1.33 \n- 0.08 \n- -1.25 \n- 1.20 \n- -1.05 \n- 1.06 \n- -0.42 \n- 2.29 \n- -2.59 \n- 2.82 \n- 0.68 \n- -1.58 \n- \n- \n- 1 \n- -1.75 \n- 1.56 \n- -1.13 \n- -1.10 \n- 1.03 \n- 0.00 \n- -2.46 \n- 3.45 \n- -1.66 \n- 1.27 \n- -0.52 \n- -0.02 \n- 1.52 \n- -1.09 \n- -1.86 \n- -1.13 \n- -0.68 \n- -0.81 \n- 0.35 \n- -0.06 \n- 1.79 \n- -2.82 \n- 2.26 \n- 0.78 \n- 0.44 \n- \n- \n- 2 \n- -0.65 \n- 3.22 \n- -1.76 \n- 0.52 \n- 2.20 \n- -0.37 \n- -3.00 \n- 3.73 \n- -1.87 \n- 2.46 \n- 0.21 \n- -0.24 \n- -0.10 \n- -0.78 \n- -3.02 \n- -0.82 \n- -0.21 \n- -0.23 \n- 0.86 \n- -0.68 \n- 1.45 \n- -4.89 \n- 3.03 \n- 1.91 \n- 0.61 \n- \n- \n- 3 \n- -1.62 \n- 3.71 \n- -2.31 \n- 0.43 \n- 4.17 \n- -0.43 \n- -3.86 \n- 4.16 \n- -2.15 \n- 1.08 \n- 0.12 \n- 0.60 \n- -0.89 \n- 0.27 \n- -3.67 \n- -2.71 \n- -0.31 \n- -1.59 \n- 1.35 \n- -1.83 \n- 0.91 \n- -5.80 \n- 2.81 \n- 2.11 \n- 0.28 \n- \n- \n- 4 \n- -3.35 \n- 4.48 \n- -1.86 \n- -1.70 \n- 5.19 \n- -1.02 \n- -3.81 \n- 4.72 \n- -0.72 \n- 1.08 \n- -0.18 \n- 0.83 \n- -0.22 \n- -1.08 \n- -4.27 \n- -2.88 \n- -0.97 \n- -1.78 \n- 1.53 \n- -1.80 \n- 2.21 \n- -6.34 \n- 3.34 \n- 2.49 \n- 2.09 \n- \n- \n- 5 \n- -0.84 \n- 4.23 \n- -1.65 \n- -2.00 \n- 5.34 \n- -0.99 \n- -4.13 \n- 3.94 \n- -1.06 \n- -0.94 \n- 1.24 \n- 0.09 \n- -1.78 \n- -0.11 \n- -4.45 \n- -0.85 \n- -2.06 \n- -1.35 \n- 0.80 \n- -1.63 \n- 1.54 \n- -6.51 \n- 2.80 \n- 2.14 \n- 3.77 \n- \n- \n- 6 \n- -0.74 \n- 5.35 \n- -2.11 \n- -1.13 \n- 4.20 \n- -1.85 \n- -3.20 \n- 3.76 \n- -3.22 \n- -1.23 \n- 0.34 \n- 0.57 \n- -1.82 \n- 0.54 \n- -4.43 \n- -1.83 \n- -4.03 \n- -2.62 \n- -0.20 \n- -4.68 \n- 1.93 \n- -8.46 \n- 3.34 \n- 2.52 \n- 5.81 \n- \n- \n- 7 \n- -0.44 \n- 4.69 \n- -2.30 \n- -0.21 \n- 5.93 \n- -2.63 \n- -1.83 \n- 5.46 \n- -4.50 \n- -3.16 \n- -1.73 \n- 0.18 \n- 0.11 \n- 0.04 \n- -5.99 \n- -0.45 \n- -6.20 \n- -3.89 \n- 0.71 \n- -3.95 \n- 0.67 \n- -7.26 \n- 2.97 \n- 3.39 \n- 6.66 \n- \n- \n- 8 \n- 0.92 \n- 5.80 \n- -3.33 \n- -0.65 \n- 5.99 \n- -3.19 \n- -1.83 \n- 5.63 \n- -3.53 \n- -1.30 \n- -1.61 \n- 0.82 \n- -2.45 \n- -0.40 \n- -6.06 \n- -0.52 \n- -6.60 \n- -3.48 \n- -0.04 \n- -4.60 \n- 0.51 \n- -5.85 \n- 3.23 \n- 2.40 \n- 5.08 \n- \n- \n- 9 \n- 0.38 \n- 5.54 \n- -4.49 \n- -0.80 \n- 7.05 \n- -2.64 \n- -0.44 \n- 5.35 \n- -1.96 \n- -0.33 \n- -0.80 \n- 0.26 \n- -3.37 \n- -0.82 \n- -6.05 \n- -2.61 \n- -8.45 \n- -4.45 \n- 0.41 \n- -4.71 \n- 1.89 \n- -6.93 \n- 2.14 \n- 3.00 \n- 5.16 \n- \n- \n- 10 \n- 2.06 \n- 5.84 \n- -3.90 \n- -0.98 \n- 7.78 \n- -2.49 \n- -0.59 \n- 5.59 \n- -2.22 \n- -0.71 \n- -0.46 \n- 1.80 \n- -2.79 \n- 0.48 \n- -5.97 \n- -3.44 \n- -7.77 \n- -5.49 \n- -0.70 \n- -4.61 \n- -0.52 \n- -7.72 \n- 1.54 \n- 5.02 \n- 5.81 \n- \n- \n- 11 \n- 1.86 \n- 4.47 \n- -2.17 \n- -1.38 \n- 5.90 \n- -0.49 \n- 0.02 \n- 5.78 \n- -1.04 \n- -0.60 \n- 0.49 \n- 1.96 \n- -1.47 \n- 1.88 \n- -5.92 \n- -4.55 \n- -8.15 \n- -3.42 \n- -2.24 \n- -4.33 \n- -1.17 \n- -7.90 \n- 1.36 \n- 5.31 \n- 5.83 \n- \n- \n- 12 \n- 3.19 \n- 4.22 \n- -3.06 \n- -2.27 \n- 5.93 \n- -2.64 \n- 0.33 \n- 6.72 \n- -2.84 \n- -0.20 \n- 1.89 \n- 2.63 \n- -1.53 \n- 0.75 \n- -5.27 \n- -4.53 \n- -7.57 \n- -2.85 \n- -2.17 \n- -4.78 \n- -1.13 \n- -8.99 \n- 2.11 \n- 6.42 \n- 5.60 \n- \n- \n- 13 \n- 2.31 \n- 4.45 \n- -3.87 \n- -2.05 \n- 6.76 \n- -3.25 \n- -2.17 \n- 7.99 \n- -2.56 \n- -0.80 \n- 0.71 \n- 2.33 \n- -0.16 \n- -0.46 \n- -5.10 \n- -3.79 \n- -7.58 \n- -4.00 \n- 0.33 \n- -3.67 \n- -1.05 \n- -8.71 \n- 2.47 \n- 5.87 \n- 6.71 \n- \n- \n- 14 \n- 3.78 \n- 4.33 \n- -3.88 \n- -1.58 \n- 6.22 \n- -3.23 \n- -1.46 \n- 5.57 \n- -2.93 \n- -0.33 \n- -0.97 \n- 1.72 \n- 3.61 \n- 0.29 \n- -4.21 \n- -4.10 \n- -6.68 \n- -4.50 \n- -2.19 \n- -2.43 \n- -1.64 \n- -9.36 \n- 3.36 \n- 6.11 \n- 7.53 \n- \n- \n- 15 \n- 5.64 \n- 5.31 \n- -3.98 \n- -2.26 \n- 5.91 \n- -3.30 \n- -1.03 \n- 5.68 \n- -3.06 \n- -0.33 \n- -1.16 \n- 2.19 \n- 4.20 \n- 1.01 \n- -3.22 \n- -4.31 \n- -5.74 \n- -4.44 \n- -2.30 \n- -1.36 \n- -1.20 \n- -11.27 \n- 2.59 \n- 6.69 \n- 5.91 \n- \n- \n- 16 \n- 4.08 \n- 4.34 \n- -2.44 \n- -3.30 \n- 6.04 \n- -2.52 \n- -0.47 \n- 5.28 \n- -4.84 \n- 1.58 \n- 0.23 \n- 0.10 \n- 5.79 \n- 1.80 \n- -3.13 \n- -3.85 \n- -5.53 \n- -2.97 \n- -2.13 \n- -1.15 \n- -0.56 \n- -13.13 \n- 2.07 \n- 6.16 \n- 4.94 \n- \n- \n- 17 \n- 5.64 \n- 4.57 \n- -3.53 \n- -3.76 \n- 6.58 \n- -2.58 \n- -0.75 \n- 6.58 \n- -4.78 \n- 3.63 \n- -0.29 \n- 0.56 \n- 5.76 \n- 2.05 \n- -2.27 \n- -2.31 \n- -4.95 \n- -3.16 \n- -3.06 \n- -2.43 \n- 0.84 \n- -12.57 \n- 3.56 \n- 7.36 \n- 4.70 \n- \n- \n- 18 \n- 5.99 \n- 5.82 \n- -2.85 \n- -4.15 \n- 7.12 \n- -3.32 \n- -1.21 \n- 7.93 \n- -4.85 \n- 1.44 \n- -0.63 \n- 0.35 \n- 7.47 \n- 0.87 \n- -1.52 \n- -2.09 \n- -4.23 \n- -2.55 \n- -2.46 \n- -2.89 \n- 1.90 \n- -9.74 \n- 3.43 \n- 7.07 \n- 4.39 \n- \n- \n- 19 \n- 4.03 \n- 6.23 \n- -4.10 \n- -4.11 \n- 7.19 \n- -4.10 \n- -1.52 \n- 6.53 \n- -5.21 \n- -0.24 \n- 0.01 \n- 1.16 \n- 6.43 \n- -1.97 \n- -2.64 \n- -1.66 \n- -5.20 \n- -3.25 \n- -2.87 \n- -1.65 \n- 1.64 \n- -10.66 \n- 2.83 \n- 7.48 \n- 3.94 \n+ 0 \n+ 0.23 \n+ 1.03 \n+ -0.84 \n+ -0.59 \n+ -0.96 \n+ -0.22 \n+ -0.62 \n+ 1.84 \n+ -2.05 \n+ 0.87 \n+ -0.92 \n+ -0.23 \n+ 2.15 \n+ -1.33 \n+ 0.08 \n+ -1.25 \n+ 1.20 \n+ -1.05 \n+ 1.06 \n+ -0.42 \n+ 2.29 \n+ -2.59 \n+ 2.82 \n+ 0.68 \n+ -1.58 \n+ \n+ \n+ 1 \n+ -1.75 \n+ 1.56 \n+ -1.13 \n+ -1.10 \n+ 1.03 \n+ 0.00 \n+ -2.46 \n+ 3.45 \n+ -1.66 \n+ 1.27 \n+ -0.52 \n+ -0.02 \n+ 1.52 \n+ -1.09 \n+ -1.86 \n+ -1.13 \n+ -0.68 \n+ -0.81 \n+ 0.35 \n+ -0.06 \n+ 1.79 \n+ -2.82 \n+ 2.26 \n+ 0.78 \n+ 0.44 \n+ \n+ \n+ 2 \n+ -0.65 \n+ 3.22 \n+ -1.76 \n+ 0.52 \n+ 2.20 \n+ -0.37 \n+ -3.00 \n+ 3.73 \n+ -1.87 \n+ 2.46 \n+ 0.21 \n+ -0.24 \n+ -0.10 \n+ -0.78 \n+ -3.02 \n+ -0.82 \n+ -0.21 \n+ -0.23 \n+ 0.86 \n+ -0.68 \n+ 1.45 \n+ -4.89 \n+ 3.03 \n+ 1.91 \n+ 0.61 \n+ \n+ \n+ 3 \n+ -1.62 \n+ 3.71 \n+ -2.31 \n+ 0.43 \n+ 4.17 \n+ -0.43 \n+ -3.86 \n+ 4.16 \n+ -2.15 \n+ 1.08 \n+ 0.12 \n+ 0.60 \n+ -0.89 \n+ 0.27 \n+ -3.67 \n+ -2.71 \n+ -0.31 \n+ -1.59 \n+ 1.35 \n+ -1.83 \n+ 0.91 \n+ -5.80 \n+ 2.81 \n+ 2.11 \n+ 0.28 \n+ \n+ \n+ 4 \n+ -3.35 \n+ 4.48 \n+ -1.86 \n+ -1.70 \n+ 5.19 \n+ -1.02 \n+ -3.81 \n+ 4.72 \n+ -0.72 \n+ 1.08 \n+ -0.18 \n+ 0.83 \n+ -0.22 \n+ -1.08 \n+ -4.27 \n+ -2.88 \n+ -0.97 \n+ -1.78 \n+ 1.53 \n+ -1.80 \n+ 2.21 \n+ -6.34 \n+ 3.34 \n+ 2.49 \n+ 2.09 \n+ \n+ \n+ 5 \n+ -0.84 \n+ 4.23 \n+ -1.65 \n+ -2.00 \n+ 5.34 \n+ -0.99 \n+ -4.13 \n+ 3.94 \n+ -1.06 \n+ -0.94 \n+ 1.24 \n+ 0.09 \n+ -1.78 \n+ -0.11 \n+ -4.45 \n+ -0.85 \n+ -2.06 \n+ -1.35 \n+ 0.80 \n+ -1.63 \n+ 1.54 \n+ -6.51 \n+ 2.80 \n+ 2.14 \n+ 3.77 \n+ \n+ \n+ 6 \n+ -0.74 \n+ 5.35 \n+ -2.11 \n+ -1.13 \n+ 4.20 \n+ -1.85 \n+ -3.20 \n+ 3.76 \n+ -3.22 \n+ -1.23 \n+ 0.34 \n+ 0.57 \n+ -1.82 \n+ 0.54 \n+ -4.43 \n+ -1.83 \n+ -4.03 \n+ -2.62 \n+ -0.20 \n+ -4.68 \n+ 1.93 \n+ -8.46 \n+ 3.34 \n+ 2.52 \n+ 5.81 \n+ \n+ \n+ 7 \n+ -0.44 \n+ 4.69 \n+ -2.30 \n+ -0.21 \n+ 5.93 \n+ -2.63 \n+ -1.83 \n+ 5.46 \n+ -4.50 \n+ -3.16 \n+ -1.73 \n+ 0.18 \n+ 0.11 \n+ 0.04 \n+ -5.99 \n+ -0.45 \n+ -6.20 \n+ -3.89 \n+ 0.71 \n+ -3.95 \n+ 0.67 \n+ -7.26 \n+ 2.97 \n+ 3.39 \n+ 6.66 \n+ \n+ \n+ 8 \n+ 0.92 \n+ 5.80 \n+ -3.33 \n+ -0.65 \n+ 5.99 \n+ -3.19 \n+ -1.83 \n+ 5.63 \n+ -3.53 \n+ -1.30 \n+ -1.61 \n+ 0.82 \n+ -2.45 \n+ -0.40 \n+ -6.06 \n+ -0.52 \n+ -6.60 \n+ -3.48 \n+ -0.04 \n+ -4.60 \n+ 0.51 \n+ -5.85 \n+ 3.23 \n+ 2.40 \n+ 5.08 \n+ \n+ \n+ 9 \n+ 0.38 \n+ 5.54 \n+ -4.49 \n+ -0.80 \n+ 7.05 \n+ -2.64 \n+ -0.44 \n+ 5.35 \n+ -1.96 \n+ -0.33 \n+ -0.80 \n+ 0.26 \n+ -3.37 \n+ -0.82 \n+ -6.05 \n+ -2.61 \n+ -8.45 \n+ -4.45 \n+ 0.41 \n+ -4.71 \n+ 1.89 \n+ -6.93 \n+ 2.14 \n+ 3.00 \n+ 5.16 \n+ \n+ \n+ 10 \n+ 2.06 \n+ 5.84 \n+ -3.90 \n+ -0.98 \n+ 7.78 \n+ -2.49 \n+ -0.59 \n+ 5.59 \n+ -2.22 \n+ -0.71 \n+ -0.46 \n+ 1.80 \n+ -2.79 \n+ 0.48 \n+ -5.97 \n+ -3.44 \n+ -7.77 \n+ -5.49 \n+ -0.70 \n+ -4.61 \n+ -0.52 \n+ -7.72 \n+ 1.54 \n+ 5.02 \n+ 5.81 \n+ \n+ \n+ 11 \n+ 1.86 \n+ 4.47 \n+ -2.17 \n+ -1.38 \n+ 5.90 \n+ -0.49 \n+ 0.02 \n+ 5.78 \n+ -1.04 \n+ -0.60 \n+ 0.49 \n+ 1.96 \n+ -1.47 \n+ 1.88 \n+ -5.92 \n+ -4.55 \n+ -8.15 \n+ -3.42 \n+ -2.24 \n+ -4.33 \n+ -1.17 \n+ -7.90 \n+ 1.36 \n+ 5.31 \n+ 5.83 \n+ \n+ \n+ 12 \n+ 3.19 \n+ 4.22 \n+ -3.06 \n+ -2.27 \n+ 5.93 \n+ -2.64 \n+ 0.33 \n+ 6.72 \n+ -2.84 \n+ -0.20 \n+ 1.89 \n+ 2.63 \n+ -1.53 \n+ 0.75 \n+ -5.27 \n+ -4.53 \n+ -7.57 \n+ -2.85 \n+ -2.17 \n+ -4.78 \n+ -1.13 \n+ -8.99 \n+ 2.11 \n+ 6.42 \n+ 5.60 \n+ \n+ \n+ 13 \n+ 2.31 \n+ 4.45 \n+ -3.87 \n+ -2.05 \n+ 6.76 \n+ -3.25 \n+ -2.17 \n+ 7.99 \n+ -2.56 \n+ -0.80 \n+ 0.71 \n+ 2.33 \n+ -0.16 \n+ -0.46 \n+ -5.10 \n+ -3.79 \n+ -7.58 \n+ -4.00 \n+ 0.33 \n+ -3.67 \n+ -1.05 \n+ -8.71 \n+ 2.47 \n+ 5.87 \n+ 6.71 \n+ \n+ \n+ 14 \n+ 3.78 \n+ 4.33 \n+ -3.88 \n+ -1.58 \n+ 6.22 \n+ -3.23 \n+ -1.46 \n+ 5.57 \n+ -2.93 \n+ -0.33 \n+ -0.97 \n+ 1.72 \n+ 3.61 \n+ 0.29 \n+ -4.21 \n+ -4.10 \n+ -6.68 \n+ -4.50 \n+ -2.19 \n+ -2.43 \n+ -1.64 \n+ -9.36 \n+ 3.36 \n+ 6.11 \n+ 7.53 \n+ \n+ \n+ 15 \n+ 5.64 \n+ 5.31 \n+ -3.98 \n+ -2.26 \n+ 5.91 \n+ -3.30 \n+ -1.03 \n+ 5.68 \n+ -3.06 \n+ -0.33 \n+ -1.16 \n+ 2.19 \n+ 4.20 \n+ 1.01 \n+ -3.22 \n+ -4.31 \n+ -5.74 \n+ -4.44 \n+ -2.30 \n+ -1.36 \n+ -1.20 \n+ -11.27 \n+ 2.59 \n+ 6.69 \n+ 5.91 \n+ \n+ \n+ 16 \n+ 4.08 \n+ 4.34 \n+ -2.44 \n+ -3.30 \n+ 6.04 \n+ -2.52 \n+ -0.47 \n+ 5.28 \n+ -4.84 \n+ 1.58 \n+ 0.23 \n+ 0.10 \n+ 5.79 \n+ 1.80 \n+ -3.13 \n+ -3.85 \n+ -5.53 \n+ -2.97 \n+ -2.13 \n+ -1.15 \n+ -0.56 \n+ -13.13 \n+ 2.07 \n+ 6.16 \n+ 4.94 \n+ \n+ \n+ 17 \n+ 5.64 \n+ 4.57 \n+ -3.53 \n+ -3.76 \n+ 6.58 \n+ -2.58 \n+ -0.75 \n+ 6.58 \n+ -4.78 \n+ 3.63 \n+ -0.29 \n+ 0.56 \n+ 5.76 \n+ 2.05 \n+ -2.27 \n+ -2.31 \n+ -4.95 \n+ -3.16 \n+ -3.06 \n+ -2.43 \n+ 0.84 \n+ -12.57 \n+ 3.56 \n+ 7.36 \n+ 4.70 \n+ \n+ \n+ 18 \n+ 5.99 \n+ 5.82 \n+ -2.85 \n+ -4.15 \n+ 7.12 \n+ -3.32 \n+ -1.21 \n+ 7.93 \n+ -4.85 \n+ 1.44 \n+ -0.63 \n+ 0.35 \n+ 7.47 \n+ 0.87 \n+ -1.52 \n+ -2.09 \n+ -4.23 \n+ -2.55 \n+ -2.46 \n+ -2.89 \n+ 1.90 \n+ -9.74 \n+ 3.43 \n+ 7.07 \n+ 4.39 \n+ \n+ \n+ 19 \n+ 4.03 \n+ 6.23 \n+ -4.10 \n+ -4.11 \n+ 7.19 \n+ -4.10 \n+ -1.52 \n+ 6.53 \n+ -5.21 \n+ -0.24 \n+ 0.01 \n+ 1.16 \n+ 6.43 \n+ -1.97 \n+ -2.64 \n+ -1.66 \n+ -5.20 \n+ -3.25 \n+ -2.87 \n+ -1.65 \n+ 1.64 \n+ -10.66 \n+ 2.83 \n+ 7.48 \n+ 3.94 \n \n
\n \n \n \n Export to Excel\u00b6
\n New in version 0.20.0
\n@@ -6156,15 +6156,15 @@\n \n \n \n \n \n My Table
\n \n-\n+\n \n \n A \n B \n C \n D \n@@ -6174,237 +6174,237 @@\n \n \n \n \n \n \n \n- 0 \n+ 0 \n \n \n- 1.000000 \n+ 1.000000 \n \n \n- 1.329212 \n+ 1.329212 \n \n \n- nan \n+ nan \n \n \n- -0.316280 \n+ -0.316280 \n \n \n- -0.990810 \n+ -0.990810 \n \n \n \n \n \n \n \n- 1 \n+ 1 \n \n \n- 2.000000 \n+ 2.000000 \n \n \n- -1.070816 \n+ -1.070816 \n \n \n- -1.438713 \n+ -1.438713 \n \n \n- 0.564417 \n+ 0.564417 \n \n \n- 0.295722 \n+ 0.295722 \n \n \n \n \n \n \n \n- 2 \n+ 2 \n \n \n- 3.000000 \n+ 3.000000 \n \n \n- -1.626404 \n+ -1.626404 \n \n \n- 0.219565 \n+ 0.219565 \n \n \n- 0.678805 \n+ 0.678805 \n \n \n- 1.889273 \n+ 1.889273 \n \n \n \n \n \n \n \n- 3 \n+ 3 \n \n \n- 4.000000 \n+ 4.000000 \n \n \n- 0.961538 \n+ 0.961538 \n \n \n- 0.104011 \n+ 0.104011 \n \n \n- nan \n+ nan \n \n \n- 0.850229 \n+ 0.850229 \n \n \n \n \n \n \n \n- 4 \n+ 4 \n \n \n- 5.000000 \n+ 5.000000 \n \n \n- 1.453425 \n+ 1.453425 \n \n \n- 1.057737 \n+ 1.057737 \n \n \n- 0.165562 \n+ 0.165562 \n \n \n- 0.515018 \n+ 0.515018 \n \n \n \n \n \n \n \n- 5 \n+ 5 \n \n \n- 6.000000 \n+ 6.000000 \n \n \n- -1.336936 \n+ -1.336936 \n \n \n- 0.562861 \n+ 0.562861 \n \n \n- 1.392855 \n+ 1.392855 \n \n \n- -0.063328 \n+ -0.063328 \n \n \n \n \n \n \n \n- 6 \n+ 6 \n \n \n- 7.000000 \n+ 7.000000 \n \n \n- 0.121668 \n+ 0.121668 \n \n \n- 1.207603 \n+ 1.207603 \n \n \n- -0.002040 \n+ -0.002040 \n \n \n- 1.627796 \n+ 1.627796 \n \n \n \n \n \n \n \n- 7 \n+ 7 \n \n \n- 8.000000 \n+ 8.000000 \n \n \n- 0.354493 \n+ 0.354493 \n \n \n- 1.037528 \n+ 1.037528 \n \n \n- -0.385684 \n+ -0.385684 \n \n \n- 0.519818 \n+ 0.519818 \n \n \n \n \n \n \n \n- 8 \n+ 8 \n \n \n- 9.000000 \n+ 9.000000 \n \n \n- 1.686583 \n+ 1.686583 \n \n \n- -1.325963 \n+ -1.325963 \n \n \n- 1.428984 \n+ 1.428984 \n \n \n- -2.089354 \n+ -2.089354 \n \n \n \n \n \n \n \n- 9 \n+ 9 \n \n \n- 10.000000 \n+ 10.000000 \n \n \n- -0.129820 \n+ -0.129820 \n \n \n- 0.631523 \n+ 0.631523 \n \n \n- -0.586538 \n+ -0.586538 \n \n \n- 0.290720 \n+ 0.290720 \n \n \n \n \n
\n \n Our custom template accepts a table_title keyword. We can provide the value in the .render method.
