三、pandas

原文出處： pandas.pydata.org   譯文出處：石卓林   

這是關於pandas的簡短介紹，主要面向新用戶。能夠參閱Cookbook瞭解更復雜的使用方法。

連接：http://python.jobbole.com/84416/

習慣上,咱們作如下導入

Python

 

1 2 3

In [1]: import pandas as pd In [2]: import numpy as np In [3]: import matplotlib.pyplot as plt

 

建立對象

使用傳遞的值列表序列建立序列, 讓pandas建立默認整數索引

Python

 

1 2 3 4 5 6 7 8 9 10

In [4]: s = pd.Series([1,3,5,np.nan,6,8]) In [5]: s Out[5]: 0     1 1     3 2     5 3   NaN 4     6 5     8 dtype: float64

使用傳遞的numpy數組建立數據幀,並使用日期索引和標記列.

Python

 

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In [6]: dates = pd.date_range('20130101',periods=6) In [7]: dates Out[7]: <class 'pandas.tseries.index.DatetimeIndex'> [2013-01-01, ..., 2013-01-06] Length: 6, Freq: D, Timezone: None   In [8]: df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=list('ABCD')) In [9]: df Out[9]:                    A         B         C         D 2013-01-01  0.469112 -0.282863 -1.509059 -1.135632 2013-01-02  1.212112 -0.173215  0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929  1.071804 2013-01-04  0.721555 -0.706771 -1.039575  0.271860 2013-01-05 -0.424972  0.567020  0.276232 -1.087401 2013-01-06 -0.673690  0.113648 -1.478427  0.524988

使用傳遞的可轉換序列的字典對象建立數據幀.

Python

 

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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]: df2 Out[11]:    A          B  C  D      E    F 0  1 2013-01-02  1  3   test  foo 1  1 2013-01-02  1  3  train  foo 2  1 2013-01-02  1  3   test  foo 3  1 2013-01-02  1  3  train  foo

全部明確類型

Python

 

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In [12]: df2.dtypes Out[12]: A           float64 B    datetime64[ns] C           float32 D             int32 E          category F            object dtype: object

若是你這個正在使用IPython，標籤補全列名（以及公共屬性）將自動啓用。這裏是將要完成的屬性的子集：

Python

 

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In [13]: df2.<TAB> df2.A                  df2.boxplot df2.abs                df2.C df2.add                df2.clip df2.add_prefix         df2.clip_lower df2.add_suffix         df2.clip_upper df2.align              df2.columns df2.all                df2.combine df2.any                df2.combineAdd df2.append             df2.combine_first df2.apply              df2.combineMult df2.applymap           df2.compound df2.as_blocks          df2.consolidate df2.asfreq             df2.convert_objects df2.as_matrix          df2.copy df2.astype             df2.corr df2.at                 df2.corrwith df2.at_time            df2.count df2.axes               df2.cov df2.B                  df2.cummax df2.between_time       df2.cummin df2.bfill              df2.cumprod df2.blocks             df2.cumsum df2.bool               df2.D

如你所見, 列 A, B, C, 和 D 也是自動完成標籤. E 也是可用的; 爲了簡便起見,後面的屬性顯示被截斷.

查看數據

參閱基礎部分

查看幀頂部和底部行

 

Python

 

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In [14]: df.head() Out[14]:                    A         B         C         D 2013-01-01  0.469112 -0.282863 -1.509059 -1.135632 2013-01-02  1.212112 -0.173215  0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929  1.071804 2013-01-04  0.721555 -0.706771 -1.039575  0.271860 2013-01-05 -0.424972  0.567020  0.276232 -1.087401   In [15]: df.tail(3) Out[15]:                    A         B         C         D 2013-01-04  0.721555 -0.706771 -1.039575  0.271860 2013-01-05 -0.424972  0.567020  0.276232 -1.087401 2013-01-06 -0.673690  0.113648 -1.478427  0.524988

顯示索引,列,和底層numpy數據

 

Python

 

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In [16]: df.index Out[16]: <class 'pandas.tseries.index.DatetimeIndex'> [2013-01-01, ..., 2013-01-06] Length: 6, Freq: D, Timezone: None   In [17]: df.columns Out[17]: Index([u'A', u'B', u'C', u'D'], dtype='object')   In [18]: df.values Out[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 ]])

描述顯示數據快速統計摘要

Python

 

