Pandas DataFrame 数据选取和过滤

This would allow chaining operations like:

pd.read_csv('imdb.txt')
  .sort(columns='year')
  .filter(lambda x: x['year']>1990)   # <---this is missing in Pandas
  .to_csv('filtered.csv')

For current alternatives see:

http://stackoverflow.com/questions/11869910/pandas-filter-rows-of-dataframe-with-operator-chaining

可以这样：

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1 2	df = pd.read_csv('imdb.txt').sort(columns='year') df[df['year']>1990].to_csv('filtered.csv')

　　

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

# however, could potentially do something like this:   pd.read_csv('imdb.txt')   .sort(columns='year')   .[lambda x: x['year']>1990]   .to_csv('filtered.csv') or   pd.read_csv('imdb.txt')   .sort(columns='year')   .loc[lambda x: x['year']>1990]   .to_csv('filtered.csv')<br>

　　

from:https://yangjin795.github.io/pandas_df_selection.html

Pandas 是 Python Data Analysis Library, 是基于 numpy 库的一个为了数据分析而设计的一个 Python 库。它提供了很多工具和方法，使得使用 python 操作大量的数据变得高效而方便。

本文专门介绍 Pandas 中对 DataFrame 的一些对数据进行过滤、选取的方法和工具。 首先，本文所用的原始数据如下：

df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))

    Out[9]: 
                     A         B         C         D
    2017-04-01  0.522241  0.495106 -0.268194 -0.035003
    2017-04-02  2.104572 -0.977768 -0.139632 -0.735926
    2017-04-03  0.480507  1.215048  1.313314 -0.072320
    2017-04-04  1.700309  0.287588 -0.012103  0.525291
    2017-04-05  0.526615 -0.417645  0.405853 -0.835213
    2017-04-06  1.143858 -0.326720  1.425379  0.531037

选取

通过 [] 来选取

选取一列或者几列：

df['A']
Out:
    2017-04-01    0.522241
    2017-04-02    2.104572
    2017-04-03    0.480507
    2017-04-04    1.700309
    2017-04-05    0.526615
    2017-04-06    1.143858

df[['A','B']]
Out:
                       A         B
    2017-04-01  0.522241  0.495106
    2017-04-02  2.104572 -0.977768
    2017-04-03  0.480507  1.215048
    2017-04-04  1.700309  0.287588
    2017-04-05  0.526615 -0.417645
    2017-04-06  1.143858 -0.326720

选取某一行或者几行：

df['2017-04-01':'2017-04-01']
Out:
                       A         B         C         D
    2017-04-01  0.522241  0.495106 -0.268194 -0.03500   

df['2017-04-01':'2017-04-03']
                       A         B         C         D
    2017-04-01  0.522241  0.495106 -0.268194 -0.035003
    2017-04-02  2.104572 -0.977768 -0.139632 -0.735926
    2017-04-03  0.480507  1.215048  1.313314 -0.072320

loc, 通过行标签选取数据

df.loc['2017-04-01','A']

df.loc['2017-04-01']
Out:
    A    0.522241
    B    0.495106
    C   -0.268194
    D   -0.035003

df.loc['2017-04-01':'2017-04-03']
Out:
                       A         B         C         D
    2017-04-01  0.522241  0.495106 -0.268194 -0.035003
    2017-04-02  2.104572 -0.977768 -0.139632 -0.735926
    2017-04-03  0.480507  1.215048  1.313314 -0.072320

df.loc['2017-04-01':'2017-04-04',['A','B']]
Out:
                       A         B
    2017-04-01  0.522241  0.495106
    2017-04-02  2.104572 -0.977768
    2017-04-03  0.480507  1.215048
    2017-04-04  1.700309  0.287588

df.loc[:,['A','B']]
Out:
                       A         B
    2017-04-01  0.522241  0.495106
    2017-04-02  2.104572 -0.977768
    2017-04-03  0.480507  1.215048
    2017-04-04  1.700309  0.287588
    2017-04-05  0.526615 -0.417645
    2017-04-06  1.143858 -0.326720

iloc, 通过行号获取数据

df.iloc[2]
Out:
    A    0.480507
    B    1.215048
    C    1.313314
    D   -0.072320

df.iloc[1:3]
Out:
                       A         B         C         D
    2017-04-02  2.104572 -0.977768 -0.139632 -0.735926
    2017-04-03  0.480507  1.215048  1.313314 -0.072320

df.iloc[1,1]

df.iloc[1:3,1]

df.iloc[1:3,1:2]

df.iloc[[1,3],[2,3]]
Out:
                       C         D
    2017-04-02 -0.139632 -0.735926
    2017-04-04 -0.012103  0.525291

df.iloc[[1,3],:]

df.iloc[:,[2,3]]

iat, 获取某一个 cell 的值

df.iat[1,2]
Out:
    -0.13963224781812655

过滤

使用 [] 过滤

[]中是一个boolean 表达式，凡是计算为 True 的行就会被选取。

df[df.A>1]
Out:
                       A         B         C         D
    2017-04-02  2.104572 -0.977768 -0.139632 -0.735926
    2017-04-04  1.700309  0.287588 -0.012103  0.525291
    2017-04-06  1.143858 -0.326720  1.425379  0.531037

df[df>1]
Out:
                       A         B         C   D
    2017-04-01       NaN       NaN       NaN NaN
    2017-04-02  2.104572       NaN       NaN NaN
    2017-04-03       NaN  1.215048  1.313314 NaN
    2017-04-04  1.700309       NaN       NaN NaN
    2017-04-05       NaN       NaN       NaN NaN
    2017-04-06  1.143858       NaN  1.425379 NaN

df[df.A+df.B>1.5]
Out:
                       A         B         C         D      
    2017-04-03  0.480507  1.215048  1.313314 -0.072320  
    2017-04-04  1.700309  0.287588 -0.012103  0.525291  

下面是一个更加复杂的例子，选取的是 index 在 '2017-04-01'中'2017-04-04'的，一行的数据的和大于1的行：

df.loc['2017-04-01':'2017-04-04',df.sum()>1]

还可以通过和 apply 方法结合，构造更加复杂的过滤，实现将某个返回值为 boolean 的方法作为过滤条件：

df[df.apply(lambda x: x['b'] > x['c'], axis=1)]

使用 isin

df['E']=['one', 'one','two','three','four','three']
                       A         B         C         D      E
    2017-04-01  0.522241  0.495106 -0.268194 -0.035003    one
    2017-04-02  2.104572 -0.977768 -0.139632 -0.735926    one
    2017-04-03  0.480507  1.215048  1.313314 -0.072320    two
    2017-04-04  1.700309  0.287588 -0.012103  0.525291  three
    2017-04-05  0.526615 -0.417645  0.405853 -0.835213   four
    2017-04-06  1.143858 -0.326720  1.425379  0.531037  three

df[df.E.isin(['one'])]
    Out:
                       A         B         C         D    E
    2017-04-01  0.522241  0.495106 -0.268194 -0.035003  one
    2017-04-02  2.104572 -0.977768 -0.139632 -0.735926  one