\n@@ -6426,15 +6426,15 @@\n \n \n \n \n \n Extending Example
\n \n-\n+\n \n \n A \n B \n C \n D \n@@ -6444,237 +6444,237 @@\n \n \n \n \n \n \n \n- 0 \n+ 0 \n \n \n- 1.000000 \n+ 1.000000 \n \n \n- 1.329212 \n+ 1.329212 \n \n \n- nan \n+ nan \n \n \n- -0.316280 \n+ -0.316280 \n \n \n- -0.990810 \n+ -0.990810 \n \n \n \n \n \n \n \n- 1 \n+ 1 \n \n \n- 2.000000 \n+ 2.000000 \n \n \n- -1.070816 \n+ -1.070816 \n \n \n- -1.438713 \n+ -1.438713 \n \n \n- 0.564417 \n+ 0.564417 \n \n \n- 0.295722 \n+ 0.295722 \n \n \n \n \n \n \n \n- 2 \n+ 2 \n \n \n- 3.000000 \n+ 3.000000 \n \n \n- -1.626404 \n+ -1.626404 \n \n \n- 0.219565 \n+ 0.219565 \n \n \n- 0.678805 \n+ 0.678805 \n \n \n- 1.889273 \n+ 1.889273 \n \n \n \n \n \n \n \n- 3 \n+ 3 \n \n \n- 4.000000 \n+ 4.000000 \n \n \n- 0.961538 \n+ 0.961538 \n \n \n- 0.104011 \n+ 0.104011 \n \n \n- nan \n+ nan \n \n \n- 0.850229 \n+ 0.850229 \n \n \n \n \n \n \n \n- 4 \n+ 4 \n \n \n- 5.000000 \n+ 5.000000 \n \n \n- 1.453425 \n+ 1.453425 \n \n \n- 1.057737 \n+ 1.057737 \n \n \n- 0.165562 \n+ 0.165562 \n \n \n- 0.515018 \n+ 0.515018 \n \n \n \n \n \n \n \n- 5 \n+ 5 \n \n \n- 6.000000 \n+ 6.000000 \n \n \n- -1.336936 \n+ -1.336936 \n \n \n- 0.562861 \n+ 0.562861 \n \n \n- 1.392855 \n+ 1.392855 \n \n \n- -0.063328 \n+ -0.063328 \n \n \n \n \n \n \n \n- 6 \n+ 6 \n \n \n- 7.000000 \n+ 7.000000 \n \n \n- 0.121668 \n+ 0.121668 \n \n \n- 1.207603 \n+ 1.207603 \n \n \n- -0.002040 \n+ -0.002040 \n \n \n- 1.627796 \n+ 1.627796 \n \n \n \n \n \n \n \n- 7 \n+ 7 \n \n \n- 8.000000 \n+ 8.000000 \n \n \n- 0.354493 \n+ 0.354493 \n \n \n- 1.037528 \n+ 1.037528 \n \n \n- -0.385684 \n+ -0.385684 \n \n \n- 0.519818 \n+ 0.519818 \n \n \n \n \n \n \n \n- 8 \n+ 8 \n \n \n- 9.000000 \n+ 9.000000 \n \n \n- 1.686583 \n+ 1.686583 \n \n \n- -1.325963 \n+ -1.325963 \n \n \n- 1.428984 \n+ 1.428984 \n \n \n- -2.089354 \n+ -2.089354 \n \n \n \n \n \n \n \n- 9 \n+ 9 \n \n \n- 10.000000 \n+ 10.000000 \n \n \n- -0.129820 \n+ -0.129820 \n \n \n- 0.631523 \n+ 0.631523 \n \n \n- -0.586538 \n+ -0.586538 \n \n \n- 0.290720 \n+ 0.290720 \n \n \n \n \n
\n \n For convenience, we provide the Styler.from_custom_template method that does the same as the custom subclass.
\n@@ -6697,15 +6697,15 @@\n \n \n \n \n \n My Table
\n \n-\n+\n \n \n A \n B \n C \n D \n@@ -6715,237 +6715,237 @@\n \n \n \n \n \n \n \n- 0 \n+ 0 \n \n \n- 1.000000 \n+ 1.000000 \n \n \n- 1.329212 \n+ 1.329212 \n \n \n- nan \n+ nan \n \n \n- -0.316280 \n+ -0.316280 \n \n \n- -0.990810 \n+ -0.990810 \n \n \n \n \n \n \n \n- 1 \n+ 1 \n \n \n- 2.000000 \n+ 2.000000 \n \n \n- -1.070816 \n+ -1.070816 \n \n \n- -1.438713 \n+ -1.438713 \n \n \n- 0.564417 \n+ 0.564417 \n \n \n- 0.295722 \n+ 0.295722 \n \n \n \n \n \n \n \n- 2 \n+ 2 \n \n \n- 3.000000 \n+ 3.000000 \n \n \n- -1.626404 \n+ -1.626404 \n \n \n- 0.219565 \n+ 0.219565 \n \n \n- 0.678805 \n+ 0.678805 \n \n \n- 1.889273 \n+ 1.889273 \n \n \n \n \n \n \n \n- 3 \n+ 3 \n \n \n- 4.000000 \n+ 4.000000 \n \n \n- 0.961538 \n+ 0.961538 \n \n \n- 0.104011 \n+ 0.104011 \n \n \n- nan \n+ nan \n \n \n- 0.850229 \n+ 0.850229 \n \n \n \n \n \n \n \n- 4 \n+ 4 \n \n \n- 5.000000 \n+ 5.000000 \n \n \n- 1.453425 \n+ 1.453425 \n \n \n- 1.057737 \n+ 1.057737 \n \n \n- 0.165562 \n+ 0.165562 \n \n \n- 0.515018 \n+ 0.515018 \n \n \n \n \n \n \n \n- 5 \n+ 5 \n \n \n- 6.000000 \n+ 6.000000 \n \n \n- -1.336936 \n+ -1.336936 \n \n \n- 0.562861 \n+ 0.562861 \n \n \n- 1.392855 \n+ 1.392855 \n \n \n- -0.063328 \n+ -0.063328 \n \n \n \n \n \n \n \n- 6 \n+ 6 \n \n \n- 7.000000 \n+ 7.000000 \n \n \n- 0.121668 \n+ 0.121668 \n \n \n- 1.207603 \n+ 1.207603 \n \n \n- -0.002040 \n+ -0.002040 \n \n \n- 1.627796 \n+ 1.627796 \n \n \n \n \n \n \n \n- 7 \n+ 7 \n \n \n- 8.000000 \n+ 8.000000 \n \n \n- 0.354493 \n+ 0.354493 \n \n \n- 1.037528 \n+ 1.037528 \n \n \n- -0.385684 \n+ -0.385684 \n \n \n- 0.519818 \n+ 0.519818 \n \n \n \n \n \n \n \n- 8 \n+ 8 \n \n \n- 9.000000 \n+ 9.000000 \n \n \n- 1.686583 \n+ 1.686583 \n \n \n- -1.325963 \n+ -1.325963 \n \n \n- 1.428984 \n+ 1.428984 \n \n \n- -2.089354 \n+ -2.089354 \n \n \n \n \n \n \n \n- 9 \n+ 9 \n \n \n- 10.000000 \n+ 10.000000 \n \n \n- -0.129820 \n+ -0.129820 \n \n \n- 0.631523 \n+ 0.631523 \n \n \n- -0.586538 \n+ -0.586538 \n \n \n- 0.290720 \n+ 0.290720 \n \n \n \n \n
\n \n Here\u2019s the template structure:
\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -77,38 +77,38 @@\n The above output looks very similar to the standard DataFrame HTML\n representation. But we\u00e2\u0080\u0099ve done some work behind the scenes to attach CSS\n classes to each cell. We can view these by calling the .render method.\n [4]:\n df.style.highlight_null().render().split('\\n')[:10]\n [4]:\n ['\n+ ' }\n A B \n C D E ',\n ' ',\n ' 0',\n ' 1.000000',\n ' 1.329212',\n ' nan',\n ' -\n+id=\"T_4056c92e_ea13_11ef_835c_39a090989f27row0_col3\" class=\"data row0 col3\" >-\n 0.316280']\n The row0_col2 is the identifier for that particular cell. We\u00e2\u0080\u0099ve also\n prepended each row/column identifier with a UUID unique to each DataFrame so\n that the style from one doesn\u00e2\u0080\u0099t collide with the styling from another within\n the same notebook or page (you can set the uuid if you\u00e2\u0080\u0099d like to tie together\n the styling of two DataFrames).\n When writing style functions, you take care of producing the CSS attribute /\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/user_guide/style.ipynb.gz", "source2": "./usr/share/doc/python-pandas-doc/html/user_guide/style.ipynb.gz", "unified_diff": null, "details": [{"source1": "style.ipynb", "source2": "style.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "comments": ["Similarity: 0.9997853087998139%", "Differences: {\"'cells'\": \"{5: {'outputs': {0: {'data': {'text/html': {insert: [(1, ' A B C D E\\n\",\n- \" A B C D E \\n\",\n+ \" A B C D E \\n\",\n \" \\n\",\n- \" 0 \\n\",\n- \" 1.000000 \\n\",\n- \" 1.329212 \\n\",\n- \" nan \\n\",\n- \" -0.316280 \\n\",\n- \" -0.990810 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 1 \\n\",\n- \" 2.000000 \\n\",\n- \" -1.070816 \\n\",\n- \" -1.438713 \\n\",\n- \" 0.564417 \\n\",\n- \" 0.295722 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 2 \\n\",\n- \" 3.000000 \\n\",\n- \" -1.626404 \\n\",\n- \" 0.219565 \\n\",\n- \" 0.678805 \\n\",\n- \" 1.889273 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 3 \\n\",\n- \" 4.000000 \\n\",\n- \" 0.961538 \\n\",\n- \" 0.104011 \\n\",\n- \" nan \\n\",\n- \" 0.850229 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 4 \\n\",\n- \" 5.000000 \\n\",\n- \" 1.453425 \\n\",\n- \" 1.057737 \\n\",\n- \" 0.165562 \\n\",\n- \" 0.515018 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 5 \\n\",\n- \" 6.000000 \\n\",\n- \" -1.336936 \\n\",\n- \" 0.562861 \\n\",\n- \" 1.392855 \\n\",\n- \" -0.063328 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 6 \\n\",\n- \" 7.000000 \\n\",\n- \" 0.121668 \\n\",\n- \" 1.207603 \\n\",\n- \" -0.002040 \\n\",\n- \" 1.627796 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 7 \\n\",\n- \" 8.000000 \\n\",\n- \" 0.354493 \\n\",\n- \" 1.037528 \\n\",\n- \" -0.385684 \\n\",\n- \" 0.519818 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 8 \\n\",\n- \" 9.000000 \\n\",\n- \" 1.686583 \\n\",\n- \" -1.325963 \\n\",\n- \" 1.428984 \\n\",\n- \" -2.089354 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 9 \\n\",\n- \" 10.000000 \\n\",\n- \" -0.129820 \\n\",\n- \" 0.631523 \\n\",\n- \" -0.586538 \\n\",\n- \" 0.290720 \\n\",\n+ \" 0 \\n\",\n+ \" 1.000000 \\n\",\n+ \" 1.329212 \\n\",\n+ \" nan \\n\",\n+ \" -0.316280 \\n\",\n+ \" -0.990810 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 1 \\n\",\n+ \" 2.000000 \\n\",\n+ \" -1.070816 \\n\",\n+ \" -1.438713 \\n\",\n+ \" 0.564417 \\n\",\n+ \" 0.295722 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 2 \\n\",\n+ \" 3.000000 \\n\",\n+ \" -1.626404 \\n\",\n+ \" 0.219565 \\n\",\n+ \" 0.678805 \\n\",\n+ \" 1.889273 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 3 \\n\",\n+ \" 4.000000 \\n\",\n+ \" 0.961538 \\n\",\n+ \" 0.104011 \\n\",\n+ \" nan \\n\",\n+ \" 0.850229 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 4 \\n\",\n+ \" 5.000000 \\n\",\n+ \" 1.453425 \\n\",\n+ \" 1.057737 \\n\",\n+ \" 0.165562 \\n\",\n+ \" 0.515018 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 5 \\n\",\n+ \" 6.000000 \\n\",\n+ \" -1.336936 \\n\",\n+ \" 0.562861 \\n\",\n+ \" 1.392855 \\n\",\n+ \" -0.063328 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 6 \\n\",\n+ \" 7.000000 \\n\",\n+ \" 0.121668 \\n\",\n+ \" 1.207603 \\n\",\n+ \" -0.002040 \\n\",\n+ \" 1.627796 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 7 \\n\",\n+ \" 8.000000 \\n\",\n+ \" 0.354493 \\n\",\n+ \" 1.037528 \\n\",\n+ \" -0.385684 \\n\",\n+ \" 0.519818 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 8 \\n\",\n+ \" 9.000000 \\n\",\n+ \" 1.686583 \\n\",\n+ \" -1.325963 \\n\",\n+ \" 1.428984 \\n\",\n+ \" -2.089354 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 9 \\n\",\n+ \" 10.000000 \\n\",\n+ \" -0.129820 \\n\",\n+ \" 0.631523 \\n\",\n+ \" -0.586538 \\n\",\n+ \" 0.290720 \\n\",\n \" \\n\",\n \"
