In statistics, **quartiles **are values that split up a dataset into four equal parts.

When analyzing a distribution, we’re typically interested in the following quartiles:

- First Quartile (
**Q1**): The value located at the 25th percentile - Second Quartile (
**Q2**): The value located at the 50th percentile - Third Quartile (
**Q3**): The value located at the 75th percentile

You can use the following methods to calculate the quartiles for columns in a pandas DataFrame:

**Method 1: Calculate Quartiles for One Column**

**df['some_column'].quantile([0.25, 0.5, 0.75])
**

**Method 2: Calculate Quartiles for Each Numeric Column**

**df.quantile(q=[0.25, 0.5, 0.75], axis=0, numeric_only=True)
**

The following examples show how to use each method in practice with the following pandas DataFrame:

**import pandas as pd
#create DataFrame
df = pd.DataFrame({'team': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J'],
'points': [12, 14, 14, 16, 24, 26, 28, 30, 31, 35],
'assists': [2, 2, 3, 3, 4, 6, 7, 8, 10, 15]})
#view DataFrame
print(df)
team points assists
0 A 12 2
1 B 14 2
2 C 14 3
3 D 16 3
4 E 24 4
5 F 26 6
6 G 28 7
7 H 30 8
8 I 31 10
9 J 35 15**

**Example 1: Calculate Quartiles for One Column**

The following code shows how to calculate the quartiles for the **points** column only:

**#calculate quartiles for points column
df['points'].quantile([0.25, 0.5, 0.75])
0.25 14.5
0.50 25.0
0.75 29.5
Name: points, dtype: float64**

From the output we can see:

- The first quartile is located at
**14.5**. - The second quartile is located at
**25**. - The third quartile is located at
**29.5**.

By only knowing these three values, we have a pretty good idea of how the values are distributed in the **points** column.

**Example 2: Calculate Quartiles for Each Numeric Column**

The following code shows how to calculate the quartiles for each numeric column in the DataFrame:

**#calculate quartiles for each numeric column in DataFrame
df.quantile(q=[0.25, 0.5, 0.75], axis=0, numeric_only=True)
points assists
0.25 14.5 3.00
0.50 25.0 5.00
0.75 29.5 7.75**

The output displays the quartiles for the two numeric columns in the DataFrame.

Note that there is more than one way to calculate quartiles for a distribution.

Refer to the pandas documentation page to see the various methods that the pandas **quantile()** function uses to calculate quartiles.

**Additional Resources**

The following tutorials explain how to perform other common tasks in pandas:

How to Calculate Percent Change in Pandas

How to Calculate Cumulative Percentage in Pandas

How to Calculate Percentage of Total Within Group in Pandas