Python Data Visualization Tutorial: Pandas, Matplotlib, Seaborn, Bokeh, Folium and More

In this guide we examine several Python libraries for data visualisation using two AI‑related datasets (temporal.csv and mapa.csv) that contain popularity scores for terms such as data science, machine learning and deep learning.

Pandas is introduced for quick data inspection with

import pandas as pd
df = pd.read_csv('temporal.csv')
df.head(10)  # view first 10 rows

. Descriptive statistics ( df.describe()) and data types ( df.info()) are shown, followed by options to increase display limits (

pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)

). Styling examples use df.head().style.format(format_dict) with currency, date and percentage formats, highlighting max/min values, background gradients, and bar charts.

A profiling report is generated with

from pandas_profiling import ProfileReport
prof = ProfileReport(df)
prof.to_file(output_file='report.html')

, producing an interactive HTML summary of the dataset.

Matplotlib is demonstrated for basic line plots, multiple series, legends, titles and grid lines. Example code:

import matplotlib.pyplot as plt
plt.plot(df['Mes'], df['data science'], label='data science')
plt.xlabel('Date')
plt.ylabel('Popularity')
plt.title('Popularity of AI terms by date')
plt.grid(True)
plt.legend()
plt.show()

. Subplots, scatter plots, bar charts, histograms, text annotations and custom line styles are also covered.

Seaborn builds on Matplotlib to produce more sophisticated visuals with less code. Example:

import seaborn as sns
sns.set()
sns.scatterplot(x='Mes', y='data science', data=df)

. Pair plots, heatmaps, and categorical violin plots are illustrated.

Bokeh creates interactive web‑ready charts. Example setup:

from bokeh.plotting import figure, output_file, save
output_file('data_science_popularity.html')
p = figure(title='data science', x_axis_label='Mes', y_axis_label='data science')
p.line(df['Mes'], df['data science'], legend='popularity', line_width=2)
save(p)

. Multiple linked plots are combined using gridplot.

Altair is mentioned briefly as an additional option for specialized visualisations.

Folium enables geographic visualisation. A simple map is created with

import folium
m1 = folium.Map(location=[41.38, 2.17], tiles='openstreetmap', zoom_start=18)
m1.save('map1.html')

. Markers, bubble maps, and colour‑coded circles based on popularity values are added, using geocoding via geopandas.tools.geocode to obtain latitude and longitude.

The article concludes with advice on selecting a visualisation library: start with pandas for quick inspection, use Matplotlib for basic static plots, and move to Seaborn, Bokeh, Altair, or Folium for more advanced or interactive visualisations.