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• Post category:Pandas

Pandas DataFrame `quantile()` function is used to return values at the given quantile over the requested axis. In other words, `DataFrame.quantile()` function helps the user calculate the quantile of the values in a given axis that returns Series or DataFrame.

While getting the quantile, this function arranges the data in ascending order and we can use the formula to get the position that is q*(n+1) where q is the quantile and n is the total number of elements. In this article, I will explain the pandas DataFrame quantile() function that returns Series or DataFrame.

## 1. Quick Examples of quantile() Function

If you are in a hurry, below are some quick examples of pandas DataFrame quantile() function.

``````
# Below are the quick examples

# Example 1: use quantile() function
df2 = df.quantile(0.6)

# Example 2: using quantile() function for
# Get quantiles along the index axis
df2 = df.quantile([0.25, 0.5, 0.75], axis = 0)

# Example 3: using quantile() function for
# Get the quantiles along the index axis = 0
df2 = df.quantile(0.4, axis = 0)

# Example 4: using quantile() function for
# Get the quantiles along the index axis =1
df2 = df.quantile(0.5, axis = 1)
``````

## 2. Syntax of Pandas DataFrame.quantile()

Following is the syntax of the Pandas DataFrame.quantile().

``````
# Syntax of DataFrame.quantile()
DataFrame.quantile(q=0.5, axis=0, numeric_only=True, interpolation='linear')
``````

### 2.1 Parameters of the quantile() Function

Following are the parameters of the quantile() function.

• `q` – It represents the float or array-like, and the default is 0.5 (50% quantile). The value between 0 <= q <= 1, the quantile(s) to compute.
• `axis` – axis or axes represents the columns and rows. If axis=1 it represents the columns, and if axis=0, then it represents the rows.
• `numeric_only` – It represents bool(True or False), the default is True. If the parameter is False, the quantile of DateTime and time delta information will be registered too.
• `interpolation` – {‘linear’, ‘lower’, ‘higher’, ‘midpoint’, ‘nearest’}: This optional parameter specifies that is always assigned to linear by default.

### 2.2 Return Value quantile()

It returns Series or DataFrame.

## 3. Create Pandas DataFrame

Python pandas is widely used for data science/data analysis and machine learning applications. It is built on top of another popular package named Numpy, which provides scientific computing in Python. pandas DataFrame is a 2-dimensional labeled data structure with rows and columns (columns of potentially different types like integers, strings, float, None, Python objects e.t.c). You can think of it as an excel spreadsheet or SQL table.

Lets create pandas DataFrame from Python dictionary in which `keys` are `"Student Names",'Age','Height' and 'Weight'`, and `values` are taken as a `list of corresponding key values`.

``````
import pandas as pd
# Create a DataFrame
technologies = [
("Jenny", 22, 140, 40),
("Charles", 28, 145, 50),
("Veena", 34, 130, 45)
]
df = pd.DataFrame(technologies,columns = ["Student Names",'Age','Height','Weight'])
print(df)
``````

Yields below output.

``````
# Output:
Student Names  Age  Height  Weight
0         Jenny   22     140      40
1       Charles   28     145      50
2         Veena   34     130      45
``````

## 4. Use quantile() Function

By using the quantile() function let’s calculate the quantile at `0.6` of the pandas DataFrame. This calculates the quantile of every numeric columns and excludes the character columns.

``````
# Use quantile() function
df2 = df.quantile(0.6)
print(df2)
``````

Yields below output.

``````
# Output:
Age        29.2
Height    141.0
Weight     46.0
Name: 0.6, dtype: float64
``````

We can also get the `(0.25, 0.5, 0.75)` quantiles along the index axis, using the `quantile()` function.

``````
# Using quantile() function for
# get quantiles along the index axis
df2 = df.quantile([0.25, 0.5, 0.75], axis = 0)
print(df2)
``````

Yields below output.

``````
# Output:
Age  Height  Weight
0.25  25.0   135.0    42.5
0.50  28.0   140.0    45.0
0.75  31.0   142.5    47.5
``````

## 5. Get the Quantile Along the Axis = 0

Create a DataFrame and get the quantile at `0.4` using the `df.quantile()` function. we pass the first parameter for the function as` 0.4` and pass the axis parameter as `0` so that the quantiles are calculated in columns.

``````
# Using quantile() function for
# get the quantiles along the index axis = 0
df2 = df.quantile(0.4, axis = 0)
print(df2)
``````

Yields below output.

``````
# Output:
Age        26.8
Height    138.0
Weight     44.0
Name: 0.4, dtype: float64
``````

## 6. Get the Quantile Along the Axis = 1

Create a DataFrame by calculating quantile at 0.5 using the` DataFrame.quantile()` function over the column axis. Following the below example, at index ‘0’, the quantile is 40.0 for three values, at index ‘1’ the quantile is 50.0 for three values.

``````
# Using quantile() function for
# get the quantiles along the index axis =1
df2 = df.quantile(0.5, axis = 1)
print(df2)
``````

Yields below output.

``````
# Output:
0    40.0
1    50.0
2    45.0
Name: 0.5, dtype: float64
``````

## 7. Conclusion

In this article, you have learned the pandas DataFrame `quantile() `function by using `DataFrame.quantile()` function and with more examples. and you have also learned the syntax, and parameters of `DataFrame.quantile()` function.

Happy Learning !!