\"\n ],\n \"text/plain\": [\n- \"\"\n+ \"\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n@@ -197,23 +197,23 @@\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[' A B C D E ',\\n\",\n+ \" ' } A B C D E ',\\n\",\n \" ' ',\\n\",\n- \" ' 0 ',\\n\",\n- \" ' 1.000000 ',\\n\",\n- \" ' 1.329212 ',\\n\",\n- \" ' nan ',\\n\",\n- \" ' -0.316280 ']\"\n+ \" ' 0 ',\\n\",\n+ \" ' 1.000000 ',\\n\",\n+ \" ' 1.329212 ',\\n\",\n+ \" ' nan ',\\n\",\n+ \" ' -0.316280 ']\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n@@ -266,103 +266,103 @@\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \" A B C D E \\n\",\n+ \" } A B C D E \\n\",\n \" \\n\",\n- \" 0 \\n\",\n- \" 1.000000 \\n\",\n- \" 1.329212 \\n\",\n- \" nan \\n\",\n- \" -0.316280 \\n\",\n- \" -0.990810 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 1 \\n\",\n- \" 2.000000 \\n\",\n- \" -1.070816 \\n\",\n- \" -1.438713 \\n\",\n- \" 0.564417 \\n\",\n- \" 0.295722 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 2 \\n\",\n- \" 3.000000 \\n\",\n- \" -1.626404 \\n\",\n- \" 0.219565 \\n\",\n- \" 0.678805 \\n\",\n- \" 1.889273 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 3 \\n\",\n- \" 4.000000 \\n\",\n- \" 0.961538 \\n\",\n- \" 0.104011 \\n\",\n- \" nan \\n\",\n- \" 0.850229 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 4 \\n\",\n- \" 5.000000 \\n\",\n- \" 1.453425 \\n\",\n- \" 1.057737 \\n\",\n- \" 0.165562 \\n\",\n- \" 0.515018 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 5 \\n\",\n- \" 6.000000 \\n\",\n- \" -1.336936 \\n\",\n- \" 0.562861 \\n\",\n- \" 1.392855 \\n\",\n- \" -0.063328 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 6 \\n\",\n- \" 7.000000 \\n\",\n- \" 0.121668 \\n\",\n- \" 1.207603 \\n\",\n- \" -0.002040 \\n\",\n- \" 1.627796 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 7 \\n\",\n- \" 8.000000 \\n\",\n- \" 0.354493 \\n\",\n- \" 1.037528 \\n\",\n- \" -0.385684 \\n\",\n- \" 0.519818 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 8 \\n\",\n- \" 9.000000 \\n\",\n- \" 1.686583 \\n\",\n- \" -1.325963 \\n\",\n- \" 1.428984 \\n\",\n- \" -2.089354 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 9 \\n\",\n- \" 10.000000 \\n\",\n- \" -0.129820 \\n\",\n- \" 0.631523 \\n\",\n- \" -0.586538 \\n\",\n- \" 0.290720 \\n\",\n+ \" 0 \\n\",\n+ \" 1.000000 \\n\",\n+ \" 1.329212 \\n\",\n+ \" nan \\n\",\n+ \" -0.316280 \\n\",\n+ \" -0.990810 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 1 \\n\",\n+ \" 2.000000 \\n\",\n+ \" -1.070816 \\n\",\n+ \" -1.438713 \\n\",\n+ \" 0.564417 \\n\",\n+ \" 0.295722 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 2 \\n\",\n+ \" 3.000000 \\n\",\n+ \" -1.626404 \\n\",\n+ \" 0.219565 \\n\",\n+ \" 0.678805 \\n\",\n+ \" 1.889273 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 3 \\n\",\n+ \" 4.000000 \\n\",\n+ \" 0.961538 \\n\",\n+ \" 0.104011 \\n\",\n+ \" nan \\n\",\n+ \" 0.850229 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 4 \\n\",\n+ \" 5.000000 \\n\",\n+ \" 1.453425 \\n\",\n+ \" 1.057737 \\n\",\n+ \" 0.165562 \\n\",\n+ \" 0.515018 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 5 \\n\",\n+ \" 6.000000 \\n\",\n+ \" -1.336936 \\n\",\n+ \" 0.562861 \\n\",\n+ \" 1.392855 \\n\",\n+ \" -0.063328 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 6 \\n\",\n+ \" 7.000000 \\n\",\n+ \" 0.121668 \\n\",\n+ \" 1.207603 \\n\",\n+ \" -0.002040 \\n\",\n+ \" 1.627796 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 7 \\n\",\n+ \" 8.000000 \\n\",\n+ \" 0.354493 \\n\",\n+ \" 1.037528 \\n\",\n+ \" -0.385684 \\n\",\n+ \" 0.519818 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 8 \\n\",\n+ \" 9.000000 \\n\",\n+ \" 1.686583 \\n\",\n+ \" -1.325963 \\n\",\n+ \" 1.428984 \\n\",\n+ \" -2.089354 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 9 \\n\",\n+ \" 10.000000 \\n\",\n+ \" -0.129820 \\n\",\n+ \" 0.631523 \\n\",\n+ \" -0.586538 \\n\",\n+ \" 0.290720 \\n\",\n \" \\n\",\n \"
\"\n ],\n \"text/plain\": [\n- \"\"\n+ \"\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n@@ -410,101 +410,101 @@\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \" A B C D E \\n\",\n+ \" } A B C D E \\n\",\n \" \\n\",\n- \" 0 \\n\",\n- \" 1.000000 \\n\",\n- \" 1.329212 \\n\",\n- \" nan \\n\",\n- \" -0.316280 \\n\",\n- \" -0.990810 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 1 \\n\",\n- \" 2.000000 \\n\",\n- \" -1.070816 \\n\",\n- \" -1.438713 \\n\",\n- \" 0.564417 \\n\",\n- \" 0.295722 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 2 \\n\",\n- \" 3.000000 \\n\",\n- \" -1.626404 \\n\",\n- \" 0.219565 \\n\",\n- \" 0.678805 \\n\",\n- \" 1.889273 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 3 \\n\",\n- \" 4.000000 \\n\",\n- \" 0.961538 \\n\",\n- \" 0.104011 \\n\",\n- \" nan \\n\",\n- \" 0.850229 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 4 \\n\",\n- \" 5.000000 \\n\",\n- \" 1.453425 \\n\",\n- \" 1.057737 \\n\",\n- \" 0.165562 \\n\",\n- \" 0.515018 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 5 \\n\",\n- \" 6.000000 \\n\",\n- \" -1.336936 \\n\",\n- \" 0.562861 \\n\",\n- \" 1.392855 \\n\",\n- \" -0.063328 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 6 \\n\",\n- \" 7.000000 \\n\",\n- \" 0.121668 \\n\",\n- \" 1.207603 \\n\",\n- \" -0.002040 \\n\",\n- \" 1.627796 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 7 \\n\",\n- \" 8.000000 \\n\",\n- \" 0.354493 \\n\",\n- \" 1.037528 \\n\",\n- \" -0.385684 \\n\",\n- \" 0.519818 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 8 \\n\",\n- \" 9.000000 \\n\",\n- \" 1.686583 \\n\",\n- \" -1.325963 \\n\",\n- \" 1.428984 \\n\",\n- \" -2.089354 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 9 \\n\",\n- \" 10.000000 \\n\",\n- \" -0.129820 \\n\",\n- \" 0.631523 \\n\",\n- \" -0.586538 \\n\",\n- \" 0.290720 \\n\",\n+ \" 0 \\n\",\n+ \" 1.000000 \\n\",\n+ \" 1.329212 \\n\",\n+ \" nan \\n\",\n+ \" -0.316280 \\n\",\n+ \" -0.990810 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 1 \\n\",\n+ \" 2.000000 \\n\",\n+ \" -1.070816 \\n\",\n+ \" -1.438713 \\n\",\n+ \" 0.564417 \\n\",\n+ \" 0.295722 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 2 \\n\",\n+ \" 3.000000 \\n\",\n+ \" -1.626404 \\n\",\n+ \" 0.219565 \\n\",\n+ \" 0.678805 \\n\",\n+ \" 1.889273 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 3 \\n\",\n+ \" 4.000000 \\n\",\n+ \" 0.961538 \\n\",\n+ \" 0.104011 \\n\",\n+ \" nan \\n\",\n+ \" 0.850229 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 4 \\n\",\n+ \" 5.000000 \\n\",\n+ \" 1.453425 \\n\",\n+ \" 1.057737 \\n\",\n+ \" 0.165562 \\n\",\n+ \" 0.515018 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 5 \\n\",\n+ \" 6.000000 \\n\",\n+ \" -1.336936 \\n\",\n+ \" 0.562861 \\n\",\n+ \" 1.392855 \\n\",\n+ \" -0.063328 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 6 \\n\",\n+ \" 7.000000 \\n\",\n+ \" 0.121668 \\n\",\n+ \" 1.207603 \\n\",\n+ \" -0.002040 \\n\",\n+ \" 1.627796 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 7 \\n\",\n+ \" 8.000000 \\n\",\n+ \" 0.354493 \\n\",\n+ \" 1.037528 \\n\",\n+ \" -0.385684 \\n\",\n+ \" 0.519818 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 8 \\n\",\n+ \" 9.000000 \\n\",\n+ \" 1.686583 \\n\",\n+ \" -1.325963 \\n\",\n+ \" 1.428984 \\n\",\n+ \" -2.089354 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 9 \\n\",\n+ \" 10.000000 \\n\",\n+ \" -0.129820 \\n\",\n+ \" 0.631523 \\n\",\n+ \" -0.586538 \\n\",\n+ \" 0.290720 \\n\",\n \" \\n\",\n \"
\"\n ],\n \"text/plain\": [\n- \"\"\n+ \"\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n@@ -532,106 +532,106 @@\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \" A B C D E \\n\",\n+ \" } A B C D E \\n\",\n \" \\n\",\n- \" 0 \\n\",\n- \" 1.000000 \\n\",\n- \" 1.329212 \\n\",\n- \" nan \\n\",\n- \" -0.316280 \\n\",\n- \" -0.990810 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 1 \\n\",\n- \" 2.000000 \\n\",\n- \" -1.070816 \\n\",\n- \" -1.438713 \\n\",\n- \" 0.564417 \\n\",\n- \" 0.295722 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 2 \\n\",\n- \" 3.000000 \\n\",\n- \" -1.626404 \\n\",\n- \" 0.219565 \\n\",\n- \" 0.678805 \\n\",\n- \" 1.889273 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 3 \\n\",\n- \" 4.000000 \\n\",\n- \" 0.961538 \\n\",\n- \" 0.104011 \\n\",\n- \" nan \\n\",\n- \" 0.850229 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 4 \\n\",\n- \" 5.000000 \\n\",\n- \" 1.453425 \\n\",\n- \" 1.057737 \\n\",\n- \" 0.165562 \\n\",\n- \" 0.515018 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 5 \\n\",\n- \" 6.000000 \\n\",\n- \" -1.336936 \\n\",\n- \" 0.562861 \\n\",\n- \" 1.392855 \\n\",\n- \" -0.063328 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 6 \\n\",\n- \" 7.000000 \\n\",\n- \" 0.121668 \\n\",\n- \" 1.207603 \\n\",\n- \" -0.002040 \\n\",\n- \" 1.627796 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 7 \\n\",\n- \" 8.000000 \\n\",\n- \" 0.354493 \\n\",\n- \" 1.037528 \\n\",\n- \" -0.385684 \\n\",\n- \" 0.519818 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 8 \\n\",\n- \" 9.000000 \\n\",\n- \" 1.686583 \\n\",\n- \" -1.325963 \\n\",\n- \" 1.428984 \\n\",\n- \" -2.089354 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 