How to Plot a DataFrame Using Pandas (21 Code Examples)

We're now ready to explore and visualize the data with Pandas.

Line Plot

The default plot is the line plot that plots the index on the x-axis and the other numeric columns in the DataFrame on the y-axis.

Let's plot a line plot and see how Microsoft performed over the previous 12 months:

df.plot(y='MSFT', figsize=(9,6))

NOTE

The figsize argument takes two arguments, width and height in inches, and allows us to change the size of the output figure. The default values of the width and height are 6.4 and 4.8, respectively.

We can plot multiple lines from the data by providing a list of column names and assigning it to the y-axis. For example, let's see how the three companies performed over the previous year:

df.plot.line(y=['FB', 'AAPL', 'MSFT'], figsize=(10,6))

We can use the other parameters provided by the plot() method to add more details to a plot, like this:

df.plot(y='FB', figsize=(10,6), title='Facebook Stock', ylabel='USD')

As we see in the figure, the title argument adds a title to the plot, and the ylabel sets a label for the y-axis of the plot. The plot's legend display by default, however, we may set the legend argument to false to hide the legend.

Bar Plot

A bar chart is a basic visualization for comparing values between data groups and representing categorical data with rectangular bars. This plot may include the count of a specific category or any defined value, and the lengths of the bars correspond to the values they represent.

In the following example, we'll create a bar chart based on the average monthly stock price to compare the average stock price of each company to others in a particular month. To do so, first, we need to resample data by month-end and then use the mean() method to calculate the average stock price in each month. We also select the last three months of data, like this:

df_3Months = df.resample(rule='M').mean()[-3:]
print(df_3Months)

                  MSFT          FB        AAPL
Date                                          
2022-03-31  298.400002  212.692505  166.934998
2022-04-30  282.087494  204.272499  163.704994
2022-05-31  262.803335  198.643331  147.326665

Now, we're ready to create a bar chart based on the aggregated data by assigning the bar string value to the kind argument:

df_3Months.plot(kind='bar', figsize=(10,6), ylabel='Price')

We can create horizontal bar charts by assigning the barh string value to the kind argument. Let's do it:

df_3Months.plot(kind='barh', figsize=(9,6))

We can also plot the data on the stacked vertical or horizontal bar charts, which represent different groups on top of each other. The height of the resulting bar shows the combined result of the groups. To create a stacked bar chart we need to assign True to the stacked argument, like this:

df_3Months.plot(kind='bar', stacked=True, figsize=(9,6))

Histogram

A histogram is a type of bar chart that represents the distribution of numerical data where the x-axis represents the bin ranges while the y-axis represents the data frequency within a certain interval.

df[['MSFT', 'FB']].plot(kind='hist', bins=25, alpha=0.6, figsize=(9,6))

In the example above, the bins argument specifies the number of bin intervals, and the alpha argument specifies the degree of transparency.

A histogram can also be stacked. Let's try it out:

df[['MSFT', 'FB']].plot(kind='hist', bins=25, alpha=0.6, stacked=True, figsize=(9,6))

Box Plot

A box plot consists of three quartiles and two whiskers that summarize the data in a set of indicators: minimum, first quartile, median, third quartile, and maximum values. A box plot conveys useful information, such as the interquartile range (IQR), the median, and the outliers of each data group. Let's see how it works:

df.plot(kind='box', figsize=(9,6))

We can create horizontal box plots, like horizontal bar charts, by assigning False to the vert argument. Like this:

df.plot(kind='box', vert=False, figsize=(9,6))

Area Plot

An area plot is an extension of a line chart that fills the region between the line chart and the x-axis with a color. If more than one area chart displays in the same plot, different colors distinguish different area charts. Let's try it out:

df.plot(kind='area', figsize=(9,6))

The Pandas plot() method creates a stacked area plot by default. It's a common task to unstack the area chart by assigning False to the stacked argument:

df.plot(kind='area', stacked=False, figsize=(9,6))

Pie Plot

If we're interested in ratios, a pie plot is a great proportional representation of numerical data in a column. The following example shows the average Apple stock price distribution over the previous three months:

df_3Months.index=['March', 'April', 'May']
df_3Months.plot(kind='pie', y='AAPL', legend=False, autopct='


A legend will display on pie plots by default, so we assigned False to the legend keyword to hide the legend.
The new keyword argument in the code above is autopct, which shows the percent value on the pie chart slices.
If we want to represent the data of all the columns in multiple pie charts as subplots, we can assign True to the subplots argument, like this:
df_3Months.plot(kind='pie', legend=False, autopct='
array([, ,
       ], dtype=object)

Scatter Plot
Scatter plots plot data points on the x and y axes to show the correlation between two variables. Like this:
df.plot(kind='scatter', x='MSFT', y='AAPL', figsize=(9,6), color='Green')


As we see in the plot above, the scatter plot shows the relationship between Microsoft and Apple stock prices. 
Hexbin Plot
When the data is very dense, a hexagon bin plot, also known as a hexbin plot, can be an alternative to a scatter plot. In other words, when the number of data points is enormous, and each data point can't be plotted separately, it's better to use this kind of plot that represents data in the form of a honeycomb. Also, the color of each hexbin defines the density of data points in that range.
df.plot(kind='hexbin', x='MSFT', y='AAPL', gridsize=10, figsize=(10,6))


The gridsize argument specifies the number of hexagons in the x-direction. A larger grid size means more and smaller bins. The default value of the gridsize argument is 100.
KDE Plot
The last plot we want to discuss in this tutorial is the Kernel Density Estimate, also known as KDE, which visualizes the probability density of a continuous and non-parametric data variable. This plot uses Gaussian kernels to estimate the probability density function (PDF) internally. Let's try it out:
df.plot(kind='kde')


We're can also specify the bandwidth that affects the plot smoothness in the KDE plot, like this:
df.plot(kind='kde', bw_method=0.1)


df.plot(kind='kde', bw_method=1)


As we can see, selecting a small bandwidth leads to under-smoothing, which means the density plot appears as a combination of individual peaks. On the contrary, a huge bandwidth leads to over-smoothing, which means the density plot appears as unimodal distribution. 
Summary
In this tutorial, we discussed the capabilities of the Pandas library as an easy-to-learn and straightforward data visualization tool. Then, we covered all the plots provided in Pandas by implementing some examples with very few lines of code.
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