pandas Python数据帧中的置信区间
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Confidence Interval in Python dataframe
提问by MasterShifu
I am trying to calculate the mean and confidence interval(95%) of a column "Force" in a large dataset. I need the result by using the groupby function by grouping different "Classes".
我正在尝试计算大型数据集中“力”列的均值和置信区间（95%）。我需要通过对不同的“类”进行分组来使用 groupby 函数的结果。
When I calculate the mean and put it in the new dataframe, it gives me NaN values for all rows. I'm not sure if I'm going the correct way. Is there any easier way to do this?
当我计算平均值并将其放入新数据框中时，它为我提供了所有行的 NaN 值。我不确定我是否走正确的路。有没有更简单的方法来做到这一点？
This is the sample dataframe:
这是示例数据框：
df=pd.DataFrame({ 'Class': ['A1','A1','A1','A2','A3','A3'],
'Force': [50,150,100,120,140,160] },
columns=['Class', 'Force'])
To calculate the confidence interval, the first step I did was to calculate the mean. This is what I used:
为了计算置信区间，我做的第一步是计算平均值。这是我使用的：
F1_Mean = df.groupby(['Class'])['Force'].mean()
This gave me NaNvalues for all rows.
这给了我NaN所有行的值。
回答by yoonghm
import pandas as pd
import numpy as np
import math
df=pd.DataFrame({'Class': ['A1','A1','A1','A2','A3','A3'],
'Force': [50,150,100,120,140,160] },
columns=['Class', 'Force'])
print(df)
print('-'*30)
stats = df.groupby(['Class'])['Force'].agg(['mean', 'count', 'std'])
print(stats)
print('-'*30)
ci95_hi = []
ci95_lo = []
for i in stats.index:
m, c, s = stats.loc[i]
ci95_hi.append(m + 1.96*s/math.sqrt(c))
ci95_lo.append(m - 1.96*s/math.sqrt(c))
stats['ci95_hi'] = ci95_hi
stats['ci95_lo'] = ci95_lo
print(stats)
The output is
输出是
Class Force
0 A1 50
1 A1 150
2 A1 100
3 A2 120
4 A3 140
5 A3 160
------------------------------
mean count std
Class
A1 100 3 50.000000
A2 120 1 NaN
A3 150 2 14.142136
------------------------------
mean count std ci95_hi ci95_lo
Class
A1 100 3 50.000000 156.580326 43.419674
A2 120 1 NaN NaN NaN
A3 150 2 14.142136 169.600000 130.400000
回答by Dror Paz
As mentioned in the comments, I could not duplicate your error, but you can try to check that your numbers are stored as numbers and not as strings. use df.info()and make sure that the relevant columns are float or int:
正如评论中提到的，我无法复制您的错误，但您可以尝试检查您的数字是否存储为数字而不是字符串。使用df.info()并确保相关列是 float 或 int：
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6 entries, 0 to 5
Data columns (total 2 columns):
Class 6 non-null object # <--- non-number column
Force 6 non-null int64 # <--- number (int) column
dtypes: int64(1), object(1)
memory usage: 176.0+ bytes
