讓我們考慮以下 CSV 文件test.csv："x","y","A","B"8000000000,"0,1","0.113948,0.113689",0.1140428000000000,"0,1","0.114063,0.113823",0.1141758000000000,"0,1","0.114405,0.114366",0.1145248000000000,"0,1,2,3","0.167543,0.172369,0.419197,0.427285",0.4275768000000000,"0,1,2,3","0.167784,0.172145,0.418624,0.426492",0.4287368000000000,"0,1,2,3","0.168121,0.172729,0.419768,0.427467",0.428578我的目標是按列"x"和來對行進行分組，并計算列和"y"的算術(shù)平均值。"A""B"我的第一個方法是在 Pandas 中使用groupby()和 的組合：mean()import pandasif __name__ == "__main__": data = pandas.read_csv("test.csv", header=0) data = data.groupby(["x", "y"], as_index=False).mean() print(data)運行此腳本會產(chǎn)生以下輸出： x y B0 8000000000 0,1 0.1142471 8000000000 0,1,2,3 0.428297正如我們所看到的，實現(xiàn)單值列的目標"B"很簡單。然而，該列"A"被省略。相反，我希望該列帶有"A"一個字符串，其中包含每個逗號分隔值的算術(shù)平均值。所需的輸出應(yīng)如下所示： x y A B0 8000000000 0,1 0.114139,0.113959 0.1142471 8000000000 0,1,2,3 0.167816,0.172414,0.419196,0.427081 0.428297有人知道怎么做這個嗎？
1 回答
哆啦的時光機
TA貢獻1779條經(jīng)驗 獲得超6個贊
您可以創(chuàng)建一個自定義聚合函數(shù)，將這些字符串解析為列表，查找每列的平均值，并將它們格式化回字符串：
def string_mean(rows):
data_list = []
for row in rows:
data_list.append([float(item) for item in row.split(",")])
data = np.array(data_list)
return ",".join([f"{item:.6f}" for item in data.mean(axis=0)])
df.groupby(["x", "y"], as_index=False).agg({"A": string_mean, "B": "mean"})
返回
x y A B
0 8000000000 0,1 0.114139,0.113959 0.114247
1 8000000000 0,1,2,3 0.167816,0.172414,0.419196,0.427081 0.428297
請注意，如果 A 中的字符串在單個組中具有不同數(shù)量的列，則會出錯。
順便說一句，你可能可以大大清理我上面的函數(shù)
添加回答
舉報
0/150
提交
取消