9 \\n\",\n- \" 10.000000 \\n\",\n- \" -0.129820 \\n\",\n- \" 0.631523 \\n\",\n- \" -0.586538 \\n\",\n- \" 0.290720 \\n\",\n+ \" 0 \\n\",\n+ \" 1.000000 \\n\",\n+ \" 1.329212 \\n\",\n+ \" nan \\n\",\n+ \" -0.316280 \\n\",\n+ \" -0.990810 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 1 \\n\",\n+ \" 2.000000 \\n\",\n+ \" -1.070816 \\n\",\n+ \" -1.438713 \\n\",\n+ \" 0.564417 \\n\",\n+ \" 0.295722 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 2 \\n\",\n+ \" 3.000000 \\n\",\n+ \" -1.626404 \\n\",\n+ \" 0.219565 \\n\",\n+ \" 0.678805 \\n\",\n+ \" 1.889273 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 3 \\n\",\n+ \" 4.000000 \\n\",\n+ \" 0.961538 \\n\",\n+ \" 0.104011 \\n\",\n+ \" nan \\n\",\n+ \" 0.850229 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 4 \\n\",\n+ \" 5.000000 \\n\",\n+ \" 1.453425 \\n\",\n+ \" 1.057737 \\n\",\n+ \" 0.165562 \\n\",\n+ \" 0.515018 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 5 \\n\",\n+ \" 6.000000 \\n\",\n+ \" -1.336936 \\n\",\n+ \" 0.562861 \\n\",\n+ \" 1.392855 \\n\",\n+ \" -0.063328 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 6 \\n\",\n+ \" 7.000000 \\n\",\n+ \" 0.121668 \\n\",\n+ \" 1.207603 \\n\",\n+ \" -0.002040 \\n\",\n+ \" 1.627796 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 7 \\n\",\n+ \" 8.000000 \\n\",\n+ \" 0.354493 \\n\",\n+ \" 1.037528 \\n\",\n+ \" -0.385684 \\n\",\n+ \" 0.519818 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 8 \\n\",\n+ \" 9.000000 \\n\",\n+ \" 1.686583 \\n\",\n+ \" -1.325963 \\n\",\n+ \" 1.428984 \\n\",\n+ \" -2.089354 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 9 \\n\",\n+ \" 10.000000 \\n\",\n+ \" -0.129820 \\n\",\n+ \" 0.631523 \\n\",\n+ \" -0.586538 \\n\",\n+ \" 0.290720 \\n\",\n \" \\n\",\n \"
\"\n ],\n \"text/plain\": [\n- \"\"\n+ \"\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n@@ -687,101 +687,101 @@\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \" A B C D E \\n\",\n+ \" } A B C D E \\n\",\n \" \\n\",\n- \" 0 \\n\",\n- \" 1.000000 \\n\",\n- \" 1.329212 \\n\",\n- \" nan \\n\",\n- \" -0.316280 \\n\",\n- \" -0.990810 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 1 \\n\",\n- \" 2.000000 \\n\",\n- \" -1.070816 \\n\",\n- \" -1.438713 \\n\",\n- \" 0.564417 \\n\",\n- \" 0.295722 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 2 \\n\",\n- \" 3.000000 \\n\",\n- \" -1.626404 \\n\",\n- \" 0.219565 \\n\",\n- \" 0.678805 \\n\",\n- \" 1.889273 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 3 \\n\",\n- \" 4.000000 \\n\",\n- \" 0.961538 \\n\",\n- \" 0.104011 \\n\",\n- \" nan \\n\",\n- \" 0.850229 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 4 \\n\",\n- \" 5.000000 \\n\",\n- \" 1.453425 \\n\",\n- \" 1.057737 \\n\",\n- \" 0.165562 \\n\",\n- \" 0.515018 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 5 \\n\",\n- \" 6.000000 \\n\",\n- \" -1.336936 \\n\",\n- \" 0.562861 \\n\",\n- \" 1.392855 \\n\",\n- \" -0.063328 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 6 \\n\",\n- \" 7.000000 \\n\",\n- \" 0.121668 \\n\",\n- \" 1.207603 \\n\",\n- \" -0.002040 \\n\",\n- \" 1.627796 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 7 \\n\",\n- \" 8.000000 \\n\",\n- \" 0.354493 \\n\",\n- \" 1.037528 \\n\",\n- \" -0.385684 \\n\",\n- \" 0.519818 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 8 \\n\",\n- \" 9.000000 \\n\",\n- \" 1.686583 \\n\",\n- \" -1.325963 \\n\",\n- \" 1.428984 \\n\",\n- \" -2.089354 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 9 \\n\",\n- \" 10.000000 \\n\",\n- \" -0.129820 \\n\",\n- \" 0.631523 \\n\",\n- \" -0.586538 \\n\",\n- \" 0.290720 \\n\",\n+ \" 0 \\n\",\n+ \" 1.000000 \\n\",\n+ \" 1.329212 \\n\",\n+ \" nan \\n\",\n+ \" -0.316280 \\n\",\n+ \" -0.990810 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 1 \\n\",\n+ \" 2.000000 \\n\",\n+ \" -1.070816 \\n\",\n+ \" -1.438713 \\n\",\n+ \" 0.564417 \\n\",\n+ \" 0.295722 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 2 \\n\",\n+ \" 3.000000 \\n\",\n+ \" -1.626404 \\n\",\n+ \" 0.219565 \\n\",\n+ \" 0.678805 \\n\",\n+ \" 1.889273 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 3 \\n\",\n+ \" 4.000000 \\n\",\n+ \" 0.961538 \\n\",\n+ \" 0.104011 \\n\",\n+ \" nan \\n\",\n+ \" 0.850229 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 4 \\n\",\n+ \" 5.000000 \\n\",\n+ \" 1.453425 \\n\",\n+ \" 1.057737 \\n\",\n+ \" 0.165562 \\n\",\n+ \" 0.515018 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 5 \\n\",\n+ \" 6.000000 \\n\",\n+ \" -1.336936 \\n\",\n+ \" 0.562861 \\n\",\n+ \" 1.392855 \\n\",\n+ \" -0.063328 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 6 \\n\",\n+ \" 7.000000 \\n\",\n+ \" 0.121668 \\n\",\n+ \" 1.207603 \\n\",\n+ \" -0.002040 \\n\",\n+ \" 1.627796 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 7 \\n\",\n+ \" 8.000000 \\n\",\n+ \" 0.354493 \\n\",\n+ \" 1.037528 \\n\",\n+ \" -0.385684 \\n\",\n+ \" 0.519818 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 8 \\n\",\n+ \" 9.000000 \\n\",\n+ \" 1.686583 \\n\",\n+ \" -1.325963 \\n\",\n+ \" 1.428984 \\n\",\n+ \" -2.089354 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 9 \\n\",\n+ \" 10.000000 \\n\",\n+ \" -0.129820 \\n\",\n+ \" 0.631523 \\n\",\n+ \" -0.586538 \\n\",\n+ \" 0.290720 \\n\",\n \" \\n\",\n \"
\"\n ],\n \"text/plain\": [\n- \"\"\n+ \"\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n@@ -833,101 +833,101 @@\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \" A B C D E \\n\",\n+ \" } A B C D E \\n\",\n \" \\n\",\n- \" 0 \\n\",\n- \" 1.000000 \\n\",\n- \" 1.329212 \\n\",\n- \" nan \\n\",\n- \" -0.316280 \\n\",\n- \" -0.990810 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 1 \\n\",\n- \" 2.000000 \\n\",\n- \" -1.070816 \\n\",\n- \" -1.438713 \\n\",\n- \" 0.564417 \\n\",\n- \" 0.295722 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 2 \\n\",\n- \" 3.000000 \\n\",\n- \" -1.626404 \\n\",\n- \" 0.219565 \\n\",\n- \" 0.678805 \\n\",\n- \" 1.889273 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 3 \\n\",\n- \" 4.000000 \\n\",\n- \" 0.961538 \\n\",\n- \" 0.104011 \\n\",\n- \" nan \\n\",\n- \" 0.850229 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 4 \\n\",\n- \" 5.000000 \\n\",\n- \" 1.453425 \\n\",\n- \" 1.057737 \\n\",\n- \" 0.165562 \\n\",\n- \" 0.515018 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 5 \\n\",\n- \" 6.000000 \\n\",\n- \" -1.336936 \\n\",\n- \" 0.562861 \\n\",\n- \" 1.392855 \\n\",\n- \" -0.063328 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 6 \\n\",\n- \" 7.000000 \\n\",\n- \" 0.121668 \\n\",\n- \" 1.207603 \\n\",\n- \" -0.002040 \\n\",\n- \" 1.627796 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 7 \\n\",\n- \" 8.000000 \\n\",\n- \" 0.354493 \\n\",\n- \" 1.037528 \\n\",\n- \" -0.385684 \\n\",\n- \" 0.519818 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 8 \\n\",\n- \" 9.000000 \\n\",\n- \" 1.686583 \\n\",\n- \" -1.325963 \\n\",\n- \" 1.428984 \\n\",\n- \" -2.089354 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 9 \\n\",\n- \" 10.000000 \\n\",\n- \" -0.129820 \\n\",\n- \" 0.631523 \\n\",\n- \" -0.586538 \\n\",\n- \" 0.290720 \\n\",\n+ \" 0 \\n\",\n+ \" 1.000000 \\n\",\n+ \" 1.329212 \\n\",\n+ \" nan \\n\",\n+ \" -0.316280 \\n\",\n+ \" -0.990810 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 1 \\n\",\n+ \" 2.000000 \\n\",\n+ \" -1.070816 \\n\",\n+ \" -1.438713 \\n\",\n+ \" 0.564417 \\n\",\n+ \" 0.295722 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 2 \\n\",\n+ \" 3.000000 \\n\",\n+ \" -1.626404 \\n\",\n+ \" 0.219565 \\n\",\n+ \" 0.678805 \\n\",\n+ \" 1.889273 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 3 \\n\",\n+ \" 4.000000 \\n\",\n+ \" 0.961538 \\n\",\n+ \" 0.104011 \\n\",\n+ \" nan \\n\",\n+ \" 0.850229 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 4 \\n\",\n+ \" 5.000000 \\n\",\n+ \" 1.453425 \\n\",\n+ \" 1.057737 \\n\",\n+ \" 0.165562 \\n\",\n+ \" 0.515018 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 5 \\n\",\n+ \" 6.000000 \\n\",\n+ \" -1.336936 \\n\",\n+ \" 0.562861 \\n\",\n+ \" 1.392855 \\n\",\n+ \" -0.063328 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 6 \\n\",\n+ \" 7.000000 \\n\",\n+ \" 0.121668 \\n\",\n+ \" 1.207603 \\n\",\n+ \" -0.002040 \\n\",\n+ \" 1.627796 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 7 \\n\",\n+ \" 8.000000 \\n\",\n+ \" 0.354493 \\n\",\n+ \" 1.037528 \\n\",\n+ \" -0.385684 \\n\",\n+ \" 0.519818 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 8 \\n\",\n+ \" 9.000000 \\n\",\n+ \" 1.686583 \\n\",\n+ \" -1.325963 \\n\",\n+ \" 1.428984 \\n\",\n+ \" -2.089354 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 9 \\n\",\n+ \" 10.000000 \\n\",\n+ \" -0.129820 \\n\",\n+ \" 0.631523 \\n\",\n+ \" -0.586538 \\n\",\n+ \" 0.290720 \\n\",\n \" \\n\",\n \"