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In [19]: df.describe() Out[19]:               A         B         C         D count  6.000000  6.000000  6.000000  6.000000 mean   0.073711 -0.431125 -0.687758 -0.233103 std    0.843157  0.922818  0.779887  0.973118 min   -0.861849 -2.104569 -1.509059 -1.135632 25%   -0.611510 -0.600794 -1.368714 -1.076610 50%    0.022070 -0.228039 -0.767252 -0.386188 75%    0.658444  0.041933 -0.034326  0.461706 max    1.212112  0.567020  0.276232  1.071804

轉置數據

Python

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

按軸排序

Python

 

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In [21]: df.sort_index(axis=1, ascending=False) Out[21]:                    D         C         B         A 2013-01-01 -1.135632 -1.509059 -0.282863  0.469112 2013-01-02 -1.044236  0.119209 -0.173215  1.212112 2013-01-03  1.071804 -0.494929 -2.104569 -0.861849 2013-01-04  0.271860 -1.039575 -0.706771  0.721555 2013-01-05 -1.087401  0.276232  0.567020 -0.424972 2013-01-06  0.524988 -1.478427  0.113648 -0.673690

按值排序

Python

 

1 2 3 4 5 6 7 8 9

In [22]: df.sort(columns='B') Out[22]:                    A         B         C         D 2013-01-03 -0.861849 -2.104569 -0.494929  1.071804 2013-01-04  0.721555 -0.706771 -1.039575  0.271860 2013-01-01  0.469112 -0.282863 -1.509059 -1.135632 2013-01-02  1.212112 -0.173215  0.119209 -1.044236 2013-01-06 -0.673690  0.113648 -1.478427  0.524988 2013-01-05 -0.424972  0.567020  0.276232 -1.087401

 

選擇器

註釋: 標準Python / Numpy表達式能夠完成這些互動工做, 但在生產代碼中, 咱們推薦使用優化的pandas數據訪問方法, .at, .iat, .loc, .iloc 和 .ix.

參閱索引文檔 索引和選擇數據 and 多索引/高級索引

讀取

選擇單列, 這會產生一個序列, 等價df.A

Python

 

1 2 3 4 5 6 7 8 9

In [23]: df['A'] Out[23]: 2013-01-01    0.469112 2013-01-02    1.212112 2013-01-03   -0.861849 2013-01-04    0.721555 2013-01-05   -0.424972 2013-01-06   -0.673690 Freq: D, Name: A, dtype: float64

使用[]選擇行片段

Python

 

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In [24]: df[0:3] Out[24]:                    A         B         C         D 2013-01-01  0.469112 -0.282863 -1.509059 -1.135632 2013-01-02  1.212112 -0.173215  0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929  1.071804   In [25]: df['20130102':'20130104'] Out[25]:                    A         B         C         D 2013-01-02  1.212112 -0.173215  0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929  1.071804 2013-01-04  0.721555 -0.706771 -1.039575  0.271860

 

使用標籤選擇

更多信息請參閱按標籤選擇

使用標籤獲取橫截面

Python

 

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

使用標籤選擇多軸

Python

 

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In [27]: df.loc[:,['A','B']] Out[27]:                    A         B 2013-01-01  0.469112 -0.282863 2013-01-02  1.212112 -0.173215 2013-01-03 -0.861849 -2.104569 2013-01-04  0.721555 -0.706771 2013-01-05 -0.424972  0.567020 2013-01-06 -0.673690  0.113648

顯示標籤切片, 包含兩個端點

Python

 

1 2 3 4 5 6

In [28]: df.loc['20130102':'20130104',['A','B']] Out[28]:                    A         B 2013-01-02  1.212112 -0.173215 2013-01-03 -0.861849 -2.104569 2013-01-04  0.721555 -0.706771

下降返回對象維度

Python

 

1 2 3 4 5

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

獲取標量值

Python

 

1 2	In [30]: df.loc[dates[0],'A'] Out[30]: 0.46911229990718628

快速訪問並獲取標量數據 (等價上面的方法)

Python

 

1 2	In [31]: df.at[dates[0],'A'] Out[31]: 0.46911229990718628

 

按位置選擇

更多信息請參閱按位置參閱

傳遞整數選擇位置

Python

 

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

使用整數片段,效果相似numpy/python

Python

 

1 2 3 4 5

In [33]: df.iloc[3:5,0:2] Out[33]:                    A         B 2013-01-04  0.721555 -0.706771 2013-01-05 -0.424972  0.567020

使用整數偏移定位列表,效果相似 numpy/python 樣式

Python

 

1 2 3 4 5 6

In [34]: df.iloc[[1,2,4],[0,2]] Out[34]:                    A         C 2013-01-02  1.212112  0.119209 2013-01-03 -0.861849 -0.494929 2013-01-05 -0.424972  0.276232

