How to calculate summary statistics of columns in a Pandas DataFrame?

 To calculate summary statistics of columns in a Pandas DataFrame, you can use the describe() method. This method provides various summary statistics of each column, such as count, mean, standard deviation, minimum, maximum, and quartile values.

Here's an example of how to use the describe() method:

python
import pandas as pd

# create a sample DataFrame
data = {'column1': [1, 2, 3, 4, 5],
        'column2': [10, 20, 30, 40, 50],
        'column3': [100, 200, 300, 400, 500]}
df = pd.DataFrame(data)

# calculate summary statistics
stats = df.describe()
print(stats)

Output:

        column1    column2     column3
count  5.000000   5.000000    5.000000
mean   3.000000  30.000000  300.000000
std    1.581139  15.811388  158.113883
min    1.000000  10.000000  100.000000
25%    2.000000  20.000000  200.000000
50%    3.000000  30.000000  300.000000
75%    4.000000  40.000000  400.000000
max    5.000000  50.000000  500.000000

As you can see, the describe() method returns a DataFrame containing the summary statistics for each column. The count column shows the number of non-missing values in each column, the mean column shows the average value of each column, the std column shows the standard deviation of each column, the min and max columns show the minimum and maximum values of each column, and the 25%, 50%, and 75% columns show the quartile values of each column.