\"\n ],\n \"text/plain\": [\n- \"\"\n+ \"\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n@@ -947,103 +947,103 @@\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \" A B C D E \\n\",\n+ \" } A B C D E \\n\",\n \" \\n\",\n- \" 0 \\n\",\n- \" 1.000000 \\n\",\n- \" 1.329212 \\n\",\n- \" nan \\n\",\n- \" -0.316280 \\n\",\n- \" -0.990810 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 1 \\n\",\n- \" 2.000000 \\n\",\n- \" -1.070816 \\n\",\n- \" -1.438713 \\n\",\n- \" 0.564417 \\n\",\n- \" 0.295722 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 2 \\n\",\n- \" 3.000000 \\n\",\n- \" -1.626404 \\n\",\n- \" 0.219565 \\n\",\n- \" 0.678805 \\n\",\n- \" 1.889273 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 3 \\n\",\n- \" 4.000000 \\n\",\n- \" 0.961538 \\n\",\n- \" 0.104011 \\n\",\n- \" nan \\n\",\n- \" 0.850229 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 4 \\n\",\n- \" 5.000000 \\n\",\n- \" 1.453425 \\n\",\n- \" 1.057737 \\n\",\n- \" 0.165562 \\n\",\n- \" 0.515018 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 5 \\n\",\n- \" 6.000000 \\n\",\n- \" -1.336936 \\n\",\n- \" 0.562861 \\n\",\n- \" 1.392855 \\n\",\n- \" -0.063328 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 6 \\n\",\n- \" 7.000000 \\n\",\n- \" 0.121668 \\n\",\n- \" 1.207603 \\n\",\n- \" -0.002040 \\n\",\n- \" 1.627796 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 7 \\n\",\n- \" 8.000000 \\n\",\n- \" 0.354493 \\n\",\n- \" 1.037528 \\n\",\n- \" -0.385684 \\n\",\n- \" 0.519818 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 8 \\n\",\n- \" 9.000000 \\n\",\n- \" 1.686583 \\n\",\n- \" -1.325963 \\n\",\n- \" 1.428984 \\n\",\n- \" -2.089354 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 9 \\n\",\n- \" 10.000000 \\n\",\n- \" -0.129820 \\n\",\n- \" 0.631523 \\n\",\n- \" -0.586538 \\n\",\n- \" 0.290720 \\n\",\n+ \" 0 \\n\",\n+ \" 1.000000 \\n\",\n+ \" 1.329212 \\n\",\n+ \" nan \\n\",\n+ \" -0.316280 \\n\",\n+ \" -0.990810 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 1 \\n\",\n+ \" 2.000000 \\n\",\n+ \" -1.070816 \\n\",\n+ \" -1.438713 \\n\",\n+ \" 0.564417 \\n\",\n+ \" 0.295722 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 2 \\n\",\n+ \" 3.000000 \\n\",\n+ \" -1.626404 \\n\",\n+ \" 0.219565 \\n\",\n+ \" 0.678805 \\n\",\n+ \" 1.889273 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 3 \\n\",\n+ \" 4.000000 \\n\",\n+ \" 0.961538 \\n\",\n+ \" 0.104011 \\n\",\n+ \" nan \\n\",\n+ \" 0.850229 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 4 \\n\",\n+ \" 5.000000 \\n\",\n+ \" 1.453425 \\n\",\n+ \" 1.057737 \\n\",\n+ \" 0.165562 \\n\",\n+ \" 0.515018 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 5 \\n\",\n+ \" 6.000000 \\n\",\n+ \" -1.336936 \\n\",\n+ \" 0.562861 \\n\",\n+ \" 1.392855 \\n\",\n+ \" -0.063328 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 6 \\n\",\n+ \" 7.000000 \\n\",\n+ \" 0.121668 \\n\",\n+ \" 1.207603 \\n\",\n+ \" -0.002040 \\n\",\n+ \" 1.627796 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 7 \\n\",\n+ \" 8.000000 \\n\",\n+ \" 0.354493 \\n\",\n+ \" 1.037528 \\n\",\n+ \" -0.385684 \\n\",\n+ \" 0.519818 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 8 \\n\",\n+ \" 9.000000 \\n\",\n+ \" 1.686583 \\n\",\n+ \" -1.325963 \\n\",\n+ \" 1.428984 \\n\",\n+ \" -2.089354 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 9 \\n\",\n+ \" 10.000000 \\n\",\n+ \" -0.129820 \\n\",\n+ \" 0.631523 \\n\",\n+ \" -0.586538 \\n\",\n+ \" 0.290720 \\n\",\n \" \\n\",\n \"
\"\n ],\n \"text/plain\": [\n- \"\"\n+ \"\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n@@ -1080,99 +1080,99 @@\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \" A B C D E \\n\",\n+ \" A B C D E \\n\",\n \" \\n\",\n- \" 0 \\n\",\n- \" 100.00% \\n\",\n- \" 132.92% \\n\",\n- \" nan% \\n\",\n- \" -31.63% \\n\",\n- \" -99.08% \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 1 \\n\",\n- \" 200.00% \\n\",\n- \" -107.08% \\n\",\n- \" -143.87% \\n\",\n- \" 56.44% \\n\",\n- \" 29.57% \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 2 \\n\",\n- \" 300.00% \\n\",\n- \" -162.64% \\n\",\n- \" 21.96% \\n\",\n- \" 67.88% \\n\",\n- \" 188.93% \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 3 \\n\",\n- \" 400.00% \\n\",\n- \" 96.15% \\n\",\n- \" 10.40% \\n\",\n- \" nan% \\n\",\n- \" 85.02% \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 4 \\n\",\n- \" 500.00% \\n\",\n- \" 145.34% \\n\",\n- \" 105.77% \\n\",\n- \" 16.56% \\n\",\n- \" 51.50% \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 5 \\n\",\n- \" 600.00% \\n\",\n- \" -133.69% \\n\",\n- \" 56.29% \\n\",\n- \" 139.29% \\n\",\n- \" -6.33% \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 6 \\n\",\n- \" 700.00% \\n\",\n- \" 12.17% \\n\",\n- \" 120.76% \\n\",\n- \" -0.20% \\n\",\n- \" 162.78% \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 7 \\n\",\n- \" 800.00% \\n\",\n- \" 35.45% \\n\",\n- \" 103.75% \\n\",\n- \" -38.57% \\n\",\n- \" 51.98% \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 8 \\n\",\n- \" 900.00% \\n\",\n- \" 168.66% \\n\",\n- \" -132.60% \\n\",\n- \" 142.90% \\n\",\n- \" -208.94% \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 9 \\n\",\n- \" 1000.00% \\n\",\n- \" -12.98% \\n\",\n- \" 63.15% \\n\",\n- \" -58.65% \\n\",\n- \" 29.07% \\n\",\n+ \" 0 \\n\",\n+ \" 100.00% \\n\",\n+ \" 132.92% \\n\",\n+ \" nan% \\n\",\n+ \" -31.63% \\n\",\n+ \" -99.08% \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 1 \\n\",\n+ \" 200.00% \\n\",\n+ \" -107.08% \\n\",\n+ \" -143.87% \\n\",\n+ \" 56.44% \\n\",\n+ \" 29.57% \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 2 \\n\",\n+ \" 300.00% \\n\",\n+ \" -162.64% \\n\",\n+ \" 21.96% \\n\",\n+ \" 67.88% \\n\",\n+ \" 188.93% \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 3 \\n\",\n+ \" 400.00% \\n\",\n+ \" 96.15% \\n\",\n+ \" 10.40% \\n\",\n+ \" nan% \\n\",\n+ \" 85.02% \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 4 \\n\",\n+ \" 500.00% \\n\",\n+ \" 145.34% \\n\",\n+ \" 105.77% \\n\",\n+ \" 16.56% \\n\",\n+ \" 51.50% \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 5 \\n\",\n+ \" 600.00% \\n\",\n+ \" -133.69% \\n\",\n+ \" 56.29% \\n\",\n+ \" 139.29% \\n\",\n+ \" -6.33% \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 6 \\n\",\n+ \" 700.00% \\n\",\n+ \" 12.17% \\n\",\n+ \" 120.76% \\n\",\n+ \" -0.20% \\n\",\n+ \" 162.78% \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 7 \\n\",\n+ \" 800.00% \\n\",\n+ \" 35.45% \\n\",\n+ \" 103.75% \\n\",\n+ \" -38.57% \\n\",\n+ \" 51.98% \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 8 \\n\",\n+ \" 900.00% \\n\",\n+ \" 168.66% \\n\",\n+ \" -132.60% \\n\",\n+ \" 142.90% \\n\",\n+ \" -208.94% \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 9 \\n\",\n+ \" 1000.00% \\n\",\n+ \" -12.98% \\n\",\n+ \" 63.15% \\n\",\n+ \" -58.65% \\n\",\n+ \" 29.07% \\n\",\n \" \\n\",\n \"
\"\n ],\n \"text/plain\": [\n- \"\"\n+ \"\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n@@ -1192,99 +1192,99 @@\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \" A B C D E \\n\",\n+ \" A B C D E \\n\",\n \" \\n\",\n- \" 0 \\n\",\n- \" 1.000000 \\n\",\n- \" 1000 \\n\",\n- \" nan \\n\",\n- \" -0.32 \\n\",\n- \" -0.990810 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 1 \\n\",\n- \" 2.000000 \\n\",\n- \" -100 \\n\",\n- \" -1.438713 \\n\",\n- \" +0.56 \\n\",\n- \" 0.295722 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 2 \\n\",\n- \" 3.000000 \\n\",\n- \" -200 \\n\",\n- \" 0.219565 \\n\",\n- \" +0.68 \\n\",\n- \" 1.889273 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 3 \\n\",\n- \" 4.000000 \\n\",\n- \" 1000 \\n\",\n- \" 0.104011 \\n\",\n- \" +nan \\n\",\n- \" 0.850229 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 4 \\n\",\n- \" 5.000000 \\n\",\n- \" 1000 \\n\",\n- \" 1.057737 \\n\",\n- \" +0.17 \\n\",\n- \" 0.515018 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 5 \\n\",\n- \" 6.000000 \\n\",\n- \" -100 \\n\",\n- \" 0.562861 \\n\",\n- \" +1.39 \\n\",\n- \" -0.063328 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 6 \\n\",\n- \" 7.000000 \\n\",\n- \" 0000 \\n\",\n- \" 1.207603 \\n\",\n- \" -0.00 \\n\",\n- \" 1.627796 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 7 \\n\",\n- \" 8.000000 \\n\",\n- \" 0000 \\n\",\n- \" 1.037528 \\n\",\n- \" -0.39 \\n\",\n- \" 0.519818 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 8 \\n\",\n- \" 9.000000 \\n\",\n- \" 2000 \\n\",\n- \" -1.325963 \\n\",\n- \" +1.43 \\n\",\n- \" -2.089354 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 9 \\n\",\n- \" 10.000000 \\n\",\n- \" -000 \\n\",\n- \" 0.631523 \\n\",\n- \" -0.59 \\n\",\n- \" 0.290720 \\n\",\n+ \" 0 \\n\",\n+ \" 1.000000 \\n\",\n+ \" 1000 \\n\",\n+ \" nan \\n\",\n+ \" -0.32 \\n\",\n+ \" -0.990810 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 1 \\n\",\n+ \" 2.000000 \\n\",\n+ \" -100 \\n\",\n+ \" -1.438713 \\n\",\n+ \" +0.56 \\n\",\n+ \" 0.295722 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 2 \\n\",\n+ \" 3.000000 \\n\",\n+ \" -200 \\n\",\n+ \" 0.219565 \\n\",\n+ \" +0.68 \\n\",\n+ \" 1.889273 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 3 \\n\",\n+ \" 4.000000 \\n\",\n+ \" 1000 \\n\",\n+ \" 0.104011 \\n\",\n+ \" +nan \\n\",\n+ \" 0.850229 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 4 \\n\",\n+ \" 5.000000 \\n\",\n+ \" 1000 \\n\",\n+ \" 1.057737 \\n\",\n+ \" +0.17 \\n\",\n+ \" 0.515018 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 5 \\n\",\n+ \" 6.000000 \\n\",\n+ \" -100 \\n\",\n+ \" 0.562861 \\n\",\n+ \" +1.39 \\n\",\n+ \" -0.063328 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 6 \\n\",\n+ \" 7.000000 \\n\",\n+ \" 0000 \\n\",\n+ \" 1.207603 \\n\",\n+ \" -0.00 \\n\",\n+ \" 1.627796 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 7 \\n\",\n+ \" 8.000000 \\n\",\n+ \" 0000 \\n\",\n+ \" 1.037528 \\n\",\n+ \" -0.39 \\n\",\n+ \" 0.519818 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 8 \\n\",\n+ \" 9.000000 \\n\",\n+ \" 2000 \\n\",\n+ \" -1.325963 \\n\",\n+ \" +1.43 \\n\",\n+ \" -2.089354 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 9 \\n\",\n+ \" 10.000000 \\n\",\n+ \" -000 \\n\",\n+ \" 0.631523 \\n\",\n+ \" -0.59 \\n\",\n+ \" 0.290720 \\n\",\n \" \\n\",\n \"
