Pandas DataFrame 读取添加和删除

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Pandas DataFrame 读取添加和删除，上文介绍了如何创建 DataFrame，本章介绍如何对 Pandas.DataFrame 进行读取，添加和删除。

文章目录

1 DataFrame 读取列2 DataFrame 添加列3 DataFrame 删除列4 DataFrame 读取行5 DataFrame 添加行6 DataFrame 删除行 DataFrame 读取列

下面将从数据帧(DataFrame)中读取一列。

import pandas as pd d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']), 'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])} df = pd.DataFrame(d) print (df['one'])

执行结果如下:

a 1.0 b 2.0 c 3.0 d NaN Name: one, dtype: float64 DataFrame 添加列

下面演示如何向一个 DataFrame 中添加一个新列

import pandas as pd d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']), 'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])} df = pd.DataFrame(d) # Adding a new column to an existing DataFrame object with column label by passing new series print ("Adding a new column by passing as Series:") df['three']=pd.Series([10,20,30],index=['a','b','c']) print (df) print ("Adding a new column using the existing columns in DataFrame:") df['four']=df['one']+df['three'] print (df)

执行结果如下:

Adding a new column by passing as Series: one two three a 1.0 1 10.0 b 2.0 2 20.0 c 3.0 3 30.0 d NaN 4 NaN Adding a new column using the existing columns in DataFrame: one two three four a 1.0 1 10.0 11.0 b 2.0 2 20.0 22.0 c 3.0 3 30.0 33.0 d NaN 4 NaN NaN DataFrame 删除列

下面演示 DataFrame 中如何删除或弹出一列

# Using the previous DataFrame, we will delete a column # using del function import pandas as pd d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']), 'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd']), 'three' : pd.Series([10,20,30], index=['a','b','c'])} df = pd.DataFrame(d) print ("Our dataframe is:") print (df) # using del function print ("Deleting the first column using DEL function:") del df['one'] print (df) # using pop function print ("Deleting another column using POP function:") df.pop('two') print (df)

执行结果如下:

Our dataframe is: one three two a 1.0 10.0 1 b 2.0 20.0 2 c 3.0 30.0 3 d NaN NaN 4 Deleting the first column using DEL function: three two a 10.0 1 b 20.0 2 c 30.0 3 d NaN 4 Deleting another column using POP function: three a 10.0 b 20.0 c 30.0 d NaN DataFrame 读取行

按索引选择可以通过将行索引传递给loc()函数来选择行

import pandas as pd d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']), 'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])} df = pd.DataFrame(d) print (df.loc['b'])

执行结果如下:

one 2.0 two 2.0 Name: b, dtype: float64

按位置选择可以通过将整数位置传递给iloc()函数来选择行

import pandas as pd d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']), 'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])} df = pd.DataFrame(d) print (df.iloc[2])

执行结果如下:

one 3.0 two 3.0 Name: c, dtype: float64

按行切片选择可以使用:运算符选择多行

import pandas as pd d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']), 'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])} df = pd.DataFrame(d) print (df[2:4])

执行结果如下:

one two c 3.0 3 d NaN 4 DataFrame 添加行

使用append()函数将新行添加到 DataFrame

import pandas as pd df = pd.DataFrame([[1, 2], [3, 4]], columns = ['a','b']) df2 = pd.DataFrame([[5, 6], [7, 8]], columns = ['a','b']) df = df.append(df2) print (df)

执行结果如下:

a b 0 1 2 1 3 4 0 5 6 1 7 8 DataFrame 删除行

使用索引标签从 DataFrame 中删除行。如果标签重复，则会删除多行。

import pandas as pd df = pd.DataFrame([[1, 2], [3, 4]], columns = ['a','b']) df2 = pd.DataFrame([[5, 6], [7, 8]], columns = ['a','b']) df = df.append(df2) # Drop rows with label 0 df = df.drop(0) print (df)

执行结果如下:

a b 1 3 4 1 7 8

在上面的例子中，一共有两行被删除，因为这两行包含相同的标签0。

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Pandas DataFrame 读取 添加和删除

Pandas DataFrame 读取添加和删除