十分钟搞定pandas

http://pandas.pydata.org/pandas-docs/stable/10min.html

10 Minutes to pandas

This is a short introduction to pandas, geared mainly for new users. You can see more complex recipes in the Cookbook

Customarily, we import as follows:

In [1]: import pandas as pdIn [2]: import numpy as npIn [3]: import matplotlib.pyplot as plt

Object Creation

See the Data Structure Intro section

Creating a Series by passing a list of values, letting pandas create a default integer index:

In [4]: s = pd.Series([1,3,5,np.nan,6,8])In [5]: sOut[5]: 0    1.01    3.02    5.03    NaN4    6.05    8.0dtype: float64

Creating a DataFrame by passing a numpy array, with a datetime index and labeled columns:

In [6]: dates = pd.date_range('20130101', periods=6)In [7]: datesOut[7]: DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',               '2013-01-05', '2013-01-06'],              dtype='datetime64[ns]', freq='D')In [8]: df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))In [9]: dfOut[9]:                    A         B         C         D2013-01-01  0.469112 -0.282863 -1.509059 -1.1356322013-01-02  1.212112 -0.173215  0.119209 -1.0442362013-01-03 -0.861849 -2.104569 -0.494929  1.0718042013-01-04  0.721555 -0.706771 -1.039575  0.2718602013-01-05 -0.424972  0.567020  0.276232 -1.0874012013-01-06 -0.673690  0.113648 -1.478427  0.524988

Creating a DataFrame by passing a dict of objects that can be converted to series-like.

In [10]: df2 = pd.DataFrame({ 'A' : 1.,   ....:                      'B' : pd.Timestamp('20130102'),   ....:                      'C' : pd.Series(1,index=list(range(4)),dtype='float32'),   ....:                      'D' : np.array([3] * 4,dtype='int32'),   ....:                      'E' : pd.Categorical(["test","train","test","train"]),   ....:                      'F' : 'foo' })   ....: In [11]: df2Out[11]:      A          B    C  D      E    F0  1.0 2013-01-02  1.0  3   test  foo1  1.0 2013-01-02  1.0  3  train  foo2  1.0 2013-01-02  1.0  3   test  foo3  1.0 2013-01-02  1.0  3  train  foo

Having specific dtypes

In [12]: df2.dtypesOut[12]: A           float64B    datetime64[ns]C           float32D             int32E          categoryF            objectdtype: object

If you’re using IPython, tab completion for column names (as well as public attributes) is automatically enabled. Here’s a subset of the attributes that will be completed:

In [13]: df2.<TAB>df2.A                  df2.booldf2.abs                df2.boxplotdf2.add                df2.Cdf2.add_prefix         df2.clipdf2.add_suffix         df2.clip_lowerdf2.align              df2.clip_upperdf2.all                df2.columnsdf2.any                df2.combinedf2.append             df2.combine_firstdf2.apply              df2.compounddf2.applymap           df2.consolidatedf2.as_blocks          df2.convert_objectsdf2.asfreq             df2.copydf2.as_matrix          df2.corrdf2.astype             df2.corrwithdf2.at                 df2.countdf2.at_time            df2.covdf2.axes               df2.cummaxdf2.B                  df2.cummindf2.between_time       df2.cumproddf2.bfill              df2.cumsumdf2.blocks             df2.D

As you can see, the columns A, B, C, and D are automatically tab completed. E is there as well; the rest of the attributes have been truncated for brevity.

Viewing Data

See the Basics section

See the top & bottom rows of the frame

In [14]: df.head()Out[14]:                    A         B         C         D2013-01-01  0.469112 -0.282863 -1.509059 -1.1356322013-01-02  1.212112 -0.173215  0.119209 -1.0442362013-01-03 -0.861849 -2.104569 -0.494929  1.0718042013-01-04  0.721555 -0.706771 -1.039575  0.2718602013-01-05 -0.424972  0.567020  0.276232 -1.087401In [15]: df.tail(3)Out[15]:                    A         B         C         D2013-01-04  0.721555 -0.706771 -1.039575  0.2718602013-01-05 -0.424972  0.567020  0.276232 -1.0874012013-01-06 -0.673690  0.113648 -1.478427  0.524988

Display the index, columns, and the underlying numpy data

In [16]: df.indexOut[16]: DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',               '2013-01-05', '2013-01-06'],              dtype='datetime64[ns]', freq='D')In [17]: df.columnsOut[17]: Index(['A', 'B', 'C', 'D'], dtype='object')In [18]: df.valuesOut[18]: array([[ 0.4691, -0.2829, -1.5091, -1.1356],       [ 1.2121, -0.1732,  0.1192, -1.0442],       [-0.8618, -2.1046, -0.4949,  1.0718],       [ 0.7216, -0.7068, -1.0396,  0.2719],       [-0.425 ,  0.567 ,  0.2762, -1.0874],       [-0.6737,  0.1136, -1.4784,  0.525 ]])

Describe shows a quick statistic summary of your data

In [19]: df.describe()Out[19]:               A         B         C         Dcount  6.000000  6.000000  6.000000  6.000000mean   0.073711 -0.431125 -0.687758 -0.233103std    0.843157  0.922818  0.779887  0.973118min   -0.861849 -2.104569 -1.509059 -1.13563225%   -0.611510 -0.600794 -1.368714 -1.07661050%    0.022070 -0.228039 -0.767252 -0.38618875%    0.658444  0.041933 -0.034326  0.461706max    1.212112  0.567020  0.276232  1.071804