顯式行切片

Python

 

1 2 3 4 5

In [35]: df.iloc[1:3,:] Out[35]:                    A         B         C         D 2013-01-02  1.212112 -0.173215  0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929  1.071804

顯式列切片

Python

 

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In [36]: df.iloc[:,1:3] Out[36]:                    B         C 2013-01-01 -0.282863 -1.509059 2013-01-02 -0.173215  0.119209 2013-01-03 -2.104569 -0.494929 2013-01-04 -0.706771 -1.039575 2013-01-05  0.567020  0.276232 2013-01-06  0.113648 -1.478427

顯式獲取一個值

Python

 

1 2	In [37]: df.iloc[1,1] Out[37]: -0.17321464905330861

快速訪問一個標量（等同上個方法）

Python

 

1 2	In [38]: df.iat[1,1] Out[38]: -0.17321464905330861

 

布爾索引

使用單個列的值選擇數據.

Python

 

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In [39]: df[df.A > 0] Out[39]:                    A         B         C         D 2013-01-01  0.469112 -0.282863 -1.509059 -1.135632 2013-01-02  1.212112 -0.173215  0.119209 -1.044236 2013-01-04  0.721555 -0.706771 -1.039575  0.271860

where 操做.

Python

 

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In [40]: df[df > 0] Out[40]:                    A         B         C         D 2013-01-01  0.469112       NaN       NaN       NaN 2013-01-02  1.212112       NaN  0.119209       NaN 2013-01-03       NaN       NaN       NaN  1.071804 2013-01-04  0.721555       NaN       NaN  0.271860 2013-01-05       NaN  0.567020  0.276232       NaN 2013-01-06       NaN  0.113648       NaN  0.524988

使用 isin() 篩選：

Python

 

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In [41]: df2 = df.copy() In [42]: df2['E']=['one', 'one','two','three','four','three']   In [43]: df2 Out[43]:                    A         B         C         D      E 2013-01-01  0.469112 -0.282863 -1.509059 -1.135632    one 2013-01-02  1.212112 -0.173215  0.119209 -1.044236    one 2013-01-03 -0.861849 -2.104569 -0.494929  1.071804    two 2013-01-04  0.721555 -0.706771 -1.039575  0.271860  three 2013-01-05 -0.424972  0.567020  0.276232 -1.087401   four 2013-01-06 -0.673690  0.113648 -1.478427  0.524988  three   In [44]: df2[df2['E'].isin(['two','four'])] Out[44]:                    A         B         C         D     E 2013-01-03 -0.861849 -2.104569 -0.494929  1.071804   two 2013-01-05 -0.424972  0.567020  0.276232 -1.087401  four

 

賦值

賦值一個新列，經過索引自動對齊數據

Python

 

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In [45]: s1 = pd.Series([1,2,3,4,5,6],index=pd.date_range('20130102',periods=6)) In [46]: s1 Out[46]: 2013-01-02    1 2013-01-03    2 2013-01-04    3 2013-01-05    4 2013-01-06    5 2013-01-07    6 Freq: D, dtype: int64   In [47]: df['F'] = s1

按標籤賦值

Python

 

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

按位置賦值

Python

 

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

經過numpy數組分配賦值

Python

 

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

以前的操做結果

Python

 

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In [51]: df Out[51]:                    A         B         C  D   F 2013-01-01  0.000000  0.000000 -1.509059  5 NaN 2013-01-02  1.212112 -0.173215  0.119209  5   1 2013-01-03 -0.861849 -2.104569 -0.494929  5   2 2013-01-04  0.721555 -0.706771 -1.039575  5   3 2013-01-05 -0.424972  0.567020  0.276232  5   4 2013-01-06 -0.673690  0.113648 -1.478427  5   5

where 操做賦值.

Python

 

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In [52]: df2 = df.copy() In [53]: df2[df2 > 0] = -df2 In [54]: df2 Out[54]:                    A         B         C  D   F 2013-01-01  0.000000  0.000000 -1.509059 -5 NaN 2013-01-02 -1.212112 -0.173215 -0.119209 -5  -1 2013-01-03 -0.861849 -2.104569 -0.494929 -5  -2 2013-01-04 -0.721555 -0.706771 -1.039575 -5  -3 2013-01-05 -0.424972 -0.567020 -0.276232 -5  -4 2013-01-06 -0.673690 -0.113648 -1.478427 -5  -5

 

丟失的數據

pandas主要使用np.nan替換丟失的數據. 默認狀況下它並不包含在計算中. 請參閱 Missing Data section

重建索引容許更改/添加/刪除指定軸索引,並返回數據副本.

