DataFrame与标量运算,标量与每个元素进行计算df * 2DataFrame与DataFrame运算,根据标签索引进行对位计算索引没有匹配上的用NaN填充。df1 df2importpandasaspdimportnumpyasnpimportmatplotlib.pyplotasplt# DataFrame与标量运算,标量与每个元素进行计算。dfpd.DataFrame(data{age:[20,30,40,10],name:[张三,李四,王五,赵六]},columns[name,age],index[101,104,103,102],)print()print(df)print()print(df*2)# DataFrame与DataFrame运算,根据标签索引进行对位计算索引没有匹配上的用NaN填充。df1pd.DataFrame(data{age:[10,20,30,40],name:[张三,李四,王五,赵六]},columns[name,age],index[101,102,103,104],)df2pd.DataFrame(data{age:[10,20,30,40],name:[张三,李四,王五,田七]},columns[name,age],index[102,103,104,105],)print(df1df2)
DataFrame与标量运算,标量与每个元素进行计算df * 2DataFrame与DataFrame运算,根据标签索引进行对位计算索引没有匹配上的用NaN填充。df1 df2importpandasaspdimportnumpyasnpimportmatplotlib.pyplotasplt# DataFrame与标量运算,标量与每个元素进行计算。dfpd.DataFrame(data{age:[20,30,40,10],name:[张三,李四,王五,赵六]},columns[name,age],index[101,104,103,102],)print()print(df)print()print(df*2)# DataFrame与DataFrame运算,根据标签索引进行对位计算索引没有匹配上的用NaN填充。df1pd.DataFrame(data{age:[10,20,30,40],name:[张三,李四,王五,赵六]},columns[name,age],index[101,102,103,104],)df2pd.DataFrame(data{age:[10,20,30,40],name:[张三,李四,王五,田七]},columns[name,age],index[102,103,104,105],)print(df1df2)
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