Transposing your data

In [20]: df.TOut[20]:    2013-01-01  2013-01-02  2013-01-03  2013-01-04  2013-01-05  2013-01-06A    0.469112    1.212112   -0.861849    0.721555   -0.424972   -0.673690B   -0.282863   -0.173215   -2.104569   -0.706771    0.567020    0.113648C   -1.509059    0.119209   -0.494929   -1.039575    0.276232   -1.478427D   -1.135632   -1.044236    1.071804    0.271860   -1.087401    0.524988

Sorting by an axis

In [21]: df.sort_index(axis=1, ascending=False)Out[21]:                    D         C         B         A2013-01-01 -1.135632 -1.509059 -0.282863  0.4691122013-01-02 -1.044236  0.119209 -0.173215  1.2121122013-01-03  1.071804 -0.494929 -2.104569 -0.8618492013-01-04  0.271860 -1.039575 -0.706771  0.7215552013-01-05 -1.087401  0.276232  0.567020 -0.4249722013-01-06  0.524988 -1.478427  0.113648 -0.673690

Sorting by values

In [22]: df.sort_values(by='B')Out[22]:                    A         B         C         D2013-01-03 -0.861849 -2.104569 -0.494929  1.0718042013-01-04  0.721555 -0.706771 -1.039575  0.2718602013-01-01  0.469112 -0.282863 -1.509059 -1.1356322013-01-02  1.212112 -0.173215  0.119209 -1.0442362013-01-06 -0.673690  0.113648 -1.478427  0.5249882013-01-05 -0.424972  0.567020  0.276232 -1.087401

Selection

Note

 

While standard Python / Numpy expressions for selecting and setting are intuitive and come in handy for interactive work, for production code, we recommend the optimized pandas data access methods, .at, .iat, .loc,.iloc and .ix.

See the indexing documentation Indexing and Selecting Data and MultiIndex / Advanced Indexing

Getting

Selecting a single column, which yields a Series, equivalent to df.A

In [23]: df['A']Out[23]: 2013-01-01    0.4691122013-01-02    1.2121122013-01-03   -0.8618492013-01-04    0.7215552013-01-05   -0.4249722013-01-06   -0.673690Freq: D, Name: A, dtype: float64

Selecting via [], which slices the rows.

In [24]: df[0:3]Out[24]:                    A         B         C         D2013-01-01  0.469112 -0.282863 -1.509059 -1.1356322013-01-02  1.212112 -0.173215  0.119209 -1.0442362013-01-03 -0.861849 -2.104569 -0.494929  1.071804In [25]: df['20130102':'20130104']Out[25]:                    A         B         C         D2013-01-02  1.212112 -0.173215  0.119209 -1.0442362013-01-03 -0.861849 -2.104569 -0.494929  1.0718042013-01-04  0.721555 -0.706771 -1.039575  0.271860

Selection by Label

See more in Selection by Label

For getting a cross section using a label

In [26]: df.loc[dates[0]]Out[26]: A    0.469112B   -0.282863C   -1.509059D   -1.135632Name: 2013-01-01 00:00:00, dtype: float64

Selecting on a multi-axis by label

In [27]: df.loc[:,['A','B']]Out[27]:                    A         B2013-01-01  0.469112 -0.2828632013-01-02  1.212112 -0.1732152013-01-03 -0.861849 -2.1045692013-01-04  0.721555 -0.7067712013-01-05 -0.424972  0.5670202013-01-06 -0.673690  0.113648

Showing label slicing, both endpoints are included

In [28]: df.loc['20130102':'20130104',['A','B']]Out[28]:                    A         B2013-01-02  1.212112 -0.1732152013-01-03 -0.861849 -2.1045692013-01-04  0.721555 -0.706771

Reduction in the dimensions of the returned object

In [29]: df.loc['20130102',['A','B']]Out[29]: A    1.212112B   -0.173215Name: 2013-01-02 00:00:00, dtype: float64

For getting a scalar value

In [30]: df.loc[dates[0],'A']Out[30]: 0.46911229990718628

For getting fast access to a scalar (equiv to the prior method)

In [31]: df.at[dates[0],'A']Out[31]: 0.46911229990718628

Selection by Position

See more in Selection by Position

Select via the position of the passed integers

In [32]: df.iloc[3]Out[32]: A    0.721555B   -0.706771C   -1.039575D    0.271860Name: 2013-01-04 00:00:00, dtype: float64

By integer slices, acting similar to numpy/python

In [33]: df.iloc[3:5,0:2]Out[33]:                    A         B2013-01-04  0.721555 -0.7067712013-01-05 -0.424972  0.567020

By lists of integer position locations, similar to the numpy/python style

In [34]: df.iloc[[1,2,4],[0,2]]Out[34]:                    A         C2013-01-02  1.212112  0.1192092013-01-03 -0.861849 -0.4949292013-01-05 -0.424972  0.276232

For slicing rows explicitly

In [35]: df.iloc[1:3,:]Out[35]:                    A         B         C         D2013-01-02  1.212112 -0.173215  0.119209 -1.0442362013-01-03 -0.861849 -2.104569 -0.494929  1.071804

For slicing columns explicitly

In [36]: df.iloc[:,1:3]Out[36]:                    B         C2013-01-01 -0.282863 -1.5090592013-01-02 -0.173215  0.1192092013-01-03 -2.104569 -0.4949292013-01-04 -0.706771 -1.0395752013-01-05  0.567020  0.2762322013-01-06  0.113648 -1.478427

For getting a value explicitly

In [37]: df.iloc[1,1]Out[37]: -0.17321464905330858

For getting fast access to a scalar (equiv to the prior method)

In [38]: df.iat[1,1]Out[38]: -0.17321464905330858

Boolean Indexing

Using a single column’s values to select data.