Python

 

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In [55]: df1 = df.reindex(index=dates[0:4],columns=list(df.columns) + ['E']) In [56]: df1.loc[dates[0]:dates[1],'E'] = 1 In [57]: df1 Out[57]:                    A         B         C  D   F   E 2013-01-01  0.000000  0.000000 -1.509059  5 NaN   1 2013-01-02  1.212112 -0.173215  0.119209  5   1   1 2013-01-03 -0.861849 -2.104569 -0.494929  5   2 NaN 2013-01-04  0.721555 -0.706771 -1.039575  5   3 NaN

刪除任何有丟失數據的行.

Python

 

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In [58]: df1.dropna(how='any') Out[58]:                    A         B         C  D  F  E 2013-01-02  1.212112 -0.173215  0.119209  5  1  1

填充丟失數據

Python

 

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In [59]: df1.fillna(value=5) Out[59]:                    A         B         C  D  F  E 2013-01-01  0.000000  0.000000 -1.509059  5  5  1 2013-01-02  1.212112 -0.173215  0.119209  5  1  1 2013-01-03 -0.861849 -2.104569 -0.494929  5  2  5 2013-01-04  0.721555 -0.706771 -1.039575  5  3  5

獲取值是否nan的布爾標記

Python

 

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In [60]: pd.isnull(df1) Out[60]:                 A      B      C      D      F      E 2013-01-01  False  False  False  False   True  False 2013-01-02  False  False  False  False  False  False 2013-01-03  False  False  False  False  False   True 2013-01-04  False  False  False  False  False   True

 

運算

參閱二元運算基礎

統計

計算時通常不包括丟失的數據

執行描述性統計

Python

 

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

在其餘軸作相同的運算

Python

 

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In [62]: df.mean(1) Out[62]: 2013-01-01    0.872735 2013-01-02    1.431621 2013-01-03    0.707731 2013-01-04    1.395042 2013-01-05    1.883656 2013-01-06    1.592306 Freq: D, dtype: float64

用於運算的對象有不一樣的維度並須要對齊.除此以外，pandas會自動沿着指定維度計算.

Python

 

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In [63]: s = pd.Series([1,3,5,np.nan,6,8],index=dates).shift(2) In [64]: s Out[64]: 2013-01-01   NaN 2013-01-02   NaN 2013-01-03     1 2013-01-04     3 2013-01-05     5 2013-01-06   NaN Freq: D, dtype: float64   In [65]: df.sub(s,axis='index') Out[65]:                    A         B         C   D   F 2013-01-01       NaN       NaN       NaN NaN NaN 2013-01-02       NaN       NaN       NaN NaN NaN 2013-01-03 -1.861849 -3.104569 -1.494929   4   1 2013-01-04 -2.278445 -3.706771 -4.039575   2   0 2013-01-05 -5.424972 -4.432980 -4.723768   0  -1 2013-01-06       NaN       NaN       NaN NaN NaN

 

Apply

在數據上使用函數

Python

 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

In [66]: df.apply(np.cumsum) Out[66]:                    A         B         C   D   F 2013-01-01  0.000000  0.000000 -1.509059   5 NaN 2013-01-02  1.212112 -0.173215 -1.389850  10   1 2013-01-03  0.350263 -2.277784 -1.884779  15   3 2013-01-04  1.071818 -2.984555 -2.924354  20   6 2013-01-05  0.646846 -2.417535 -2.648122  25  10 2013-01-06 -0.026844 -2.303886 -4.126549  30  15   In [67]: df.apply(lambda x: x.max() - x.min()) Out[67]: A    2.073961 B    2.671590 C    1.785291 D    0.000000 F    4.000000 dtype: float64

 

直方圖

請參閱 直方圖和離散化

Python

 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

In [68]: s = pd.Series(np.random.randint(0,7,size=10)) In [69]: s Out[69]: 0    4 1    2 2    1 3    2 4    6 5    4 6    4 7    6 8    4 9    4 dtype: int32   In [70]: s.value_counts() Out[70]: 4    5 6    2 2    2 1    1 dtype: int64

 

字符串方法

序列能夠使用一些字符串處理方法很輕易操做數據組中的每一個元素,好比如下代碼片段。 注意字符匹配方法默認狀況下一般使用正則表達式（而且大多數時候都如此）. 更多信息請參閱字符串向量方法.

