我有多個DataFrames,我想做同樣的事情.

首先,我創建一個DataFrame列表.所有這些都有相同的列名為’result’.

df_list = [df1,df2,df3]

我想只保留所有DataFrame中的行’pass’,所以我在列表中使用for循環：

for df in df_list:

df =df[df['result'] == 'passed']

…這不起作用,不會從每個DataFrame中過濾掉這些值.

如果我分別過濾每一個,那么它確實有效.

df1 =df1[df1['result'] == 'passed']

df2 =df2[df2['result'] == 'passed']

df3 =df3[df3['result'] == 'passed']

解決方法:

這是因為每次你做這樣的子集df [< whatever>]你返回一個新的數據幀,并將它分配給df循環變量,每次你進入下一次迭代時都會被刪除(盡管你確實保留了最后一個).這與切片列表類似：

>>> list1 = [1,2,3,4]

>>> list2 = [11,12,13,14]

>>> for lyst in list1,list2:

... lyst = lyst[1:-1]

...

>>> list1, list2

([1, 2, 3, 4], [11, 12, 13, 14])

>>> lyst

[12, 13]

通常,如果要實際修改列表,則需要使用mutator方法.同樣,對于數據框,您可以在索引器上使用賦值,例如.loc / .ix / .iloc / etc與.dropna方法結合使用,小心傳遞inplace = True參數.假設我有三個數據幀,我想只保留第二列為正的行：

警告：這種方式并不理想,請查看編輯以獲得更好的方法

In [11]: df1

Out[11]:

0 1 2 3

0 0.957288 -0.170286 0.406841 -3.058443

1 1.762343 -1.837631 -0.867520 1.666193

2 0.618665 0.660312 -1.319740 -0.024854

3 -2.008017 -0.445997 -0.028739 -0.227665

4 0.638419 -0.271300 -0.918894 1.524009

5 0.957006 1.181246 0.513298 0.370174

6 0.613378 -0.852546 -1.778761 -1.386848

7 -1.891993 -0.304533 -1.427700 0.099904

In [12]: df2

Out[12]:

0 1 2 3

0 -0.521018 0.407258 -1.167445 -0.363503

1 -0.879489 0.008560 0.224466 -0.165863

2 0.550845 -0.102224 -0.575909 -0.404770

3 -1.171828 -0.912451 -1.197273 0.719489

4 -0.887862 1.073306 0.351835 0.313953

5 -0.517824 -0.096929 -0.300282 0.716020

6 -1.121527 0.183219 0.938509 0.842882

7 0.003498 -2.241854 -1.146984 -0.751192

In [13]: df3

Out[13]:

0 1 2 3

0 0.240411 0.795132 -0.305770 -0.332253

1 -1.162097 0.055346 0.094363 -1.254859

2 -0.493466 -0.717872 1.090417 -0.591872

3 1.021246 -0.060453 -0.013952 0.304933

4 -0.859882 -0.947950 0.562609 1.313632

5 0.917199 1.186865 0.354839 -1.771787

6 -0.694799 -0.695505 -1.077890 -0.880563

7 1.088068 -0.893466 -0.188419 -0.451623

In [14]: for df in df1, df2, df3:

....: df.loc[:,:] = df.loc[df[1] > 0,:]

....: df.dropna(inplace = True,axis =0)

....:

In [15]: df1

dfOut[15]:

0 1 2 3

2 0.618665 0.660312 -1.319740 -0.024854

5 0.957006 1.181246 0.513298 0.370174

In [16]: df2

Out[16]:

0 1 2 3

0 -0.521018 0.407258 -1.167445 -0.363503

1 -0.879489 0.008560 0.224466 -0.165863

4 -0.887862 1.073306 0.351835 0.313953

6 -1.121527 0.183219 0.938509 0.842882

In [17]: df3

Out[17]:

0 1 2 3

0 0.240411 0.795132 -0.305770 -0.332253

1 -1.162097 0.055346 0.094363 -1.254859

5 0.917199 1.186865 0.354839 -1.771787

編輯添加：

我想我發現只使用.drop方法更好的方法.

In [21]: df1

Out[21]:

0 1 2 3

0 -0.804913 -0.481498 0.076843 1.136567

1 -0.457197 -0.903681 -0.474828 1.289443

2 -0.820710 1.610072 0.175455 0.712052

3 0.715610 -0.178728 -0.664992 1.261465

4 -0.297114 -0.591935 0.487698 0.760450

5 1.035231 -0.108825 -1.058996 0.056320

6 1.579931 0.958331 -0.653261 -0.171245

7 0.685427 1.447411 0.001002 0.241999

In [22]: df2

Out[22]:

0 1 2 3

0 1.660864 0.110002 0.366881 1.765541

1 -0.627716 1.341457 -0.552313 0.578854

2 0.277738 0.128419 -0.279720 -1.197483

3 -1.294724 1.396698 0.108767 1.353454

4 -0.379995 0.215192 1.446584 0.530020

5 0.557042 0.339192 -0.105808 -0.693267

6 1.293941 0.203973 -3.051011 1.638143

7 -0.909982 1.998656 -0.057350 2.279443

In [23]: df3

Out[23]:

0 1 2 3

0 -0.002327 -2.054557 -1.752107 -0.911178

1 -0.998328 -1.119856 1.468124 -0.961131

2 -0.048568 0.373192 -0.666330 0.867719

3 0.533597 -1.222963 0.119789 -0.037949

4 1.203075 -0.773511 0.475809 1.352943

5 -0.984069 -0.352267 -0.313516 0.138259

6 0.114596 0.354404 2.119963 -0.452462

7 -1.033029 -0.787237 0.479321 -0.818260

In [25]: for df in df1,df2,df3:

....: df.drop(df.index[df[1] < 0],axis=0,inplace=True)

....:

In [26]: df1

Out[26]:

0 1 2 3

2 -0.820710 1.610072 0.175455 0.712052

6 1.579931 0.958331 -0.653261 -0.171245

7 0.685427 1.447411 0.001002 0.241999

In [27]: df2

Out[27]:

0 1 2 3

0 1.660864 0.110002 0.366881 1.765541

1 -0.627716 1.341457 -0.552313 0.578854

2 0.277738 0.128419 -0.279720 -1.197483

3 -1.294724 1.396698 0.108767 1.353454

4 -0.379995 0.215192 1.446584 0.530020

5 0.557042 0.339192 -0.105808 -0.693267

6 1.293941 0.203973 -3.051011 1.638143

7 -0.909982 1.998656 -0.057350 2.279443

In [28]: df3

Out[28]:

0 1 2 3

2 -0.048568 0.373192 -0.666330 0.867719

6 0.114596 0.354404 2.119963 -0.452462

當然更快：

In [8]: timeit.Timer(stmt="df.loc[:,:] = df.loc[df[1] > 0, :];df.dropna(inplace = True,axis =0)", setup="import pandas as pd,numpy as np; df = pd.DataFrame(np.random.random((8,4)))").timeit(10000)

Out[8]: 23.69621358400036

In [9]: timeit.Timer(stmt="df.drop(df.index[df[1] < 0],axis=0,inplace=True)", setup="import pandas as pd,numpy as np; df = pd.DataFrame(np.random.random((8,4)))").timeit(10000)

Out[9]: 11.476448250003159

標簽：python,pandas,dataframe

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