In [39]: df[df.A > 0]Out[39]:                    A         B         C         D2013-01-01  0.469112 -0.282863 -1.509059 -1.1356322013-01-02  1.212112 -0.173215  0.119209 -1.0442362013-01-04  0.721555 -0.706771 -1.039575  0.271860

Selecting values from a DataFrame where a boolean condition is met.

In [40]: df[df > 0]Out[40]:                    A         B         C         D2013-01-01  0.469112       NaN       NaN       NaN2013-01-02  1.212112       NaN  0.119209       NaN2013-01-03       NaN       NaN       NaN  1.0718042013-01-04  0.721555       NaN       NaN  0.2718602013-01-05       NaN  0.567020  0.276232       NaN2013-01-06       NaN  0.113648       NaN  0.524988

Using the isin() method for filtering:

In [41]: df2 = df.copy()In [42]: df2['E'] = ['one', 'one','two','three','four','three']In [43]: df2Out[43]:                    A         B         C         D      E2013-01-01  0.469112 -0.282863 -1.509059 -1.135632    one2013-01-02  1.212112 -0.173215  0.119209 -1.044236    one2013-01-03 -0.861849 -2.104569 -0.494929  1.071804    two2013-01-04  0.721555 -0.706771 -1.039575  0.271860  three2013-01-05 -0.424972  0.567020  0.276232 -1.087401   four2013-01-06 -0.673690  0.113648 -1.478427  0.524988  threeIn [44]: df2[df2['E'].isin(['two','four'])]Out[44]:                    A         B         C         D     E2013-01-03 -0.861849 -2.104569 -0.494929  1.071804   two2013-01-05 -0.424972  0.567020  0.276232 -1.087401  four

Setting

Setting a new column automatically aligns the data by the indexes

In [45]: s1 = pd.Series([1,2,3,4,5,6], index=pd.date_range('20130102', periods=6))In [46]: s1Out[46]: 2013-01-02    12013-01-03    22013-01-04    32013-01-05    42013-01-06    52013-01-07    6Freq: D, dtype: int64In [47]: df['F'] = s1

Setting values by label

In [48]: df.at[dates[0],'A'] = 0

Setting values by position

In [49]: df.iat[0,1] = 0

Setting by assigning with a numpy array

In [50]: df.loc[:,'D'] = np.array([5] * len(df))

The result of the prior setting operations

In [51]: dfOut[51]:                    A         B         C  D    F2013-01-01  0.000000  0.000000 -1.509059  5  NaN2013-01-02  1.212112 -0.173215  0.119209  5  1.02013-01-03 -0.861849 -2.104569 -0.494929  5  2.02013-01-04  0.721555 -0.706771 -1.039575  5  3.02013-01-05 -0.424972  0.567020  0.276232  5  4.02013-01-06 -0.673690  0.113648 -1.478427  5  5.0

A where operation with setting.

In [52]: df2 = df.copy()In [53]: df2[df2 > 0] = -df2In [54]: df2Out[54]:                    A         B         C  D    F2013-01-01  0.000000  0.000000 -1.509059 -5  NaN2013-01-02 -1.212112 -0.173215 -0.119209 -5 -1.02013-01-03 -0.861849 -2.104569 -0.494929 -5 -2.02013-01-04 -0.721555 -0.706771 -1.039575 -5 -3.02013-01-05 -0.424972 -0.567020 -0.276232 -5 -4.02013-01-06 -0.673690 -0.113648 -1.478427 -5 -5.0

Missing Data

pandas primarily uses the value np.nan to represent missing data. It is by default not included in computations. See the Missing Data section

Reindexing allows you to change/add/delete the index on a specified axis. This returns a copy of the data.

In [55]: df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ['E'])In [56]: df1.loc[dates[0]:dates[1],'E'] = 1In [57]: df1Out[57]:                    A         B         C  D    F    E2013-01-01  0.000000  0.000000 -1.509059  5  NaN  1.02013-01-02  1.212112 -0.173215  0.119209  5  1.0  1.02013-01-03 -0.861849 -2.104569 -0.494929  5  2.0  NaN2013-01-04  0.721555 -0.706771 -1.039575  5  3.0  NaN

To drop any rows that have missing data.

In [58]: df1.dropna(how='any')Out[58]:                    A         B         C  D    F    E2013-01-02  1.212112 -0.173215  0.119209  5  1.0  1.0

Filling missing data

In [59]: df1.fillna(value=5)Out[59]:                    A         B         C  D    F    E2013-01-01  0.000000  0.000000 -1.509059  5  5.0  1.02013-01-02  1.212112 -0.173215  0.119209  5  1.0  1.02013-01-03 -0.861849 -2.104569 -0.494929  5  2.0  5.02013-01-04  0.721555 -0.706771 -1.039575  5  3.0  5.0

To get the boolean mask where values are nan

In [60]: pd.isnull(df1)Out[60]:                 A      B      C      D      F      E2013-01-01  False  False  False  False   True  False2013-01-02  False  False  False  False  False  False2013-01-03  False  False  False  False  False   True2013-01-04  False  False  False  False  False   True

Operations

See the Basic section on Binary Ops

Stats

Operations in general exclude missing data.