 

Python

 

1 2 3 4 5 6 7 8 9 10 11 12 13

In [71]: s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat']) In [72]: s.str.lower() Out[72]: 0       a 1       b 2       c 3    aaba 4    baca 5     NaN 6    caba 7     dog 8     cat dtype: object

 

合併

鏈接

pandas提供各類工具以簡便合併序列,數據楨,和組合對象, 在鏈接/合併類型操做中使用多種類型索引和相關數學函數.

請參閱合併部分

把pandas對象鏈接到一塊兒

Python

 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

In [73]: df = pd.DataFrame(np.random.randn(10, 4)) In [74]: df Out[74]:           0         1         2         3 0 -0.548702  1.467327 -1.015962 -0.483075 1  1.637550 -1.217659 -0.291519 -1.745505 2 -0.263952  0.991460 -0.919069  0.266046 3 -0.709661  1.669052  1.037882 -1.705775 4 -0.919854 -0.042379  1.247642 -0.009920 5  0.290213  0.495767  0.362949  1.548106 6 -1.131345 -0.089329  0.337863 -0.945867 7 -0.932132  1.956030  0.017587 -0.016692 8 -0.575247  0.254161 -1.143704  0.215897 9  1.193555 -0.077118 -0.408530 -0.862495   # break it into pieces In [75]: pieces = [df[:3], df[3:7], df[7:]] In [76]: pd.concat(pieces) Out[76]:           0         1         2         3 0 -0.548702  1.467327 -1.015962 -0.483075 1  1.637550 -1.217659 -0.291519 -1.745505 2 -0.263952  0.991460 -0.919069  0.266046 3 -0.709661  1.669052  1.037882 -1.705775 4 -0.919854 -0.042379  1.247642 -0.009920 5  0.290213  0.495767  0.362949  1.548106 6 -1.131345 -0.089329  0.337863 -0.945867 7 -0.932132  1.956030  0.017587 -0.016692 8 -0.575247  0.254161 -1.143704  0.215897 9  1.193555 -0.077118 -0.408530 -0.862495

 

鏈接

SQL樣式合併. 請參閱 數據庫style聯接

Python

 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

In [77]: left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]}) In [78]: right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]}) In [79]: left Out[79]:    key  lval 0  foo     1 1  foo     2   In [80]: right Out[80]:    key  rval 0  foo     4 1  foo     5   In [81]: pd.merge(left, right, on='key') Out[81]:    key  lval  rval 0  foo     1     4 1  foo     1     5 2  foo     2     4 3  foo     2     5

 

添加

添加行到數據增. 參閱 添加

Python

 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

In [82]: df = pd.DataFrame(np.random.randn(8, 4), columns=['A','B','C','D']) In [83]: df Out[83]:           A         B         C         D 0  1.346061  1.511763  1.627081 -0.990582 1 -0.441652  1.211526  0.268520  0.024580 2 -1.577585  0.396823 -0.105381 -0.532532 3  1.453749  1.208843 -0.080952 -0.264610 4 -0.727965 -0.589346  0.339969 -0.693205 5 -0.339355  0.593616  0.884345  1.591431 6  0.141809  0.220390  0.435589  0.192451 7 -0.096701  0.803351  1.715071 -0.708758   In [84]: s = df.iloc[3] In [85]: df.append(s, ignore_index=True) Out[85]:           A         B         C         D 0  1.346061  1.511763  1.627081 -0.990582 1 -0.441652  1.211526  0.268520  0.024580 2 -1.577585  0.396823 -0.105381 -0.532532 3  1.453749  1.208843 -0.080952 -0.264610 4 -0.727965 -0.589346  0.339969 -0.693205 5 -0.339355  0.593616  0.884345  1.591431 6  0.141809  0.220390  0.435589  0.192451 7 -0.096701  0.803351  1.715071 -0.708758 8  1.453749  1.208843 -0.080952 -0.264610

 

分組

對於「group by」指的是如下一個或多個處理

• 將數據按某些標準分割爲不一樣的組
• 在每一個獨立組上應用函數
• 組合結果爲一個數據結構

請參閱 分組部分

Python

 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

In [86]: 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 [87]: df Out[87]:      A      B         C         D 0  foo    one -1.202872 -0.055224 1  bar    one -1.814470  2.395985 2  foo    two  1.018601  1.552825 3  bar  three -0.595447  0.166599 4  foo    two  1.395433  0.047609 5  bar    two -0.392670 -0.136473 6  foo    one  0.007207 -0.561757 7  foo  three  1.928123 -1.623033

分組而後應用函數統計總和存放到結果組

Python

 

1 2 3 4 5 6

In [88]: df.groupby('A').sum() Out[88]:             C        D A                     bar -2.802588  2.42611 foo  3.146492 -0.63958

按多列分組爲層次索引,而後應用函數

Python

 

1 2 3 4 5 6 7 8 9 10

In [89]: df.groupby(['A','B']).sum() Out[89]:                   C         D A   B                         bar one   -1.814470  2.395985     three -0.595447  0.166599     two   -0.392670 -0.136473 foo one   -1.195665 -0.616981     three  1.928123 -1.623033     two    2.414034  1.600434

 

重塑

請參閱章節 分層索引 和 重塑.

