Pandas is a popular open-source Python library used for data manipulation and analysis. It provides data structures and functions that make working with structured data, such as tabular data (like
Excel spreadsheets or
SQL tables), easy and intuitive.
To install Pandas, you can use the following command in your command-line interface (such as
pip install pandas
Matplotlib functionalities have been integrated into the pandas library, facilitating their use with
series. For this reason, you might also need to import the matplotlib library when building charts with Pandas.
import pandas as pd import matplotlib.pyplot as plt
In order to create graphics with Pandas, we need to use pandas objects:
Series. A dataframe can be seen as an
Excel table, and a series as a
column in that table. This means that we must systematically convert our data into a format used by pandas.
Since histograms need quantitative variables, we will get the Gap Minder dataset using the
read_csv() function. The data can be accessed using the url below.
url = 'https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/gapminderData.csv' df = pd.read_csv(url)
Histogram with the hist() function
Once we've opened our dataset, we'll now create the graph. The following displays the distribution of the life expectancy using the
hist() function. This is probably one of the shortest ways to display a histogram in Python.
Histogram with the plot() function
We'll now look at how to create a histogram using the
plot() function. This function is very general and therefore requires more arguments to be specified when it is called.
The main argument is
kind. This specifies the type of chart we want (in our case it's
'hist'). For example, we could have put
'line' for a line chart but not
'scatter' since we need 2 variables for a scatter plot (this will trigger an error).
Histogram with the plot.hist() function
And now we'll look at how to create a histogram using the
plot.hist() function. This function is a combination of the previous 2, but is no more complicated.