Performing a descriptive statistic

In [61]: df.mean()Out[61]: A   -0.004474B   -0.383981C   -0.687758D    5.000000F    3.000000dtype: float64

Same operation on the other axis

In [62]: df.mean(1)Out[62]: 2013-01-01    0.8727352013-01-02    1.4316212013-01-03    0.7077312013-01-04    1.3950422013-01-05    1.8836562013-01-06    1.592306Freq: D, dtype: float64

Operating with objects that have different dimensionality and need alignment. In addition, pandas automatically broadcasts along the specified dimension.

In [63]: s = pd.Series([1,3,5,np.nan,6,8], index=dates).shift(2)In [64]: sOut[64]: 2013-01-01    NaN2013-01-02    NaN2013-01-03    1.02013-01-04    3.02013-01-05    5.02013-01-06    NaNFreq: D, dtype: float64In [65]: df.sub(s, axis='index')Out[65]:                    A         B         C    D    F2013-01-01       NaN       NaN       NaN  NaN  NaN2013-01-02       NaN       NaN       NaN  NaN  NaN2013-01-03 -1.861849 -3.104569 -1.494929  4.0  1.02013-01-04 -2.278445 -3.706771 -4.039575  2.0  0.02013-01-05 -5.424972 -4.432980 -4.723768  0.0 -1.02013-01-06       NaN       NaN       NaN  NaN  NaN

Apply

Applying functions to the data

In [66]: df.apply(np.cumsum)Out[66]:                    A         B         C   D     F2013-01-01  0.000000  0.000000 -1.509059   5   NaN2013-01-02  1.212112 -0.173215 -1.389850  10   1.02013-01-03  0.350263 -2.277784 -1.884779  15   3.02013-01-04  1.071818 -2.984555 -2.924354  20   6.02013-01-05  0.646846 -2.417535 -2.648122  25  10.02013-01-06 -0.026844 -2.303886 -4.126549  30  15.0In [67]: df.apply(lambda x: x.max() - x.min())Out[67]: A    2.073961B    2.671590C    1.785291D    0.000000F    4.000000dtype: float64

Histogramming

See more at Histogramming and Discretization

In [68]: s = pd.Series(np.random.randint(0, 7, size=10))In [69]: sOut[69]: 0    41    22    13    24    65    46    47    68    49    4dtype: int64In [70]: s.value_counts()Out[70]: 4    56    22    21    1dtype: int64

String Methods

Series is equipped with a set of string processing methods in the str attribute that make it easy to operate on each element of the array, as in the code snippet below. Note that pattern-matching in str generally uses regular expressions by default (and in some cases always uses them). See more at Vectorized String Methods.

In [71]: s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])In [72]: s.str.lower()Out[72]: 0       a1       b2       c3    aaba4    baca5     NaN6    caba7     dog8     catdtype: object

Merge

Concat

pandas provides various facilities for easily combining together Series, DataFrame, and Panel objects with various kinds of set logic for the indexes and relational algebra functionality in the case of join / merge-type operations.

See the Merging section

Concatenating pandas objects together with concat():

In [73]: df = pd.DataFrame(np.random.randn(10, 4))In [74]: dfOut[74]:           0         1         2         30 -0.548702  1.467327 -1.015962 -0.4830751  1.637550 -1.217659 -0.291519 -1.7455052 -0.263952  0.991460 -0.919069  0.2660463 -0.709661  1.669052  1.037882 -1.7057754 -0.919854 -0.042379  1.247642 -0.0099205  0.290213  0.495767  0.362949  1.5481066 -1.131345 -0.089329  0.337863 -0.9458677 -0.932132  1.956030  0.017587 -0.0166928 -0.575247  0.254161 -1.143704  0.2158979  1.193555 -0.077118 -0.408530 -0.862495# break it into piecesIn [75]: pieces = [df[:3], df[3:7], df[7:]]In [76]: pd.concat(pieces)Out[76]:           0         1         2         30 -0.548702  1.467327 -1.015962 -0.4830751  1.637550 -1.217659 -0.291519 -1.7455052 -0.263952  0.991460 -0.919069  0.2660463 -0.709661  1.669052  1.037882 -1.7057754 -0.919854 -0.042379  1.247642 -0.0099205  0.290213  0.495767  0.362949  1.5481066 -1.131345 -0.089329  0.337863 -0.9458677 -0.932132  1.956030  0.017587 -0.0166928 -0.575247  0.254161 -1.143704  0.2158979  1.193555 -0.077118 -0.408530 -0.862495

Join

SQL style merges. See the Database style joining

In [77]: left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})In [78]: right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})In [79]: leftOut[79]:    key  lval0  foo     11  foo     2In [80]: rightOut[80]:    key  rval0  foo     41  foo     5In [81]: pd.merge(left, right, on='key')Out[81]:    key  lval  rval0  foo     1     41  foo     1     52  foo     2     43  foo     2     5

Another example that can be given is:

In [82]: left = pd.DataFrame({'key': ['foo', 'bar'], 'lval': [1, 2]})In [83]: right = pd.DataFrame({'key': ['foo', 'bar'], 'rval': [4, 5]})In [84]: leftOut[84]:    key  lval0  foo     11  bar     2In [85]: rightOut[85]:    key  rval0  foo     41  bar     5In [86]: pd.merge(left, right, on='key')Out[86]:    key  lval  rval0  foo     1     41  bar     2     5

Append

Append rows to a dataframe. See the Appending

In [87]: df = pd.DataFrame(np.random.randn(8, 4), columns=['A','B','C','D'])In [88]: dfOut[88]:           A         B         C         D0  1.346061  1.511763  1.627081 -0.9905821 -0.441652  1.211526  0.268520  0.0245802 -1.577585  0.396823 -0.105381 -0.5325323  1.453749  1.208843 -0.080952 -0.2646104 -0.727965 -0.589346  0.339969 -0.6932055 -0.339355  0.593616  0.884345  1.5914316  0.141809  0.220390  0.435589  0.1924517 -0.096701  0.803351  1.715071 -0.708758In [89]: s = df.iloc[3]In [90]: df.append(s, ignore_index=True)Out[90]:           A         B         C         D0  1.346061  1.511763  1.627081 -0.9905821 -0.441652  1.211526  0.268520  0.0245802 -1.577585  0.396823 -0.105381 -0.5325323  1.453749  1.208843 -0.080952 -0.2646104 -0.727965 -0.589346  0.339969 -0.6932055 -0.339355  0.593616  0.884345  1.5914316  0.141809  0.220390  0.435589  0.1924517 -0.096701  0.803351  1.715071 -0.7087588  1.453749  1.208843 -0.080952 -0.264610

Grouping

By “group by” we are referring to a process involving one or more of the following steps

• Splitting the data into groups based on some criteria
• Applying a function to each group independently
• Combining the results into a data structure

See the Grouping section

In [91]: df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',   ....:                           'foo', 'bar', 'foo', 'foo'],   ....:                    'B' : ['one', 'one', 'two', 'three',   ....:                           'two', 'two', 'one', 'three'],   ....:                    'C' : np.random.randn(8),   ....:                    'D' : np.random.randn(8)})   ....: In [92]: dfOut[92]:      A      B         C         D0  foo    one -1.202872 -0.0552241  bar    one -1.814470  2.3959852  foo    two  1.018601  1.5528253  bar  three -0.595447  0.1665994  foo    two  1.395433  0.0476095  bar    two -0.392670 -0.1364736  foo    one  0.007207 -0.5617577  foo  three  1.928123 -1.623033

Grouping and then applying a function sum to the resulting groups.

In [93]: df.groupby('A').sum()Out[93]:             C        DA                     bar -2.802588  2.42611foo  3.146492 -0.63958

Grouping by multiple columns forms a hierarchical index, which we then apply the function.

In [94]: df.groupby(['A','B']).sum()Out[94]:                   C         DA   B                        bar one   -1.814470  2.395985    three -0.595447  0.166599    two   -0.392670 -0.136473foo one   -1.195665 -0.616981    three  1.928123 -1.623033    two    2.414034  1.600434

Reshaping

See the sections on Hierarchical Indexing and Reshaping.

Stack

In [95]: tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',   ....:                      'foo', 'foo', 'qux', 'qux'],   ....:                     ['one', 'two', 'one', 'two',   ....:                      'one', 'two', 'one', 'two']]))   ....: In [96]: index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])In [97]: df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])In [98]: df2 = df[:4]In [99]: df2Out[99]:                      A         Bfirst second                    bar   one     0.029399 -0.542108      two     0.282696 -0.087302baz   one    -1.575170  1.771208      two     0.816482  1.100230

The stack() method “compresses” a level in the DataFrame’s columns.

In [100]: stacked = df2.stack()In [101]: stackedOut[101]: first  second   bar    one     A    0.029399               B   -0.542108       two     A    0.282696               B   -0.087302baz    one     A   -1.575170               B    1.771208       two     A    0.816482               B    1.100230dtype: float64

With a “stacked” DataFrame or Series (having a MultiIndex as the index), the inverse operation of stack() is unstack(), which by default unstacks the last level:

In [102]: stacked.unstack()Out[102]:                      A         Bfirst second                    bar   one     0.029399 -0.542108      two     0.282696 -0.087302baz   one    -1.575170  1.771208      two     0.816482  1.100230In [103]: stacked.unstack(1)Out[103]: second        one       twofirst                      bar   A  0.029399  0.282696      B -0.542108 -0.087302baz   A -1.575170  0.816482      B  1.771208  1.100230In [104]: stacked.unstack(0)Out[104]: first          bar       bazsecond                      one    A  0.029399 -1.575170       B -0.542108  1.771208two    A  0.282696  0.816482       B -0.087302  1.100230

Pivot Tables

See the section on Pivot Tables.

