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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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