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1 | 1 | .. _cookbook:
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2 | 2 |
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3 |
| -.. currentmodule:: pandas |
4 |
| - |
5 |
| -.. ipython:: python |
6 |
| - :suppress: |
7 |
| -
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8 |
| - import datetime |
9 |
| - import functools |
10 |
| - import glob |
11 |
| - import itertools |
12 |
| - import os |
13 |
| -
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14 |
| - import numpy as np |
15 |
| - import pandas as pd |
16 |
| - from pandas.compat import StringIO |
17 |
| -
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18 |
| -
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19 |
| - np.random.seed(123456) |
20 |
| - np.set_printoptions(precision=4, suppress=True) |
21 |
| - pd.options.display.max_rows = 15 |
22 |
| -
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| 3 | +{{ header }} |
23 | 4 |
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24 | 5 | ********
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25 | 6 | Cookbook
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@@ -186,6 +167,8 @@ One could hard code:
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186 | 167 |
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187 | 168 | .. ipython:: python
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188 | 169 |
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| 170 | + import functools |
| 171 | +
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189 | 172 | CritList = [Crit1, Crit2, Crit3]
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190 | 173 | AllCrit = functools.reduce(lambda x, y: x & y, CritList)
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191 | 174 |
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@@ -409,6 +392,8 @@ To take the cross section of the 1st level and 1st axis the index:
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409 | 392 |
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410 | 393 | .. ipython:: python
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411 | 394 |
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| 395 | + import itertools |
| 396 | +
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412 | 397 | index = list(itertools.product(['Ada', 'Quinn', 'Violet'],
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413 | 398 | ['Comp', 'Math', 'Sci']))
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414 | 399 | headr = list(itertools.product(['Exams', 'Labs'], ['I', 'II']))
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@@ -1022,6 +1007,9 @@ You can use the same approach to read all files matching a pattern. Here is an
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1022 | 1007 |
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1023 | 1008 | .. ipython:: python
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1024 | 1009 |
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| 1010 | + import glob |
| 1011 | + import os |
| 1012 | +
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1025 | 1013 | files = glob.glob('file_*.csv')
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1026 | 1014 | result = pd.concat([pd.read_csv(f) for f in files], ignore_index=True)
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1027 | 1015 |
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@@ -1081,6 +1069,8 @@ Option 1: pass rows explicitly to skip rows
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1081 | 1069 |
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1082 | 1070 | .. ipython:: python
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1083 | 1071 |
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| 1072 | + from pandas.compat import StringIO |
| 1073 | +
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1084 | 1074 | pd.read_csv(StringIO(data), sep=';', skiprows=[11, 12],
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1085 | 1075 | index_col=0, parse_dates=True, header=10)
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1086 | 1076 |
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@@ -1327,6 +1317,8 @@ The :ref:`Timedeltas <timedeltas.timedeltas>` docs.
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1327 | 1317 |
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1328 | 1318 | .. ipython:: python
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1329 | 1319 |
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| 1320 | + import datetime |
| 1321 | +
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1330 | 1322 | s = pd.Series(pd.date_range('2012-1-1', periods=3, freq='D'))
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1331 | 1323 |
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1332 | 1324 | s - s.max()
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