In [105]: df = pd.DataFrame({'A' : ['one', 'one', 'two', 'three'] * 3,   .....:                    'B' : ['A', 'B', 'C'] * 4,   .....:                    'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,   .....:                    'D' : np.random.randn(12),   .....:                    'E' : np.random.randn(12)})   .....: In [106]: dfOut[106]:         A  B    C         D         E0     one  A  foo  1.418757 -0.1796661     one  B  foo -1.879024  1.2918362     two  C  foo  0.536826 -0.0096143   three  A  bar  1.006160  0.3921494     one  B  bar -0.029716  0.2645995     one  C  bar -1.146178 -0.0574096     two  A  foo  0.100900 -1.4256387   three  B  foo -1.035018  1.0240988     one  C  foo  0.314665 -0.1060629     one  A  bar -0.773723  1.82437510    two  B  bar -1.170653  0.59597411  three  C  bar  0.648740  1.167115

We can produce pivot tables from this data very easily:

In [107]: pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C'])Out[107]: C             bar       fooA     B                    one   A -0.773723  1.418757      B -0.029716 -1.879024      C -1.146178  0.314665three A  1.006160       NaN      B       NaN -1.035018      C  0.648740       NaNtwo   A       NaN  0.100900      B -1.170653       NaN      C       NaN  0.536826

Time Series

pandas has simple, powerful, and efficient functionality for performing resampling operations during frequency conversion (e.g., converting secondly data into 5-minutely data). This is extremely common in, but not limited to, financial applications. See the Time Series section

In [108]: rng = pd.date_range('1/1/2012', periods=100, freq='S')In [109]: ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)In [110]: ts.resample('5Min').sum()Out[110]: 2012-01-01    25083Freq: 5T, dtype: int64

Time zone representation

In [111]: rng = pd.date_range('3/6/2012 00:00', periods=5, freq='D')In [112]: ts = pd.Series(np.random.randn(len(rng)), rng)In [113]: tsOut[113]: 2012-03-06    0.4640002012-03-07    0.2273712012-03-08   -0.4969222012-03-09    0.3063892012-03-10   -2.290613Freq: D, dtype: float64In [114]: ts_utc = ts.tz_localize('UTC')In [115]: ts_utcOut[115]: 2012-03-06 00:00:00+00:00    0.4640002012-03-07 00:00:00+00:00    0.2273712012-03-08 00:00:00+00:00   -0.4969222012-03-09 00:00:00+00:00    0.3063892012-03-10 00:00:00+00:00   -2.290613Freq: D, dtype: float64

Convert to another time zone

In [116]: ts_utc.tz_convert('US/Eastern')Out[116]: 2012-03-05 19:00:00-05:00    0.4640002012-03-06 19:00:00-05:00    0.2273712012-03-07 19:00:00-05:00   -0.4969222012-03-08 19:00:00-05:00    0.3063892012-03-09 19:00:00-05:00   -2.290613Freq: D, dtype: float64

Converting between time span representations

In [117]: rng = pd.date_range('1/1/2012', periods=5, freq='M')In [118]: ts = pd.Series(np.random.randn(len(rng)), index=rng)In [119]: tsOut[119]: 2012-01-31   -1.1346232012-02-29   -1.5618192012-03-31   -0.2608382012-04-30    0.2819572012-05-31    1.523962Freq: M, dtype: float64In [120]: ps = ts.to_period()In [121]: psOut[121]: 2012-01   -1.1346232012-02   -1.5618192012-03   -0.2608382012-04    0.2819572012-05    1.523962Freq: M, dtype: float64In [122]: ps.to_timestamp()Out[122]: 2012-01-01   -1.1346232012-02-01   -1.5618192012-03-01   -0.2608382012-04-01    0.2819572012-05-01    1.523962Freq: MS, dtype: float64

Converting between period and timestamp enables some convenient arithmetic functions to be used. In the following example, we convert a quarterly frequency with year ending in November to 9am of the end of the month following the quarter end:

In [123]: prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV')In [124]: ts = pd.Series(np.random.randn(len(prng)), prng)In [125]: ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9In [126]: ts.head()Out[126]: 1990-03-01 09:00   -0.9029371990-06-01 09:00    0.0681591990-09-01 09:00   -0.0578731990-12-01 09:00   -0.3682041991-03-01 09:00   -1.144073Freq: H, dtype: float64

Categoricals

Since version 0.15, pandas can include categorical data in a DataFrame. For full docs, see the categorical introductionand the API documentation.

In [127]: df = pd.DataFrame({"id":[1,2,3,4,5,6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']})

Convert the raw grades to a categorical data type.

In [128]: df["grade"] = df["raw_grade"].astype("category")In [129]: df["grade"]Out[129]: 0    a1    b2    b3    a4    a5    eName: grade, dtype: categoryCategories (3, object): [a, b, e]

Rename the categories to more meaningful names (assigning to Series.cat.categories is inplace!)

In [130]: df["grade"].cat.categories = ["very good", "good", "very bad"]

Reorder the categories and simultaneously add the missing categories (methods under Series .cat return a new Series per default).

In [131]: df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium", "good", "very good"])In [132]: df["grade"]Out[132]: 0    very good1         good2         good3    very good4    very good5     very badName: grade, dtype: categoryCategories (5, object): [very bad, bad, medium, good, very good]

Sorting is per order in the categories, not lexical order.

In [133]: df.sort_values(by="grade")Out[133]:    id raw_grade      grade5   6         e   very bad1   2         b       good2   3         b       good0   1         a  very good3   4         a  very good4   5         a  very good

Grouping by a categorical column shows also empty categories.

In [134]: df.groupby("grade").size()Out[134]: gradevery bad     1bad          0medium       0good         2very good    3dtype: int64

Plotting

Plotting docs.

