Data Visualizations

Within Python, the matplotlib library is popular for plotting data. There is also the seaborn library which uses functions from matplotlib as a foundation and tends to make much more visually appealing plots. Scatter plots, histograms, and count plots are all examples of found in seaborn.

All of these plots are customizable through size, shape, color, markers, and more. For now, we will explore colors and markers. For more information on choosing color palettes, check out the seaborn documentation here. For more examples of what seaborn can do, click here. You can look up additional plot types here.

To get started, import both seaborn and matplotlib libraries:

# Library for df (remember df = dataframe)
import pandas as pd

# Library for plotting - we need all of these commands
import matplotlib
%matplotlib inline
import seaborn as sns

Scatter Plots

To visually see the relationship between two variables, scatter plots can be very helpful. To view a scatter plot of the data, seaborn has a function called regplot() (short for regression) which will graph the desired data points on both the x and y axis.

The way you would format your code looks like this:

# x = horizontal axis variable
# y = vertical axis variable
plot_name = sns.regplot(x = df_name.col1, y = df_name.col2)

Enhance the graph by including colors and markers into your plots by adding the following arguments:

  • Color of plot points = ‘purple’ (after y-axis)
  • Shape of marker = ‘+’ (after y-axis)

Now your code should look something like this:

# x = horizontal axis variable
# y = vertical axis variable
plot_name = sns.regplot(x = df_name.col1, y = df_name.col2, color = 'purple', marker = '+')

For more on scatter plots, check out the seaborn documentation here.


Rather than viewing the relationship of two variables through a scatter plot, we can create plots that show a continuous variable individually. Doing so will display a distribution of the data. Therefore, when continuous variables are present in the data, a histogram can be created through the distplot() (short for distribution) function. The seaborn library will automatically pick a number for the amount of bins that makes sense based on the data, but to customize you would follow the same format of adding bins = # in the argument. For now, we will use kde = False to override a default we don’t need. Colors can be added to these plots as well.

To plot your data, write your code like this:

plot_name = sns.distplot(df_name.col1, kde = False, bins = 5, color = 'orange')

Count Plots

To view a standard bar chart, the countplot() function can be used. This type of plot will graph the amount of times a categorical variable appears in the data. To get vertical bars, use x = df_name.col1 and to get horizontal bars use y = df_name.col1.

For vertical bars, your code would look like this:

# x = horizontal axis & vertical bars
plot_name = sns.countplot(x = df_name.col1)

For horizontal bars, your code would look like this:

# y = vertical axis & horizontal bars
plot_name = sns.countplot(y = df_name.col2)

Word Clouds

To visualize text data, one of the most common Python libraries is wordcloud. It does not come pre-installed when you downloaded Jupyter Notebook through Anaconda, however, it is easy to install. To install wordcloud, go to your preferred IDE (integrated development environment) and enter the following command: pip install wordcloud. After it is installed, you can import it just like the any other library and read in your df to generate a variety of images.

Begin by installing wordcloud and importing the library as shown below:

pip install wordcloud

# import libraries
from wordcloud import WordCloud
import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt

Next, use the following command and replace ’enter_variable_name_here’ with a column from your df that you’re interested in viewing:

# generate wordcloud
wordcloud = WordCloud(background_color = 'black').generate('enter_variable_name_here')
plt.df(wordcloud, interpolations = 'bilinear')

For additional resources, click here for instructions and here for examples.

Mariah Norell
Mariah Norell
Data Scientist & Lecturer

My research interests include pay equity, diversity and inclusion, and women in leadership.