堆疊

Python

 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

In [90]: tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',    ....:                      'foo', 'foo', 'qux', 'qux'],    ....:                     ['one', 'two', 'one', 'two',    ....:                      'one', 'two', 'one', 'two']]))    ....: In [91]: index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second']) In [92]: df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B']) In [93]: df2 = df[:4] In [94]: df2 Out[94]:                      A         B first second                     bar   one     0.029399 -0.542108       two     0.282696 -0.087302 baz   one    -1.575170  1.771208       two     0.816482  1.100230

堆疊 函數 「壓縮」 數據楨的列一個級別.

Python

 

1 2 3 4 5 6 7 8 9 10 11 12 13

In [95]: stacked = df2.stack() In [96]: stacked Out[96]: first  second   bar    one     A    0.029399                B   -0.542108        two     A    0.282696                B   -0.087302 baz    one     A   -1.575170                B    1.771208        two     A    0.816482                B    1.100230 dtype: float64

被「堆疊」數據楨或序列(有多個索引做爲索引), 其堆疊的反向操做是未堆棧, 上面的數據默認反堆疊到上一級別:

Python

 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

In [97]: stacked.unstack() Out[97]:                      A         B first second                     bar   one     0.029399 -0.542108       two     0.282696 -0.087302 baz   one    -1.575170  1.771208       two     0.816482  1.100230   In [98]: stacked.unstack(1) Out[98]: second        one       two first                       bar   A  0.029399  0.282696       B -0.542108 -0.087302 baz   A -1.575170  0.816482       B  1.771208  1.100230   In [99]: stacked.unstack(0) Out[99]: first          bar       baz second                       one    A  0.029399 -1.575170        B -0.542108  1.771208 two    A  0.282696  0.816482        B -0.087302  1.100230

 

數據透視表

查看數據透視表.

Python

 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

In [100]: 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 [101]: df Out[101]:         A  B    C         D         E 0     one  A  foo  1.418757 -0.179666 1     one  B  foo -1.879024  1.291836 2     two  C  foo  0.536826 -0.009614 3   three  A  bar  1.006160  0.392149 4     one  B  bar -0.029716  0.264599 5     one  C  bar -1.146178 -0.057409 6     two  A  foo  0.100900 -1.425638 7   three  B  foo -1.035018  1.024098 8     one  C  foo  0.314665 -0.106062 9     one  A  bar -0.773723  1.824375 10    two  B  bar -1.170653  0.595974 11  three  C  bar  0.648740  1.167115

咱們能夠今後數據很是容易的產生數據透視表:

Python

 

1 2 3 4 5 6 7 8 9 10 11 12 13

In [102]: pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C']) Out[102]: C             bar       foo A     B                     one   A -0.773723  1.418757       B -0.029716 -1.879024       C -1.146178  0.314665 three A  1.006160       NaN       B       NaN -1.035018       C  0.648740       NaN two   A       NaN  0.100900       B -1.170653       NaN       C       NaN  0.536826

 

時間序列

pandas有易用,強大且高效的函數用於高頻數據重採樣轉換操做(例如,轉換秒數據到5分鐘數據), 這是很廣泛的狀況，但並不侷限於金融應用, 請參閱時間序列章節

Python

 

1 2 3 4 5 6

In [103]: rng = pd.date_range('1/1/2012', periods=100, freq='S') In [104]: ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng) In [105]: ts.resample('5Min', how='sum') Out[105]: 2012-01-01    25083 Freq: 5T, dtype: int32

時區表示

Python

 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

In [106]: rng = pd.date_range('3/6/2012 00:00', periods=5, freq='D') In [107]: ts = pd.Series(np.random.randn(len(rng)), rng) In [108]: ts Out[108]: 2012-03-06    0.464000 2012-03-07    0.227371 2012-03-08   -0.496922 2012-03-09    0.306389 2012-03-10   -2.290613 Freq: D, dtype: float64   In [109]: ts_utc = ts.tz_localize('UTC') In [110]: ts_utc Out[110]: 2012-03-06 00:00:00+00:00    0.464000 2012-03-07 00:00:00+00:00    0.227371 2012-03-08 00:00:00+00:00   -0.496922 2012-03-09 00:00:00+00:00    0.306389 2012-03-10 00:00:00+00:00   -2.290613 Freq: D, dtype: float64

