Multi-column T-test using Pandas and SciPy

This article describes how to t-test multiple columns in a Pandas DataFrame using the Pandas and SciPy libraries. With sample code, we show in detail how to perform t-tests on a specific group and provide solutions to generalize methods to more groups. In addition, it is also reminded of issues that need to be paid attention to when conducting multiple comparisons and how to deal with multiple inspection problems.

Multi-column T-test using Pandas and SciPy

The T test is a commonly used statistical method to compare whether there are significant differences in the mean of the two sets of data. In data analysis, we often need to perform t-tests on multiple columns in DataFrame to evaluate the impact of different categories of variables on numerical variables. This article will explain how to use Pandas and SciPy libraries to achieve this efficiently.

Single T test

First, we create a sample DataFrame:

 import pandas as pd
from scipy.stats import ttest_ind

data = {'Product': ['laptop', 'printer','printer','printer','laptop','laptop','laptop','laptop','laptop','printer'],
        'Purchase_cost': [120.09, 150.45, 300.12, 450.11, 200.55, 175.89, 124.12, 113.12, 143.33, 375.65],
        'Warranty_years':[3,2,2,1,4,1,2,3,1,2],
        'service_cost': [5,5,10,4,7,10,4,6,12,3]

        }

df = pd.DataFrame(data)

print(df)

Suppose we want to compare the differences in Purchase_cost between two sets of data whose Product is 'laptop' and 'printer'. We can use the following code:

 #define samples
group1 = df[df['Product']=='laptop']
group2 = df[df['Product']=='printer']

#perform independent two sample t-test
ttest_ind(group1['Purchase_cost'], group2['Purchase_cost'])

This code first divides the DataFrame into two groups based on the value of the Product column, and then uses the scipy.stats.ttest_ind function to perform independent sample t-test on the Purchase_cost column of the two groups of data.

T-test on multiple columns at the same time

If we need to t-test multiple columns (such as Purchase_cost, Warranty_years, and service_cost) at the same time, we can use the following code:

 cols = df.columns.difference(['Product'])
# or with an explicit list
# cols = ['Purchase_cost', 'Warranty_years', 'service_cost']

group1 = df[df['Product']=='laptop']
group2 = df[df['Product']=='printer']
out = pd.DataFrame(ttest_ind(group1[cols], group2[cols]),
                   columns=cols, index=['statistic', 'pvalue'])

print(out)

This code first gets the list of column names that need to be performed with t-tested cols, and then divides the DataFrame into two groups. The key is that the ttest_ind function can directly process 2D input, that is, perform t-testing on multiple columns of data at the same time. Finally, the results are stored in a new DataFrame out for easy viewing and analysis.

Another way to implement it is to use dictionary derivation:

 out = pd.DataFrame({c: ttest_ind(group1[c], group2[c]) for c in cols},
                    index=['statistic', 'pvalue'])

This approach is more concise, but may be slightly less readable.

Promote to more groups

If the DataFrame contains more different Product values and we want to compare all possible combinations, we can use the itertools.combinations function:

 from itertools import combinations

cols = df.columns.difference(['Product'])

g = df.groupby('Product')[cols]

out = pd.concat({(a,b): pd.DataFrame(ttest_ind(g.get_group(a), g.get_group(b)),
                                     columns=cols, index=['statistic', 'pvalue'])
                 for a, b in combinations(df['Product'].unique(), 2)
                }, names=['product1', 'product2'])

print(out)

This code first uses the groupby function to group DataFrames by Product column, and then uses the itertools.combinations function to generate all possible combinations. For each combination, we perform a t-test and store the results in a new DataFrame out.

Things to note

When conducting multiple comparisons, you need to pay attention to multiple test issues. Since we performed multiple t tests, the probability of false positives increased. To solve this problem, several multi-test correction methods can be used, such as Bonferroni correction or Benjamini-Hochberg correction. These correction methods can adjust the p-value to control the false positive rate.

Summarize

This article describes how to t-test multiple columns in a Pandas DataFrame using the Pandas and SciPy libraries. With sample code, we show in detail how to perform t-tests on a specific group and provide solutions to generalize methods to more groups. In addition, issues that need to be paid attention to when conducting multiple comparisons are also reminded. Mastering these techniques can help us perform data analysis more efficiently.

The above is the detailed content of Multi-column T-test using Pandas and SciPy. For more information, please follow other related articles on the PHP Chinese website!