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 use the pandas series plot() function to plot other plots like histograms, bar charts, pie charts, etc. For this, pass the suitable value to the kind parameter in which ‘line’ is the default value.
In this article, I will explain the plot() function and using this how we can plot a Series in different visualization of DataFrame.
1. Quick Examples of Convert Series to Plot
If you are in hurry below are some quick examples of how to convert the series into a plot.
# Following are the quick examples
# 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)
2. Syntax of Pandas Series.plot()
Following is the syntax of the Series.plot().
# Syntax of series.plot
series.plot(*args, **kwargs)
2.1 Parameters of plot() function
Following are the parameters of the plot() function.
data: Series or DataFrame.x:label or position, default None. Only used if data is a DataFrame.y:label, position or list of label, positions, default None. It allows plotting of multiple columns. Only used if data is a DataFrame.kind:str
The kind of plot to produce:
line -line plot (default)bar -vertical bar plotbarh -horizontal bar plothist -histogrambox -boxplotkde -Kernel Density Estimation plotdensity -same as ‘kde’area -area plotpie -pie plotscatter -scatter plot (DataFrame only)hexbin -hexbin plot (DataFrame only)
**kwargs:Options to pass to matplotlib plotting method.
2.2 Return Value
It returns matplotlib.axes.Axes or numpy.ndarray of them
3. Usage of Plot() function.
Python Pandas library is mainly focused on data analysis and it is not only a data visualization library but also using this we can create basic plots. When we want to create exploratory data analysis plots, pandas are highly useful and practical. It provides plot() and several other wrapper functions for visualizing our data. Let’s use this pandas plot() function to create a plot for the Pandas series.
4. Create Pandas Series Plot using Matplotlib
Pandas Series is a one-dimensional, Index-labeled data structure that is available only in the Pandas library. It can store all data types such as strings, integer, float, and other python objects. We can access each element in the Series with the help of corresponding default indices.
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.
5. 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
5.1 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.
Line plot of Pandas Series
6. 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.
7. Conclusion
In this article, I have explained 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
- How to create Pandas Series in Python
- Pandas Window Functions Explained
- How to Create Scatter Plot in Pandas?
- How to Generate Time Series Plot in Pandas
- How to Plot the Boxplot from DataFrame?
- Pandas Handle Missing Data in Dataframe
- 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 distribute the column values in Pandas plot?
- How to create Pandas histogram plot?
- How to make a histogram in Pandas Series?