轉換到其它時區

Python

 

1 2 3 4 5 6 7 8

In [111]: ts_utc.tz_convert('US/Eastern') Out[111]: 2012-03-05 19:00:00-05:00    0.464000 2012-03-06 19:00:00-05:00    0.227371 2012-03-07 19:00:00-05:00   -0.496922 2012-03-08 19:00:00-05:00    0.306389 2012-03-09 19:00:00-05:00   -2.290613 Freq: D, dtype: float64

轉換不一樣的時間跨度

Python

 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

In [112]: rng = pd.date_range('1/1/2012', periods=5, freq='M') In [113]: ts = pd.Series(np.random.randn(len(rng)), index=rng) In [114]: ts Out[114]: 2012-01-31   -1.134623 2012-02-29   -1.561819 2012-03-31   -0.260838 2012-04-30    0.281957 2012-05-31    1.523962 Freq: M, dtype: float64   In [115]: ps = ts.to_period() In [116]: ps Out[116]: 2012-01   -1.134623 2012-02   -1.561819 2012-03   -0.260838 2012-04    0.281957 2012-05    1.523962 Freq: M, dtype: float64   In [117]: ps.to_timestamp() Out[117]: 2012-01-01   -1.134623 2012-02-01   -1.561819 2012-03-01   -0.260838 2012-04-01    0.281957 2012-05-01    1.523962 Freq: MS, dtype: float64

轉換時段而且使用一些運算函數, 下例中, 咱們轉換年報11月到季度結束每日上午9點數據

Python

 

1 2 3 4 5 6 7 8 9 10 11

In [118]: prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV') In [119]: ts = pd.Series(np.random.randn(len(prng)), prng) In [120]: ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9 In [121]: ts.head() Out[121]: 1990-03-01 09:00   -0.902937 1990-06-01 09:00    0.068159 1990-09-01 09:00   -0.057873 1990-12-01 09:00   -0.368204 1991-03-01 09:00   -1.144073 Freq: H, dtype: float64

 

分類

自版本0.15起, pandas能夠在數據楨中包含分類. 完整的文檔, 請查看分類介紹 and the API文檔.

Python

 

1	In [122]: df = pd.DataFrame({"id":[1,2,3,4,5,6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']})

轉換原始類別爲分類數據類型.

 

 

 

 

Python

 

1 2 3 4 5 6 7 8 9 10 11

In [123]: df["grade"] = df["raw_grade"].astype("category") In [124]: df["grade"] Out[124]: 0    a 1    b 2    b 3    a 4    a 5    e Name: grade, dtype: category Categories (3, object): [a, b, e]

重命令分類爲更有意義的名稱 (分配到Series.cat.categories對應位置!)

Python

 

1	In [125]: df["grade"].cat.categories = ["very good", "good", "very bad"]

重排順分類,同時添加缺乏的分類(序列 .cat方法下返回新默認序列)

Python

 

1 2 3 4 5 6 7 8 9 10 11

In [126]: df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium", "good", "very good"]) In [127]: df["grade"] Out[127]: 0    very good 1         good 2         good 3    very good 4    very good 5     very bad Name: grade, dtype: category Categories (5, object): [very bad, bad, medium, good, very good]

排列分類中的順序,不是按詞彙排列.

Python

 

1 2 3 4 5 6 7 8 9

In [128]: df.sort("grade") Out[128]:    id raw_grade      grade 5   6         e   very bad 1   2         b       good 2   3         b       good 0   1         a  very good 3   4         a  very good 4   5         a  very good

類別列分組,而且也顯示空類別.

Python

 

1 2 3 4 5 6 7 8 9

In [129]: df.groupby("grade").size() Out[129]: grade very bad      1 bad         NaN medium      NaN good          2 very good     3 dtype: float64

 

繪圖

繪圖文檔.

