# Plot Distribution of Column Values in Pandas

We can use the `DataFrame.plot()` function to distribute column values in a pandas DataFrame plot. It is an in-built function for data visualization. Using this function we can plot the given DataFrame in different ways. In this article, I will explain the plot() and using this function how to distribute the column values of a given DataFrame in different plots.

## 1. Quick Examples of Plot Distribution of Column Values

Following are quick examples of plot distribution of column Values in pandas.

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

# Example 1: plot distribution of values in Marks column
df['Marks].plot(kind='kde')

# Example 2: Plot distribution of values in Marks column using histogram
df['Marks'].plot(kind='hist', edgecolor='black')

# Example 3: Plot distribution of points by Students
df.groupby('Students')['Marks'].plot(kind='kde')

# Example 4:  Plot distribution of points by Students using histogram
df.groupby('Students')['Marks'].plot(kind='hist')
``````

Letâ€™sÂ create Pandas DataFrameÂ usingÂ Python Dictionary where, the columns are `'Students'` and `'Marks'`. Apply `df.plot()` function on DataFrame and distribute itâ€™s column values on different type of visualization.

``````
# Create DataFrame
# Create Pandas DataFrame
import pandas as pd
import numpy as np
# Create DataFrame
df = pd.DataFrame({
'Students':  ['Student1', 'Student1', 'Student1', 'Student2', 'Student2', 'Student1', 'Student1',
'Student1', 'Student2', 'Student2'],
'Marks' : [80.4, 50.6, 70.4, 50.2, 80.5, 70.4, 50.4, 60.4, 90.1, 90.5]
})
print(df)
``````

Yields below output.

``````
# Output:
Students  Marks
0  Student1   80.4
1  Student1   50.6
2  Student1   70.4
3  Student2   50.2
4  Student2   80.5
5  Student1   70.4
6  Student1   50.4
7  Student1   60.4
8  Student2   90.1
9  Student2   90.5
``````

## 2. Plot Distribution of Column Values in Pandas

Using the `df.plot()` function we can distribute the specific column values in the form of a specified plot. For that we need to set the `kind` param as `'kde'`(kernel density estimation) and then, pass it into the plot() function, it will distribute the column values in the form smooth curve.

``````
# plot distribution of values in Marks column
df['Marks].plot(kind='kde')
print(df)
``````

Yields below output.

## 4. Plot Distribution of Column in Pandas using Histogram

In Pandas one of the visualization plots isÂ Histograms,Â which is used to represent the frequency distribution for numeric data. It divides the values within a numerical variable into bins and counts the values that are fallen into a bin. Plotting a histogram is a good way to explore the distribution of our data. This is useful when the DataFrames Series is on a similar scale.

PassÂ `kind=â€™histâ€™`Â into the plot() function and distribute the column values of the given DataFrame in the form of a histogram plot. This plot uses bars to represent the distribution of values in theÂ `'Marks'` column.

``````
# Plot distribution of values in Marks column using histogram
df['Marks'].plot(kind='hist', edgecolor='black')
print(df)
``````

Yields below output.

## 5. Plot Distribution of Column values Grouped by Another Column

Using the df.plot() function and df.groupby() function we can distribute one column values grouped by another column values. The following syntax will show a plot distribution of values in theÂ `'Marks'`Â column, grouped by theÂ `'Students'`Â column. We can add labels and title to the distribution plot using the plt.legend()Â function, and using the `plt.xlabel()` function we can add the label of the x-axis. These functions are provided byÂ the `matplotlib library`.Â Â

``````
import matplotlib.pyplot as plt
# Plot distribution of points by Students
df.groupby('Students')['Marks'].plot(kind='kde')
print(df)

plt.legend(['Student1', 'student2'], title='Students')

plt.xlabel('Marks')
``````

Yields below output.

## 6. Plot Distribution of Column values Grouped by Another Column using Histogram

The following syntax will show a plot distribution of values in theÂ `'Marks'`Â column, grouped by theÂ `'Students'`Â column in the form of a histogram. For example,

``````
# Plot distribution of points by Students using histogram
df.groupby('Students')['Marks'].plot(kind='hist')
print(df)
In this article, I have explained Pandas DataFrame `plot()` and using this function how we can distribute the column values of a given Pandas DataFrame in different plots of visualization.