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iterrows

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DataFrame.iterrows()[source]

Iterate over DataFrame rows as (index, Series) pairs. 迭代(iterate)覆蓋整個DataFrame的行中，返回(index, Series)對 >>> 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怎樣對數(shù)據(jù)進行遍歷

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import numpy as np import pandas as pddef _map(data, exp): for index, row in data.iterrows(): # 獲取每行的index、rowfor col_name in data.columns:row[col_name] = exp(row[col_name]) # 把結果返回給datareturn datadef _1map(data, exp):_data = [[exp(row[col_name]) # 把結果轉換成2級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) # 對2級list轉換成DataFrameprint res_data

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參考文獻

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pandas怎樣對數(shù)據(jù)進行遍歷

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