[英]How to loop through a pandas dataframe to run an independent ttest for each of the variables?
I have a dataset that consists of around 33 variables.我有一个包含大约 33 个变量的数据集。 The dataset contains patient information and the outcome of interest is binary in nature.
数据集包含患者信息,感兴趣的结果本质上是二进制的。 Below is a snippet of the data.
下面是数据片段。
The dataset is stored as a pandas dataframe数据集存储为 pandas dataframe
df.head()
ID Age GAD PHQ Outcome
1 23 17 23 1
2 54 19 21 1
3 61 23 19 0
4 63 16 13 1
5 37 14 8 0
I want to run independent t-tests looking at the differences in patient information based on outcome.我想运行独立的 t 检验,根据结果查看患者信息的差异。 So, if I were to run a t-test for each alone, I would do:
所以,如果我要单独对每个人进行 t 检验,我会这样做:
age_neg_outcome = df.loc[df.outcome ==0, ['Age']]
age_pos_outcome = df.loc[df.outcome ==1, ['Age']]
t_age, p_age = stats.ttest_ind(age_neg_outcome ,age_pos_outcome, unequal = True)
print('\t Age: t= ', t_age, 'with p-value= ', p_age)
How can I do this in a for loop for each of the variables?如何在每个变量的 for 循环中执行此操作?
I've seen this post which is slightly similar but couldn't manage to use it.我看过这篇文章,有点相似,但无法使用它。
Python: T test ind looping over columns of df Python:T 测试 ind 在 df 列上循环
You are almost there.你快到了。
ttest_ind
accepts multi-dimensional arrays too: ttest_ind
接受多维 arrays :
cols = ['Age', 'GAD', 'PHQ']
cond = df['outcome'] == 0
neg_outcome = df.loc[cond, cols]
pos_outcome = df.loc[~cond, cols]
# The unequal parameter is invalid so I'm leaving it out
t, p = stats.ttest_ind(neg_outcome, pos_outcome)
for i, col in enumerate(cols):
print(f'\t{col}: t = {t[i]:.5f}, with p-value = {p[i]:.5f}')
Output: Output:
Age: t = 0.12950, with p-value = 0.90515
GAD: t = 0.32937, with p-value = 0.76353
PHQ: t = -0.96683, with p-value = 0.40495
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