我有一個數據框，如下所示。我想使用“part1”列作為將數據分類為 3 個部分（每個部分具有相同數量的數據集）的基準，并計算每個組的 part2 均值的均值。如row0和row1為groupB，均值為(0.67+(-0.03))/2。import pandas as pddf = pd.DataFrame({ "date":["20130101","20130101","20130103","20130103","20130105","20130105"], "part1":[0.5,0.7,1.3,1.5,0.1,0.3], "part2":[0.67,-0.03,1.95,-3.25,-0.3,0.6]}) date part1 part2 output0 20130101 0.5 0.67 0.321 20130101 0.7 -0.03 0.322 20130103 1.3 1.95 -0.653 20130103 1.5 -3.25 -0.654 20130105 0.1 -0.3 0.155 20130105 0.3 0.6 0.15
3 回答
慕容森
TA貢獻1853條經驗 獲得超18個贊
如果你想計算每天的平均值，你可以使用groupby如下：
import pandas as pd
df = pd.DataFrame({
"date":["20130101","20130101","20130103","20130103","20130105","20130105"],
"part1":[0.5,0.7,1.3,1.5,0.1,0.3],
"part2":[0.67,-0.03,1.95,-3.25,-0.3,0.6]
})
df.groupby("date").mean().reset_index()
結果：
date part1 part2
0 20130101 0.6 0.32
1 20130103 1.4 -0.65
2 20130105 0.2 0.15
犯罪嫌疑人X
TA貢獻2080條經驗 獲得超4個贊
您可以by為熊貓groupby方法的參數傳遞一個函數。
from functools import partial
import pandas as pd
df = pd.DataFrame({
"date":["20130101","20130101","20130103","20130103","20130105","20130105"],
"part1":[0.5,0.7,1.3,1.5,0.1,0.3],
"part2":[0.67,-0.03,1.95,-3.25,-0.3,0.6]
})
def grouper(df, val):
foo = df.iloc[val]['part1']
if foo > 0.0 and foo < 0.4:
return 0
elif foo > 0.3 and foo < 1.0:
return 1
elif foo > 1.0:
return 2
grouped = df['part2'].groupby(by=partial(grouper, df)).mean()
這導致
1 0.15
2 0.32
3 -0.65
Name: part2, dtype: float64
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