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iterrows

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DataFrame.iterrows()[source]

Iterate over DataFrame rows as (index, Series) pairs. 迭代(iterate)覆盖整个DataFrame的行中，返回(index, Series)对

>>> df = pd.DataFrame([[1, 1.5]], columns=['int', 'float'])
>>> row = next(df.iterrows())[1]
>>> row
int      1.0
float    1.5
Name: 0, dtype: float64
>>> print(row['int'].dtype)
float64
>>> print(df['int'].dtype)
int64

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pandas怎样对数据进行遍历

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import numpy as np
import pandas as pddef _map(data, exp):                  for index, row in data.iterrows():   # 获取每行的index、rowfor col_name in data.columns:row[col_name] = exp(row[col_name]) # 把结果返回给datareturn datadef _1map(data, exp):_data = [[exp(row[col_name])               # 把结果转换成2级listfor col_name in data.columns]for index, row in data.iterrows()]return _dataif __name__ == "__main__":inp = [{'c1':10, 'c2':100}, {'c1':11,'c2':110}, {'c1':12,'c2':120}]df = pd.DataFrame(inp)temp = _map(df, lambda ele: ele+1 )print temp_temp = _1map(df, lambda ele: ele+1)res_data = pd.DataFrame(_temp)         # 对2级list转换成DataFrameprint res_data

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参考文献

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pandas怎样对数据进行遍历