\"\n ],\n \"text/plain\": [\n- \"\"\n+ \"\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n@@ -1304,99 +1304,99 @@\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \" A B C D E \\n\",\n+ \" A B C D E \\n\",\n \" \\n\",\n- \" 0 \\n\",\n- \" 1.000000 \\n\",\n- \" \\u00b11.33 \\n\",\n- \" nan \\n\",\n- \" -0.316280 \\n\",\n- \" -0.990810 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 1 \\n\",\n- \" 2.000000 \\n\",\n- \" \\u00b11.07 \\n\",\n- \" -1.438713 \\n\",\n- \" 0.564417 \\n\",\n- \" 0.295722 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 2 \\n\",\n- \" 3.000000 \\n\",\n- \" \\u00b11.63 \\n\",\n- \" 0.219565 \\n\",\n- \" 0.678805 \\n\",\n- \" 1.889273 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 3 \\n\",\n- \" 4.000000 \\n\",\n- \" \\u00b10.96 \\n\",\n- \" 0.104011 \\n\",\n- \" nan \\n\",\n- \" 0.850229 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 4 \\n\",\n- \" 5.000000 \\n\",\n- \" \\u00b11.45 \\n\",\n- \" 1.057737 \\n\",\n- \" 0.165562 \\n\",\n- \" 0.515018 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 5 \\n\",\n- \" 6.000000 \\n\",\n- \" \\u00b11.34 \\n\",\n- \" 0.562861 \\n\",\n- \" 1.392855 \\n\",\n- \" -0.063328 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 6 \\n\",\n- \" 7.000000 \\n\",\n- \" \\u00b10.12 \\n\",\n- \" 1.207603 \\n\",\n- \" -0.002040 \\n\",\n- \" 1.627796 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 7 \\n\",\n- \" 8.000000 \\n\",\n- \" \\u00b10.35 \\n\",\n- \" 1.037528 \\n\",\n- \" -0.385684 \\n\",\n- \" 0.519818 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 8 \\n\",\n- \" 9.000000 \\n\",\n- \" \\u00b11.69 \\n\",\n- \" -1.325963 \\n\",\n- \" 1.428984 \\n\",\n- \" -2.089354 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 9 \\n\",\n- \" 10.000000 \\n\",\n- \" \\u00b10.13 \\n\",\n- \" 0.631523 \\n\",\n- \" -0.586538 \\n\",\n- \" 0.290720 \\n\",\n+ \" 0 \\n\",\n+ \" 1.000000 \\n\",\n+ \" \\u00b11.33 \\n\",\n+ \" nan \\n\",\n+ \" -0.316280 \\n\",\n+ \" -0.990810 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 1 \\n\",\n+ \" 2.000000 \\n\",\n+ \" \\u00b11.07 \\n\",\n+ \" -1.438713 \\n\",\n+ \" 0.564417 \\n\",\n+ \" 0.295722 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 2 \\n\",\n+ \" 3.000000 \\n\",\n+ \" \\u00b11.63 \\n\",\n+ \" 0.219565 \\n\",\n+ \" 0.678805 \\n\",\n+ \" 1.889273 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 3 \\n\",\n+ \" 4.000000 \\n\",\n+ \" \\u00b10.96 \\n\",\n+ \" 0.104011 \\n\",\n+ \" nan \\n\",\n+ \" 0.850229 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 4 \\n\",\n+ \" 5.000000 \\n\",\n+ \" \\u00b11.45 \\n\",\n+ \" 1.057737 \\n\",\n+ \" 0.165562 \\n\",\n+ \" 0.515018 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 5 \\n\",\n+ \" 6.000000 \\n\",\n+ \" \\u00b11.34 \\n\",\n+ \" 0.562861 \\n\",\n+ \" 1.392855 \\n\",\n+ \" -0.063328 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 6 \\n\",\n+ \" 7.000000 \\n\",\n+ \" \\u00b10.12 \\n\",\n+ \" 1.207603 \\n\",\n+ \" -0.002040 \\n\",\n+ \" 1.627796 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 7 \\n\",\n+ \" 8.000000 \\n\",\n+ \" \\u00b10.35 \\n\",\n+ \" 1.037528 \\n\",\n+ \" -0.385684 \\n\",\n+ \" 0.519818 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 8 \\n\",\n+ \" 9.000000 \\n\",\n+ \" \\u00b11.69 \\n\",\n+ \" -1.325963 \\n\",\n+ \" 1.428984 \\n\",\n+ \" -2.089354 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 9 \\n\",\n+ \" 10.000000 \\n\",\n+ \" \\u00b10.13 \\n\",\n+ \" 0.631523 \\n\",\n+ \" -0.586538 \\n\",\n+ \" 0.290720 \\n\",\n \" \\n\",\n \"
\"\n ],\n \"text/plain\": [\n- \"\"\n+ \"\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n@@ -1416,99 +1416,99 @@\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \" A B C D E \\n\",\n+ \" A B C D E \\n\",\n \" \\n\",\n- \" 0 \\n\",\n- \" 100.00% \\n\",\n- \" 132.92% \\n\",\n- \" - \\n\",\n- \" -31.63% \\n\",\n- \" -99.08% \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 1 \\n\",\n- \" 200.00% \\n\",\n- \" -107.08% \\n\",\n- \" -143.87% \\n\",\n- \" 56.44% \\n\",\n- \" 29.57% \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 2 \\n\",\n- \" 300.00% \\n\",\n- \" -162.64% \\n\",\n- \" 21.96% \\n\",\n- \" 67.88% \\n\",\n- \" 188.93% \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 3 \\n\",\n- \" 400.00% \\n\",\n- \" 96.15% \\n\",\n- \" 10.40% \\n\",\n- \" - \\n\",\n- \" 85.02% \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 4 \\n\",\n- \" 500.00% \\n\",\n- \" 145.34% \\n\",\n- \" 105.77% \\n\",\n- \" 16.56% \\n\",\n- \" 51.50% \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 5 \\n\",\n- \" 600.00% \\n\",\n- \" -133.69% \\n\",\n- \" 56.29% \\n\",\n- \" 139.29% \\n\",\n- \" -6.33% \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 6 \\n\",\n- \" 700.00% \\n\",\n- \" 12.17% \\n\",\n- \" 120.76% \\n\",\n- \" -0.20% \\n\",\n- \" 162.78% \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 7 \\n\",\n- \" 800.00% \\n\",\n- \" 35.45% \\n\",\n- \" 103.75% \\n\",\n- \" -38.57% \\n\",\n- \" 51.98% \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 8 \\n\",\n- \" 900.00% \\n\",\n- \" 168.66% \\n\",\n- \" -132.60% \\n\",\n- \" 142.90% \\n\",\n- \" -208.94% \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 9 \\n\",\n- \" 1000.00% \\n\",\n- \" -12.98% \\n\",\n- \" 63.15% \\n\",\n- \" -58.65% \\n\",\n- \" 29.07% \\n\",\n+ \" 0 \\n\",\n+ \" 100.00% \\n\",\n+ \" 132.92% \\n\",\n+ \" - \\n\",\n+ \" -31.63% \\n\",\n+ \" -99.08% \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 1 \\n\",\n+ \" 200.00% \\n\",\n+ \" -107.08% \\n\",\n+ \" -143.87% \\n\",\n+ \" 56.44% \\n\",\n+ \" 29.57% \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 2 \\n\",\n+ \" 300.00% \\n\",\n+ \" -162.64% \\n\",\n+ \" 21.96% \\n\",\n+ \" 67.88% \\n\",\n+ \" 188.93% \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 3 \\n\",\n+ \" 400.00% \\n\",\n+ \" 96.15% \\n\",\n+ \" 10.40% \\n\",\n+ \" - \\n\",\n+ \" 85.02% \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 4 \\n\",\n+ \" 500.00% \\n\",\n+ \" 145.34% \\n\",\n+ \" 105.77% \\n\",\n+ \" 16.56% \\n\",\n+ \" 51.50% \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 5 \\n\",\n+ \" 600.00% \\n\",\n+ \" -133.69% \\n\",\n+ \" 56.29% \\n\",\n+ \" 139.29% \\n\",\n+ \" -6.33% \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 6 \\n\",\n+ \" 700.00% \\n\",\n+ \" 12.17% \\n\",\n+ \" 120.76% \\n\",\n+ \" -0.20% \\n\",\n+ \" 162.78% \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 7 \\n\",\n+ \" 800.00% \\n\",\n+ \" 35.45% \\n\",\n+ \" 103.75% \\n\",\n+ \" -38.57% \\n\",\n+ \" 51.98% \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 8 \\n\",\n+ \" 900.00% \\n\",\n+ \" 168.66% \\n\",\n+ \" -132.60% \\n\",\n+ \" 142.90% \\n\",\n+ \" -208.94% \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 9 \\n\",\n+ \" 1000.00% \\n\",\n+ \" -12.98% \\n\",\n+ \" 63.15% \\n\",\n+ \" -58.65% \\n\",\n+ \" 29.07% \\n\",\n \" \\n\",\n \"
\"\n ],\n \"text/plain\": [\n- \"\"\n+ \"\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n@@ -1528,101 +1528,101 @@\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \" A B C D E \\n\",\n+ \" } A B C D E \\n\",\n \" \\n\",\n- \" 0 \\n\",\n- \" 1.000000 \\n\",\n- \" 1.329212 \\n\",\n- \" - \\n\",\n- \" -0.316280 \\n\",\n- \" -0.990810 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 1 \\n\",\n- \" 2.000000 \\n\",\n- \" -1.070816 \\n\",\n- \" -1.438713 \\n\",\n- \" 0.564417 \\n\",\n- \" 0.295722 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 2 \\n\",\n- \" 3.000000 \\n\",\n- \" -1.626404 \\n\",\n- \" 0.219565 \\n\",\n- \" 0.678805 \\n\",\n- \" 1.889273 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 3 \\n\",\n- \" 4.000000 \\n\",\n- \" 0.961538 \\n\",\n- \" 0.104011 \\n\",\n- \" - \\n\",\n- \" 0.850229 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 4 \\n\",\n- \" 5.000000 \\n\",\n- \" 1.453425 \\n\",\n- \" 1.057737 \\n\",\n- \" 0.165562 \\n\",\n- \" 0.515018 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 5 \\n\",\n- \" 6.000000 \\n\",\n- \" -1.336936 \\n\",\n- \" 0.562861 \\n\",\n- \" 1.392855 \\n\",\n- \" -0.063328 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 6 \\n\",\n- \" 7.000000 \\n\",\n- \" 0.121668 \\n\",\n- \" 1.207603 \\n\",\n- \" -0.002040 \\n\",\n- \" 1.627796 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 7 \\n\",\n- \" 8.000000 \\n\",\n- \" 0.354493 \\n\",\n- \" 1.037528 \\n\",\n- \" -0.385684 \\n\",\n- \" 0.519818 