Python

 

1 2 3 4

In [130]: ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000)) In [131]: ts = ts.cumsum() In [132]: ts.plot() Out[132]: <matplotlib.axes._subplots.AxesSubplot at 0xb02091ac>

在數據楨中,能夠很方便的繪製帶標籤列:

Python

 

1 2 3 4 5 6

In [133]: df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,    .....:                   columns=['A', 'B', 'C', 'D'])    .....: In [134]: df = df.cumsum() In [135]: plt.figure(); df.plot(); plt.legend(loc='best') Out[135]: <matplotlib.legend.Legend at 0xb01c9cac>

獲取數據輸入/輸出

CSV

寫入csv文件

Python

 

1	In [136]: df.to_csv('foo.csv')

讀取csv文件

Python

 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

In [137]: pd.read_csv('foo.csv') Out[137]:      Unnamed: 0          A          B         C          D 0    2000-01-01   0.266457  -0.399641 -0.219582   1.186860 1    2000-01-02  -1.170732  -0.345873  1.653061  -0.282953 2    2000-01-03  -1.734933   0.530468  2.060811  -0.515536 3    2000-01-04  -1.555121   1.452620  0.239859  -1.156896 4    2000-01-05   0.578117   0.511371  0.103552  -2.428202 5    2000-01-06   0.478344   0.449933 -0.741620  -1.962409 6    2000-01-07   1.235339  -0.091757 -1.543861  -1.084753 ..          ...        ...        ...       ...        ... 993  2002-09-20 -10.628548  -9.153563 -7.883146  28.313940 994  2002-09-21 -10.390377  -8.727491 -6.399645  30.914107 995  2002-09-22  -8.985362  -8.485624 -4.669462  31.367740 996  2002-09-23  -9.558560  -8.781216 -4.499815  30.518439 997  2002-09-24  -9.902058  -9.340490 -4.386639  30.105593 998  2002-09-25 -10.216020  -9.480682 -3.933802  29.758560 999  2002-09-26 -11.856774 -10.671012 -3.216025  29.369368   [1000 rows x 5 columns]

 

HDF5

讀寫HDF存儲

寫入HDF5存儲

Python

 

1	In [138]: df.to_hdf('foo.h5','df')

讀取HDF5存儲

Python

 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

In [139]: pd.read_hdf('foo.h5','df') Out[139]:                     A          B         C          D 2000-01-01   0.266457  -0.399641 -0.219582   1.186860 2000-01-02  -1.170732  -0.345873  1.653061  -0.282953 2000-01-03  -1.734933   0.530468  2.060811  -0.515536 2000-01-04  -1.555121   1.452620  0.239859  -1.156896 2000-01-05   0.578117   0.511371  0.103552  -2.428202 2000-01-06   0.478344   0.449933 -0.741620  -1.962409 2000-01-07   1.235339  -0.091757 -1.543861  -1.084753 ...               ...        ...       ...        ... 2002-09-20 -10.628548  -9.153563 -7.883146  28.313940 2002-09-21 -10.390377  -8.727491 -6.399645  30.914107 2002-09-22  -8.985362  -8.485624 -4.669462  31.367740 2002-09-23  -9.558560  -8.781216 -4.499815  30.518439 2002-09-24  -9.902058  -9.340490 -4.386639  30.105593 2002-09-25 -10.216020  -9.480682 -3.933802  29.758560 2002-09-26 -11.856774 -10.671012 -3.216025  29.369368   [1000 rows x 4 columns]

 

Excel

讀寫MS Excel

寫入excel文件

Python

 

1	In [140]: df.to_excel('foo.xlsx', sheet_name='Sheet1')

讀取excel文件

Python

 

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In [141]: pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA']) Out[141]:                     A          B         C          D 2000-01-01   0.266457  -0.399641 -0.219582   1.186860 2000-01-02  -1.170732  -0.345873  1.653061  -0.282953 2000-01-03  -1.734933   0.530468  2.060811  -0.515536 2000-01-04  -1.555121   1.452620  0.239859  -1.156896 2000-01-05   0.578117   0.511371  0.103552  -2.428202 2000-01-06   0.478344   0.449933 -0.741620  -1.962409 2000-01-07   1.235339  -0.091757 -1.543861  -1.084753 ...               ...        ...       ...        ... 2002-09-20 -10.628548  -9.153563 -7.883146  28.313940 2002-09-21 -10.390377  -8.727491 -6.399645  30.914107 2002-09-22  -8.985362  -8.485624 -4.669462  31.367740 2002-09-23  -9.558560  -8.781216 -4.499815  30.518439 2002-09-24  -9.902058  -9.340490 -4.386639  30.105593 2002-09-25 -10.216020  -9.480682 -3.933802  29.758560 2002-09-26 -11.856774 -10.671012 -3.216025  29.369368   [1000 rows x 4 columns]

 

陷阱

若是嘗試這樣操做可能會看到像這樣的異常:

Python

 

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>>> 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().

查看對照獲取解釋和怎麼作的幫助

也能夠查看陷阱.