Performing T-Test in Pandas
This tutorial will discuss how to find T-test values in Pandas.
Steps to perform T-test in Pandas
Following are the steps to perform T-test in Pandas.
Import related libraries
We must import the Pandas library and from scipy.stats ttest_ind
to get started.
import pandas as pd
from scipy.stats import ttest_ind
Creating a Pandas DataFrame
Let us create a sample DataFrame to perform T-test operation on the same DataFrame.
data = {
"Category": [
"type2",
"type1",
"type2",
"type1",
"type2",
"type1",
"type2",
"type1",
"type1",
"type1",
"type2",
],
"values": [1, 2, 3, 1, 2, 3, 1, 2, 3, 5, 1],
}
df = pd.DataFrame(data)
We created a DataFrame with a categorical column containing two categories and assigned a value to each category instance.
Let's view our DataFrame below.
print(df)
Output:
Category values
0 type2 1
1 type1 2
2 type2 3
3 type1 1
4 type2 2
5 type1 3
6 type2 1
7 type1 2
8 type1 3
9 type1 5
10 type2 1
We will now create a separate DataFrame for these two category types using the following code. This step facilitates the T-test finding process.
type1 = my_data[my_data["Category"] == "type1"]
type2 = my_data[my_data["Category"] == "type2"]
Getting T-Test Values in Pandas
We will now find the T-test results and ttest_ind()
store them in a variable using the function. We use this function in the following way.
res = ttest_ind(type1["values"], type2["values"])
In the above code, we passed the DataFrame as an argument to the function and we got the T-test results, including a tuple with the t-statistic and the p-value.
Now let's print res
the variable to see the result.
print(res)
Output:
Ttest_indResult(statistic=1.4927289925706944, pvalue=0.16970867501294376)
In the above output, we have found the T-test value with t-statistic and p-value. Hence, we can successfully find the T-test value in Pandas by the above method.
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