Pandas provides you a quick and easy way to visualize the relationship between the features of a dataframe. The Pandas line plot represents information as a series of data points connected with a straight line. Very often, we use this to find out how a particular feature changes with respect to time and also with respect to one another.

`pandas.dataframe.plot`

function can be used to directly create line plots from pandas dataframes. We will also cover how to create multiple plots, `DateTime` axis plots, and more.

## Create a Sample Data set

Let us create a simple pandas dataframe, upon which we will create a line plot. Start by importing necessary libraries

```
# Import packages
import pandas as pd
import numpy as np
```

Create a simple dataframe having 5 columns or features. The first 3 columns are scores obtained in each subject, the fourth has the school name. The last column has the corresponding dates for each.

```
df = pd.DataFrame({
'Subject_1': [70.5, 80.7, 50.4, 70.5, 80.9],
'Subject_2': [40.24, 50.9, 70.6, 80.1, 50.9],
'Subject_3': [30, 50.5, 70.8, 90.88, 30],
'School': ['School_1', 'School_2', 'School_3', 'School_4', 'School_5'],
'Date': ['2021/06/01', '2021/06/02', '2021/06/03', '2021/06/04', '2021/06/05']
})
df
```

## Pandas Line Plot using `.plot()`

function

To create a line plot using pandas, chain the `.plot()`

function to the dataframe. This function can be applied in the following ways:

### Method 1: df.plot( ) defaults

By default, the `kind`

parameter of plot function, that defines the type of plot to be created, takes the value as `line`

. Therefore, you can directly chain the `plot()`

function to the dataframe.

```
# Line Plot using 'plot' function on dataframe
df.plot();
```

*Note: Adding semicolon at the end of code suppresses text output from functions*

### Method 2: Using `kind`

parameter

If you want to be sure the output is going to be a line plot, explicitly set `kind=’line’`.

```
# Line Plot using 'plot' function and kind=line parameter on dataframe
df.plot(kind='line');
```

### Method 3: Calling Line method from Plot function

Plot function is internally a `PlotAccessor`

object.

This means that one can directly call any method (type of plot in this case) from this function itself. For this case, chaining `plot.line()`

function to the dataframe will return the matplotlib plot.

```
# Line Plot using 'line' method from plot function
df.plot.line();
```

## Pandas Line Plot for Selective Columns

In the previous examples, you must have noticed that the pandas line plot was being generated for all the columns of the dataset. You can control this behavior by explicitly passing the column names that should go in the X and Y axis.

Simply pass the respective x and y positions to the plot. These parameters take the name of the column as value.

```
# line plot with x and y values defined
df.plot(x='School', y='Subject_1');
```

## Multiple Pandas Line Plots (Alternative ways)

What happens when you call `df.plot()`

on the raw dataframe?

Without any parameter, the `plot()`

function returns the line plot for all the numeric columns of the dataset on the same axes. You can create similar plots on the same axes or even separate axes using basic matplotlib commands.

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### Multiple lines on same plot (axes)

For this, you need to create an axes object. Next, create different plots for every desirable feature. Then, pass the axes object to all the `plot`

functions using `ax`

parameter.

```
# Setup matplotlib
import matplotlib.pyplot as plt
# Create an axes object
axes = plt.gca()
# pass the axes object to plot function
df.plot(kind='line', x='School', y='Subject_1', ax=axes);
df.plot(kind='line', x='School', y='Subject_2', ax=axes);
df.plot(kind='line', x='School', y='Subject_3', ax=axes);
```

### Multiple lines on separate plots (axes)

Separate axes means that the plots can have different x and y limits. Using `subplots=True`

parameter, separate line plots are created for every numeric feature of the dataset without explicit call.

```
# line plots with subplots=True
df.plot(subplots=True);
```

## How to make Line plots with datetime in X axis?

Pandas Line plots can represent the change of a value across datetime. To effectively draw a line plot with datetime as axes, you need to follow these steps:

**Step 1: Check if datetime values are in correct format**

The datetime values should be of the form of pandas datetime objects. To convert the string dates to this format, use `pd.to_datetime()`

function.

```
# Convert the string dates to pandas datetime format
df['Date'] = pd.to_datetime(df.Date)
df.Date
```

```
0 2021-06-01
1 2021-06-02
2 2021-06-03
3 2021-06-04
4 2021-06-05
Name: Date, dtype: datetime64[ns]
```

**Step 2: Make datetime values index of the dataframe**

Set the index of the dataframe to the datetime values column using the `set_index`

function. This will eliminate the need to specifying the x-axes in line plots. Matplotlib will directly use pandas index to draw x-axes.

```
# Make datetime values as index
df.set_index('Date', inplace=True)
```

**Step 3: Create the Line plot**

When you create the pandas line plot for such dated-index dataframe, the plot will show the change of variable over datetime.

`df.plot(y='Subject_2');`

## Practical Tips

- You should use pandas for quick plotting options to get instant data visualizations. This eliminates the need to use external libraries (though these plotting functions are using matplotlib under the hood)
- You can also use other parameters offered by matplotlib such as rotate x labels, title to improve the readability of the plots.

## Test your knowledge

**Q1:** You can create line plots using:

a) `pandas.dataframe.plot`

b) `pandas.dataframe.plot.line`

c) `pandas.dataframe.plot(kind='line')`

d) All of the above

Answer:**Answer:** d) All of the above

**Q2:** What is the advantage of making datetime columns as an index?

**Answer:** It eliminates the need to explicitly specify the line plot’s ‘x’ axes. In such a case, matplotlib picks the index as ‘x’ axes.

**Q3:** Consider the following dataframe:

```
# Creating dataframe
df = pd.DataFrame({
'Subject_1': [40.24, 50.9, 70.6, 80.1, 50.9],
'Subject_2': [30, 50.5, 70.8, 90.88, 30],
'Subject_3': [70.5, 80.7, 50.4, 70.5, 80.9],
'Date': ['2021/06/06', '2021/06/07', '2021/06/08', '2021/06/09', '2021/06/10']
})
# parsing datetime values as pandas datetime values
df['Date'] = pd.to_datetime(df.Date)
# making datetime values as index
df.set_index('Date', inplace=True)
df
```

Create a line plot to show the trend of `Subject_2`

over the specified dates.

```
df.plot(y='Subject_2', kind='line');
```

This concludes the most common applications of the pandas line plot function. Hope you found it useful! If you would like to test your pandas knowledge, check out our blog on pandas exercises here.

The article was contributed by Kaustubh