In [135]: ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))In [136]: ts = ts.cumsum()In [137]: ts.plot()Out[137]: <matplotlib.axes._subplots.AxesSubplot at 0x1187d7278>

On DataFrame, plot() is a convenience to plot all of the columns with labels:

In [138]: df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,   .....:                   columns=['A', 'B', 'C', 'D'])   .....: In [139]: df = df.cumsum()In [140]: plt.figure(); df.plot(); plt.legend(loc='best')Out[140]: <matplotlib.legend.Legend at 0x11b5dea20>

Getting Data In/Out

CSV

Writing to a csv file

In [141]: df.to_csv('foo.csv')

Reading from a csv file

In [142]: pd.read_csv('foo.csv')Out[142]:      Unnamed: 0          A          B         C          D0    2000-01-01   0.266457  -0.399641 -0.219582   1.1868601    2000-01-02  -1.170732  -0.345873  1.653061  -0.2829532    2000-01-03  -1.734933   0.530468  2.060811  -0.5155363    2000-01-04  -1.555121   1.452620  0.239859  -1.1568964    2000-01-05   0.578117   0.511371  0.103552  -2.4282025    2000-01-06   0.478344   0.449933 -0.741620  -1.9624096    2000-01-07   1.235339  -0.091757 -1.543861  -1.084753..          ...        ...        ...       ...        ...993  2002-09-20 -10.628548  -9.153563 -7.883146  28.313940994  2002-09-21 -10.390377  -8.727491 -6.399645  30.914107995  2002-09-22  -8.985362  -8.485624 -4.669462  31.367740996  2002-09-23  -9.558560  -8.781216 -4.499815  30.518439997  2002-09-24  -9.902058  -9.340490 -4.386639  30.105593998  2002-09-25 -10.216020  -9.480682 -3.933802  29.758560999  2002-09-26 -11.856774 -10.671012 -3.216025  29.369368[1000 rows x 5 columns]

HDF5

Reading and writing to HDFStores

Writing to a HDF5 Store

In [143]: df.to_hdf('foo.h5','df')

Reading from a HDF5 Store

In [144]: pd.read_hdf('foo.h5','df')Out[144]:                     A          B         C          D2000-01-01   0.266457  -0.399641 -0.219582   1.1868602000-01-02  -1.170732  -0.345873  1.653061  -0.2829532000-01-03  -1.734933   0.530468  2.060811  -0.5155362000-01-04  -1.555121   1.452620  0.239859  -1.1568962000-01-05   0.578117   0.511371  0.103552  -2.4282022000-01-06   0.478344   0.449933 -0.741620  -1.9624092000-01-07   1.235339  -0.091757 -1.543861  -1.084753...               ...        ...       ...        ...2002-09-20 -10.628548  -9.153563 -7.883146  28.3139402002-09-21 -10.390377  -8.727491 -6.399645  30.9141072002-09-22  -8.985362  -8.485624 -4.669462  31.3677402002-09-23  -9.558560  -8.781216 -4.499815  30.5184392002-09-24  -9.902058  -9.340490 -4.386639  30.1055932002-09-25 -10.216020  -9.480682 -3.933802  29.7585602002-09-26 -11.856774 -10.671012 -3.216025  29.369368[1000 rows x 4 columns]

Excel

Reading and writing to MS Excel

Writing to an excel file

In [145]: df.to_excel('foo.xlsx', sheet_name='Sheet1')

Reading from an excel file

In [146]: pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA'])Out[146]:                     A          B         C          D2000-01-01   0.266457  -0.399641 -0.219582   1.1868602000-01-02  -1.170732  -0.345873  1.653061  -0.2829532000-01-03  -1.734933   0.530468  2.060811  -0.5155362000-01-04  -1.555121   1.452620  0.239859  -1.1568962000-01-05   0.578117   0.511371  0.103552  -2.4282022000-01-06   0.478344   0.449933 -0.741620  -1.9624092000-01-07   1.235339  -0.091757 -1.543861  -1.084753...               ...        ...       ...        ...2002-09-20 -10.628548  -9.153563 -7.883146  28.3139402002-09-21 -10.390377  -8.727491 -6.399645  30.9141072002-09-22  -8.985362  -8.485624 -4.669462  31.3677402002-09-23  -9.558560  -8.781216 -4.499815  30.5184392002-09-24  -9.902058  -9.340490 -4.386639  30.1055932002-09-25 -10.216020  -9.480682 -3.933802  29.7585602002-09-26 -11.856774 -10.671012 -3.216025  29.369368[1000 rows x 4 columns]

Gotchas

If you are trying an operation and you see an exception like:

>>> if pd.Series([False, True, False]):    print("I was true")Traceback    ...ValueError: The truth value of an array is ambiguous. Use a.empty, a.any() or a.all().

