How to Plot the Pandas Series?

Pandas Series.plot() function is used to make plots of given series. Python provides various functions to convert our data into a presentable form like never before through its Pandas plot() function. The default plot of the Pandas Series is a line plot. The pandas series plot() function returns a matplotlib axes object to which you can add additional formatting.

Note: You can utilize the plot() function of Pandas Series to generate various types of plots such as histograms, bar charts, pie charts, and more. To do so, simply provide an appropriate value to the kind parameter. By default, the kind parameter is set to ‘line’.

In this article, I will explain the plot() function and using this how we can plot a Series in different visualization of DataFrame.

Key Points –

• Ensure that you have both Pandas and Matplotlib libraries imported in your Python environment to utilize their plotting functionalities.
• Pandas Series objects come with built-in plotting methods. Familiarize yourself with these methods such as plot(), which is a wrapper around Matplotlib’s plotting functions.
• Before plotting, thoroughly explore and understand the characteristics of the Pandas Series data. Identify patterns, trends, and potential outliers to inform the choice of an appropriate plot type.
• Tailor the plot to the intended audience. Choose visualizations that effectively convey the key insights to your target audience, whether they are technical experts, stakeholders, or the general public.
• Adjust visual elements such as color, markers, and line styles to enhance readability and convey specific information. Use labels and legends to provide context.

Quick Examples of Convert Series to Plot

Following are quick examples of converting the series into a plot.

# Quick examples of convert series to plot

# Example 1: Create Pandas Series plot
# Create Series 
s = pd.Series([np.random.randint(1,500) for i in range(1,100)])
p = plt.plot(s.index, s.values) 

# Example 2: create timeseries plot
# Create Series
ser = pd.Series({1720: 1483799, 1820: 1660099, 1920: 2045638, 2020: 3221898},
               name='population')
ser.plot(x="year", y="people")
plt.xlabel("year",  size = 20)
plt.ylabel("people", size = 20)
plt.title("Population", size = 25)

# Example 3: Draw a plot bar chart
ser.plot.bar()
# Set the Bar Plot Labels and Title
ser.plot(x="year", y="people")
plt.xlabel("year",  size = 20)
plt.ylabel("people", size = 20)
plt.title("Population", size = 25)

Pandas Series.plot() Introduction

Following is the syntax of the Series.plot().

# Syntax of series.plot
series.plot(*args, **kwargs)

Parameters

Following are the parameters of the plot() method.

• data – A Pandas Series or DataFrame.
• x – Label or position, with a default value of None. This parameter is only applicable when the data is a DataFrame.
• y – label, position, or a list of labels and positions, by default None. This parameter facilitates the plotting of multiple columns and is applicable only when the data is a DataFrame
• kind – Specifies the type of plot to be created (‘line’, ‘bar’, ‘barh’, ‘hist’, ‘box’, ‘kde’, ‘density’, ‘area’, ‘pie’, ‘scatter’, ‘hexbin’, etc.).

The kind of plot to produce:

• line - line plot (default)
• bar - vertical bar plot
• barh - horizontal bar plot
• hist - histogram
• box - boxplot
• kde - Kernel Density Estimation plot
• density - same as ‘kde’
• area - area plot
• pie - pie plot
• scatter - scatter plot (DataFrame only)
• hexbin - hexbin plot (DataFrame only)

• **kwargs: Options to pass to matplotlib plotting method.

Return Value

It returns matplotlib.axes.Axes or numpy.ndarray of them

Usage of Plot() Function

The Python Pandas library primarily focused data analysis, offering not only robust data visualization capabilities but also the ability to generate fundamental plots. Particularly valuable for exploratory data analysis, Pandas equips users with its plot() function and a range of other wrapper functions tailored for visualizing data. Let’s use this Pandas plot() function to create a plot for the Pandas series.

Create Pandas Series Plot using Matplotlib

A Pandas Series represents one-dimensional data indexed with labels, exclusive to the Pandas library. It accommodates diverse data types like strings, integers, floats, and other Python objects. Each element in the Series can be accessed using its respective default index.

Now, let’s create a Pandas series. Here I will create a Series using numpy. random.randint() function.

# Create Series 
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
s = pd.Series([np.random.randint(1,500) for i in range(1,100)])
print(s)

# Create Pandas Series plot
p = plt.plot(s.index, s.values) 

Yields below output.

# Output:
0     487
1     406
2     484
3      76
4     114

94     19
95     58
96    239
97    231
98     10
Length: 99, dtype: int64

Use Pandas Series for plotting where its indices are on the x-axis and values are on the y-axis. This process will be done by using matplotlib.pyplot plot() method.

Create Series Plot using plot()

Here, I have created a Series using general data based on population and I will use Series.plot() function to plot the values in a chart.

# Create Series
ser = pd.Series({1720: 1483799, 1820: 1660099, 1920: 2045638, 2020: 3221898},
               name='population')
print(ser)
Print(type(ser))
ser.plot()

Yields below output.

# Output:
1720    1483799
1820    1660099
1920    2045638
2020    3221898
Name: population, dtype: int64

Customize the Series plot

We can customize the Series plots by providing any keyword argument. Here, I will customize the timeseries plot by providing axis labels and title using matplotlib.pyplot.

import matplotlib.pyplot as plt
# Set the timeseries Plot Labels and Title
ser.plot(x="year", y="people")
plt.xlabel("year",  size = 20)
plt.ylabel("people", size = 20)
plt.title("Population", size = 25) 

Yield below output.

Plot Bar of Series

We can create a bar graph by calling a plot.bar() on the pandas Series, so let’s create a Series. Here I have created a Series using general data on the population. I have taken a year as an index, which is set on an x-axis label and people as a measured value, which is set on a y-axis label.

# Draw a plot bar chart
ser.plot.bar()
# Set the Bar Plot Labels and Title
ser.plot(x="year", y="people")
plt.xlabel("year",  size = 20)
plt.ylabel("people", size = 20)
plt.title("Population", size = 25)

Yields below output.

Frequently Asked Questions on Plot the Pandas Series

Conclusion

In this article, you have learned the Series.plot() function and using this function how we can plot the different plots in Pandas Series. As well as I explained how to customize the plots by using any keyword argument of plot() function.

Related Articles

• Pandas Series rank() Function
• Pandas Series.rolling() Function
• Pandas Series sample() Function
• How to Change Pandas Plot Size?
• How to add title to Pandas plots?
• How to generate line plot in Pandas?
• How to add legends to plots in Pandas
• How to create Pandas histogram plot?
• How to create Pandas Series in Python
• How to Create Scatter Plot in Pandas?
• Pandas Series reset_index() Function
• Pandas Series.str.contains() With Examples
• Pandas Series.value_counts() With Examples
• Distribute the column values in Pandas plot?

References

• https://pandas.pydata.org/docs/reference/api/pandas.Series.plot.area.html