python pandas dataframe 行列选择,切片操作方法

2019-11-25 14:57:53

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SQL中的select是根据列的名称来选取；Pandas则更为灵活，不但可根据列名称选取，还可以根据列所在的position（数字，在第几行第几列，注意pandas行列的position是从0开始）选取。相关函数如下：

1）loc，基于列label，可选取特定行（根据行index）；

2）iloc，基于行/列的position；

3）at，根据指定行index及列label，快速定位DataFrame的元素；

4）iat，与at类似，不同的是根据position来定位的；

5）ix，为loc与iloc的混合体，既支持label也支持position；

实例

import pandas as pdimport numpy as npdf = pd.DataFrame({'total_bill': [16.99, 10.34, 23.68, 23.68, 24.59],          'tip': [1.01, 1.66, 3.50, 3.31, 3.61],          'sex': ['Female', 'Male', 'Male', 'Male', 'Female']})# data type of columnsprint df.dtypes# indexesprint df.index# return pandas.Indexprint df.columns# each row, return array[array]print df.valuesprint df

sex      objecttip      float64total_bill  float64dtype: objectRangeIndex(start=0, stop=5, step=1)Index([u'sex', u'tip', u'total_bill'], dtype='object')[['Female' 1.01 16.99] ['Male' 1.66 10.34] ['Male' 3.5 23.68] ['Male' 3.31 23.68] ['Female' 3.61 24.59]]   sex  tip total_bill0 Female 1.01    16.991  Male 1.66    10.342  Male 3.50    23.683  Male 3.31    23.684 Female 3.61    24.59

print df.loc[1:3, ['total_bill', 'tip']]print df.loc[1:3, 'tip': 'total_bill']print df.iloc[1:3, [1, 2]]print df.iloc[1:3, 1: 3]

  total_bill  tip1    10.34 1.662    23.68 3.503    23.68 3.31  tip total_bill1 1.66    10.342 3.50    23.683 3.31    23.68  tip total_bill1 1.66    10.342 3.50    23.68  tip total_bill1 1.66    10.342 3.50    23.68

错误的表示：

print df.loc[1:3, [2, 3]]#.loc仅支持列名操作

KeyError: 'None of [[2, 3]] are in the [columns]'

print df.loc[[2, 3]]#.loc可以不加列名，则是行选择

  sex  tip total_bill2 Male 3.50    23.683 Male 3.31    23.68

print df.iloc[1:3]#.iloc可以不加第几列，则是行选择

sex  tip total_bill1 Male 1.66    10.342 Male 3.50    23.68

print df.iloc[1:3, 'tip': 'total_bill']

TypeError: cannot do slice indexing on <class 'pandas.indexes.base.Index'> with these indexers [tip] of <type 'str'>

print df.at[3, 'tip']print df.iat[3, 1]print df.ix[1:3, [1, 2]]print df.ix[1:3, ['total_bill', 'tip']]

3.313.31  tip total_bill1 1.66    10.342 3.50    23.683 3.31    23.68  total_bill  tip1    10.34 1.662    23.68 3.503    23.68 3.31

print df.ix[[1, 2]]#行选择

  sex  tip total_bill1 Male 1.66    10.342 Male 3.50    23.68

print df[1: 3]print df[['total_bill', 'tip']]# print df[1:2, ['total_bill', 'tip']] # TypeError: unhashable type

sex  tip total_bill1 Male 1.66    10.342 Male 3.50    23.68  total_bill  tip0    16.99 1.011    10.34 1.662    23.68 3.503    23.68 3.314    24.59 3.61