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 8 \\n\",\n- \" 9.000000 \\n\",\n- \" 1.686583 \\n\",\n- \" -1.325963 \\n\",\n- \" 1.428984 \\n\",\n- \" -2.089354 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 9 \\n\",\n- \" 10.000000 \\n\",\n- \" -0.129820 \\n\",\n- \" 0.631523 \\n\",\n- \" -0.586538 \\n\",\n- \" 0.290720 \\n\",\n+ \" 0 \\n\",\n+ \" 1.000000 \\n\",\n+ \" 1.329212 \\n\",\n+ \" - \\n\",\n+ \" -0.316280 \\n\",\n+ \" -0.990810 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 1 \\n\",\n+ \" 2.000000 \\n\",\n+ \" -1.070816 \\n\",\n+ \" -1.438713 \\n\",\n+ \" 0.564417 \\n\",\n+ \" 0.295722 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 2 \\n\",\n+ \" 3.000000 \\n\",\n+ \" -1.626404 \\n\",\n+ \" 0.219565 \\n\",\n+ \" 0.678805 \\n\",\n+ \" 1.889273 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 3 \\n\",\n+ \" 4.000000 \\n\",\n+ \" 0.961538 \\n\",\n+ \" 0.104011 \\n\",\n+ \" - \\n\",\n+ \" 0.850229 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 4 \\n\",\n+ \" 5.000000 \\n\",\n+ \" 1.453425 \\n\",\n+ \" 1.057737 \\n\",\n+ \" 0.165562 \\n\",\n+ \" 0.515018 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 5 \\n\",\n+ \" 6.000000 \\n\",\n+ \" -1.336936 \\n\",\n+ \" 0.562861 \\n\",\n+ \" 1.392855 \\n\",\n+ \" -0.063328 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 6 \\n\",\n+ \" 7.000000 \\n\",\n+ \" 0.121668 \\n\",\n+ \" 1.207603 \\n\",\n+ \" -0.002040 \\n\",\n+ \" 1.627796 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 7 \\n\",\n+ \" 8.000000 \\n\",\n+ \" 0.354493 \\n\",\n+ \" 1.037528 \\n\",\n+ \" -0.385684 \\n\",\n+ \" 0.519818 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 8 \\n\",\n+ \" 9.000000 \\n\",\n+ \" 1.686583 \\n\",\n+ \" -1.325963 \\n\",\n+ \" 1.428984 \\n\",\n+ \" -2.089354 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 9 \\n\",\n+ \" 10.000000 \\n\",\n+ \" -0.129820 \\n\",\n+ \" 0.631523 \\n\",\n+ \" -0.586538 \\n\",\n+ \" 0.290720 \\n\",\n \" \\n\",\n \"
\"\n ],\n \"text/plain\": [\n- \"\"\n+ \"\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n@@ -1649,101 +1649,101 @@\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \" A B C D E \\n\",\n+ \" } A B C D E \\n\",\n \" \\n\",\n- \" 0 \\n\",\n- \" 1.000000 \\n\",\n- \" 1.329212 \\n\",\n- \" nan \\n\",\n- \" -0.316280 \\n\",\n- \" -0.990810 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 1 \\n\",\n- \" 2.000000 \\n\",\n- \" -1.070816 \\n\",\n- \" -1.438713 \\n\",\n- \" 0.564417 \\n\",\n- \" 0.295722 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 2 \\n\",\n- \" 3.000000 \\n\",\n- \" -1.626404 \\n\",\n- \" 0.219565 \\n\",\n- \" 0.678805 \\n\",\n- \" 1.889273 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 3 \\n\",\n- \" 4.000000 \\n\",\n- \" 0.961538 \\n\",\n- \" 0.104011 \\n\",\n- \" nan \\n\",\n- \" 0.850229 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 4 \\n\",\n- \" 5.000000 \\n\",\n- \" 1.453425 \\n\",\n- \" 1.057737 \\n\",\n- \" 0.165562 \\n\",\n- \" 0.515018 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 5 \\n\",\n- \" 6.000000 \\n\",\n- \" -1.336936 \\n\",\n- \" 0.562861 \\n\",\n- \" 1.392855 \\n\",\n- \" -0.063328 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 6 \\n\",\n- \" 7.000000 \\n\",\n- \" 0.121668 \\n\",\n- \" 1.207603 \\n\",\n- \" -0.002040 \\n\",\n- \" 1.627796 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 7 \\n\",\n- \" 8.000000 \\n\",\n- \" 0.354493 \\n\",\n- \" 1.037528 \\n\",\n- \" -0.385684 \\n\",\n- \" 0.519818 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 8 \\n\",\n- \" 9.000000 \\n\",\n- \" 1.686583 \\n\",\n- \" -1.325963 \\n\",\n- \" 1.428984 \\n\",\n- \" -2.089354 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 9 \\n\",\n- \" 10.000000 \\n\",\n- \" -0.129820 \\n\",\n- \" 0.631523 \\n\",\n- \" -0.586538 \\n\",\n- \" 0.290720 \\n\",\n+ \" 0 \\n\",\n+ \" 1.000000 \\n\",\n+ \" 1.329212 \\n\",\n+ \" nan \\n\",\n+ \" -0.316280 \\n\",\n+ \" -0.990810 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 1 \\n\",\n+ \" 2.000000 \\n\",\n+ \" -1.070816 \\n\",\n+ \" -1.438713 \\n\",\n+ \" 0.564417 \\n\",\n+ \" 0.295722 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 2 \\n\",\n+ \" 3.000000 \\n\",\n+ \" -1.626404 \\n\",\n+ \" 0.219565 \\n\",\n+ \" 0.678805 \\n\",\n+ \" 1.889273 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 3 \\n\",\n+ \" 4.000000 \\n\",\n+ \" 0.961538 \\n\",\n+ \" 0.104011 \\n\",\n+ \" nan \\n\",\n+ \" 0.850229 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 4 \\n\",\n+ \" 5.000000 \\n\",\n+ \" 1.453425 \\n\",\n+ \" 1.057737 \\n\",\n+ \" 0.165562 \\n\",\n+ \" 0.515018 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 5 \\n\",\n+ \" 6.000000 \\n\",\n+ \" -1.336936 \\n\",\n+ \" 0.562861 \\n\",\n+ \" 1.392855 \\n\",\n+ \" -0.063328 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 6 \\n\",\n+ \" 7.000000 \\n\",\n+ \" 0.121668 \\n\",\n+ \" 1.207603 \\n\",\n+ \" -0.002040 \\n\",\n+ \" 1.627796 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 7 \\n\",\n+ \" 8.000000 \\n\",\n+ \" 0.354493 \\n\",\n+ \" 1.037528 \\n\",\n+ \" -0.385684 \\n\",\n+ \" 0.519818 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 8 \\n\",\n+ \" 9.000000 \\n\",\n+ \" 1.686583 \\n\",\n+ \" -1.325963 \\n\",\n+ \" 1.428984 \\n\",\n+ \" -2.089354 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 9 \\n\",\n+ \" 10.000000 \\n\",\n+ \" -0.129820 \\n\",\n+ \" 0.631523 \\n\",\n+ \" -0.586538 \\n\",\n+ \" 0.290720 \\n\",\n \" \\n\",\n \"
\"\n ],\n \"text/plain\": [\n- \"\"\n+ \"\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n@@ -1763,204 +1763,204 @@\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \" A B C D E \\n\",\n+ \" } A B C D E \\n\",\n \" \\n\",\n- \" 0 \\n\",\n- \" 1.000000 \\n\",\n- \" 1.329212 \\n\",\n- \" nan \\n\",\n- \" -0.316280 \\n\",\n- \" -0.990810 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 1 \\n\",\n- \" 2.000000 \\n\",\n- \" -1.070816 \\n\",\n- \" -1.438713 \\n\",\n- \" 0.564417 \\n\",\n- \" 0.295722 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 2 \\n\",\n- \" 3.000000 \\n\",\n- \" -1.626404 \\n\",\n- \" 0.219565 \\n\",\n- \" 0.678805 \\n\",\n- \" 1.889273 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 3 \\n\",\n- \" 4.000000 \\n\",\n- \" 0.961538 \\n\",\n- \" 0.104011 \\n\",\n- \" nan \\n\",\n- \" 0.850229 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 4 \\n\",\n- \" 5.000000 \\n\",\n- \" 1.453425 \\n\",\n- \" 1.057737 \\n\",\n- \" 0.165562 \\n\",\n- \" 0.515018 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 5 \\n\",\n- \" 6.000000 \\n\",\n- \" -1.336936 \\n\",\n- \" 0.562861 \\n\",\n- \" 1.392855 \\n\",\n- \" -0.063328 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 6 \\n\",\n- \" 7.000000 \\n\",\n- \" 0.121668 \\n\",\n- \" 1.207603 \\n\",\n- \" -0.002040 \\n\",\n- \" 1.627796 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 7 \\n\",\n- \" 8.000000 \\n\",\n- \" 0.354493 \\n\",\n- \" 1.037528 \\n\",\n- \" -0.385684 \\n\",\n- \" 0.519818 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 8 \\n\",\n- \" 9.000000 \\n\",\n- \" 1.686583 \\n\",\n- \" -1.325963 \\n\",\n- \" 1.428984 \\n\",\n- \" -2.089354 \\n\",\n- \" \\n\",\n- \" \\n\",\n- \" 9 \\n\",\n- \" 10.000000 \\n\",\n- \" -0.129820 \\n\",\n- \" 0.631523 \\n\",\n- \" -0.586538 \\n\",\n- \" 0.290720 \\n\",\n+ \" 0 \\n\",\n+ \" 1.000000 \\n\",\n+ \" 1.329212 \\n\",\n+ \" nan \\n\",\n+ \" -0.316280 \\n\",\n+ \" -0.990810 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 1 \\n\",\n+ \" 2.000000 \\n\",\n+ \" -1.070816 \\n\",\n+ \" -1.438713 \\n\",\n+ \" 0.564417 \\n\",\n+ \" 0.295722 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 2 \\n\",\n+ \" 3.000000 \\n\",\n+ \" -1.626404 \\n\",\n+ \" 0.219565 \\n\",\n+ \" 0.678805 \\n\",\n+ \" 1.889273 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 3 \\n\",\n+ \" 4.000000 \\n\",\n+ \" 0.961538 \\n\",\n+ \" 0.104011 \\n\",\n+ \" nan \\n\",\n+ \" 0.850229 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 4 \\n\",\n+ \" 5.000000 \\n\",\n+ \" 1.453425 \\n\",\n+ \" 1.057737 \\n\",\n+ \" 0.165562 \\n\",\n+ \" 0.515018 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 5 \\n\",\n+ \" 6.000000 \\n\",\n+ \" -1.336936 \\n\",\n+ \" 0.562861 \\n\",\n+ \" 1.392855 \\n\",\n+ \" -0.063328 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 6 \\n\",\n+ \" 7.000000 \\n\",\n+ \" 0.121668 \\n\",\n+ \" 1.207603 \\n\",\n+ \" -0.002040 \\n\",\n+ \" 1.627796 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 7 \\n\",\n+ \" 8.000000 \\n\",\n+ \" 0.354493 \\n\",\n+ \" 1.037528 \\n\",\n+ \" -0.385684 \\n\",\n+ \" 0.519818 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 8 \\n\",\n+ \" 9.000000 \\n\",\n+ \" 1.686583 \\n\",\n+ \" -1.325963 \\n\",\n+ \" 1.428984 \\n\",\n+ \" -2.089354 \\n\",\n+ \" \\n\",\n+ \" \\n\",\n+ \" 9 \\n\",\n+ \" 10.000000 \\n\",\n+ \" -0.129820 \\n\",\n+ \" 0.631523 \\n\",\n+ \" -0.586538 \\n\",\n+ \" 0.290720 \\n\",\n \" \\n\",\n \"
\"\n ],\n \"text/plain\": [\n- \"\"\n+ \"\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n@@ -1985,107 +1985,107 @@\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"