问题描述
我只是看着https://en.wikipedia.org/wiki/Chi-squared_test，并想重新创建示例“ 用于分类数据的示例卡方检验”。
我觉得我所采用的方法可能还有改进的余地，所以想知道如何实现。
这是代码：
Lines <- "x y
19.005 5.49
18.19 6
19.59 5.885
19.93 8.96
17.615 13.85
18.795 2.72
19.11 8.09
19.885 8.11
15.76 6.66
16.48 6.27
15.805 5.375
15.825 3.06
15.985 7.795
15.755 6.255
15.485 5.925
15.475 9.925
16.45 6.055
16.285 5.24
15.92 11.15
16.775 5.57
16.075 3.275
16.475 5.635
16.825 4.72
16.28 2.035
17.26 6.07
17.245 4.9
17.98 8.06
17.35 6.94
18.22 7.8
16.27 12.2
17.555 7.335
16.98 5.76
17.415 7.51
17.5 6.18"
DF <- read.table(text = Lines,header = TRUE)
这返回正确的值，但可能不知道使用某些特定的numpy / pandas方法更好的方法。
解决方法
使用 numpy/scipy：
csv = """\,A,B,C,D
White collar,90,60,104,95
Blue collar,30,50,51,20
No collar,40,45,35
"""
import io
from numpy import genfromtxt,outer
from scipy.stats.contingency import margins
observed = genfromtxt(io.StringIO(csv),delimiter=',',skip_header=True,usecols=range(1,5))
row_sums,col_sums = margins(observed)
expected = outer(row_sums,col_sums) / observed.sum()
chi_squared_stat = ((observed - expected)**2 / expected).sum()
print(chi_squared_stat)
与熊猫：
import io
import pandas as pd
csv = """\
work_group,35
"""
df = pd.read_csv(io.StringIO(csv))
df_melt = df.melt(id_vars ='work_group',var_name='group',value_name='observed')
df_melt['col_sum'] = df_melt.groupby('group')['observed'].transform(np.sum)
df_melt['row_sum'] = df_melt.groupby('work_group')['observed'].transform(np.sum)
total = df_melt['observed'].sum()
df_melt['expected'] = df_melt.apply(lambda row: row['col_sum']*row['row_sum']/total,axis=1)
chi_squared_stat = df_melt.apply(lambda row: ((row['observed'] - row['expected'])**2) / row['expected'],axis=1).sum()
print(chi_squared_stat)