See Comparisons for an explanation and what to do.

http://www.cnblogs.com/chaosimple/p/4153083.html

本文是对pandas官方网站上《10 Minutes to pandas》的一个简单的翻译，原文在这里。这篇文章是对pandas的一个简单的介绍，详细的介绍请参考：Cookbook 。习惯上，我们会按下面格式引入所需要的包：

一、            创建对象

可以通过 Data Structure Intro Setion 来查看有关该节内容的详细信息。

1、可以通过传递一个list对象来创建一个Series，pandas会默认创建整型索引：

2、通过传递一个numpy array，时间索引以及列标签来创建一个DataFrame：

3、通过传递一个能够被转换成类似序列结构的字典对象来创建一个DataFrame：

4、查看不同列的数据类型：

5、如果你使用的是IPython，使用Tab自动补全功能会自动识别所有的属性以及自定义的列，下图中是所有能够被自动识别的属性的一个子集：

二、            查看数据

详情请参阅：Basics Section

 

1、  查看frame中头部和尾部的行：

2、  显示索引、列和底层的numpy数据：

3、  describe()函数对于数据的快速统计汇总：

4、  对数据的转置：

5、  按轴进行排序

6、  按值进行排序

三、            选择

虽然标准的Python/Numpy的选择和设置表达式都能够直接派上用场，但是作为工程使用的代码，我们推荐使用经过优化的pandas数据访问方式： .at, .iat, .loc, .iloc 和 .ix详情请参阅Indexing and Selecing Data 和 MultiIndex / Advanced Indexing。

l  获取

1、 选择一个单独的列，这将会返回一个Series，等同于df.A：

2、 通过[]进行选择，这将会对行进行切片

l  通过标签选择

1、 使用标签来获取一个交叉的区域

2、 通过标签来在多个轴上进行选择

3、 标签切片

4、 对于返回的对象进行维度缩减

5、 获取一个标量

6、 快速访问一个标量（与上一个方法等价）

l  通过位置选择

1、 通过传递数值进行位置选择（选择的是行）

2、 通过数值进行切片，与numpy/python中的情况类似

3、 通过指定一个位置的列表，与numpy/python中的情况类似

4、 对行进行切片

5、 对列进行切片

6、 获取特定的值

l  布尔索引

1、 使用一个单独列的值来选择数据：

2、 使用where操作来选择数据：

3、 使用isin()方法来过滤：

 

l  设置

1、 设置一个新的列：

2、 通过标签设置新的值：

3、 通过位置设置新的值：

4、 通过一个numpy数组设置一组新值：

上述操作结果如下：

5、 通过where操作来设置新的值：

四、            缺失值处理

在pandas中，使用np.nan来代替缺失值，这些值将默认不会包含在计算中，详情请参阅：Missing Data Section。

1、  reindex()方法可以对指定轴上的索引进行改变/增加/删除操作，这将返回原始数据的一个拷贝：、

2、  去掉包含缺失值的行：

3、  对缺失值进行填充：

4、  对数据进行布尔填充：

五、            相关操作

详情请参与 Basic Section On Binary Ops

l  统计（相关操作通常情况下不包括缺失值）

1、  执行描述性统计：

2、  在其他轴上进行相同的操作：

3、  对于拥有不同维度，需要对齐的对象进行操作。Pandas会自动的沿着指定的维度进行广播：

l  Apply

1、  对数据应用函数：

l  直方图

具体请参照：Histogramming and Discretization

 

l  字符串方法

Series对象在其str属性中配备了一组字符串处理方法，可以很容易的应用到数组中的每个元素，如下段代码所示。更多详情请参考：Vectorized String Methods.

六、            合并

Pandas提供了大量的方法能够轻松的对Series，DataFrame和Panel对象进行各种符合各种逻辑关系的合并操作。具体请参阅：Merging section

l  Concat

l  Join 类似于SQL类型的合并，具体请参阅：Database style joining

l  Append 将一行连接到一个DataFrame上，具体请参阅Appending：

七、            分组

对于”group by”操作，我们通常是指以下一个或多个操作步骤：

l  （Splitting）按照一些规则将数据分为不同的组；

l  （Applying）对于每组数据分别执行一个函数；

l  （Combining）将结果组合到一个数据结构中；

详情请参阅：Grouping section

1、  分组并对每个分组执行sum函数：

2、  通过多个列进行分组形成一个层次索引，然后执行函数：

八、            Reshaping

详情请参阅 Hierarchical Indexing 和 Reshaping。

l  Stack

l  数据透视表，详情请参阅：Pivot Tables.

可以从这个数据中轻松的生成数据透视表：

九、            时间序列

Pandas在对频率转换进行重新采样时拥有简单、强大且高效的功能（如将按秒采样的数据转换为按5分钟为单位进行采样的数据）。这种操作在金融领域非常常见。具体参考：Time Series section。

1、  时区表示：

2、  时区转换：

3、  时间跨度转换：

4、  时期和时间戳之间的转换使得可以使用一些方便的算术函数。

十、            Categorical

从0.15版本开始，pandas可以在DataFrame中支持Categorical类型的数据，详细 介绍参看：categorical introduction和API documentation。

1、  将原始的grade转换为Categorical数据类型：

2、  将Categorical类型数据重命名为更有意义的名称：

3、  对类别进行重新排序，增加缺失的类别：

4、  排序是按照Categorical的顺序进行的而不是按照字典顺序进行：

5、  对Categorical列进行排序时存在空的类别：

十一、           画图

具体文档参看：Plotting docs

对于DataFrame来说，plot是一种将所有列及其标签进行绘制的简便方法：

十二、           导入和保存数据

l  CSV，参考：Writing to a csv file

1、  写入csv文件：

2、  从csv文件中读取：

l  HDF5，参考：HDFStores

1、  写入HDF5存储：

2、  从HDF5存储中读取：

l  Excel，参考：MS Excel

1、  写入excel文件：

2、  从excel文件中读取：