Introduction to Data Visualization in Python

Hi friends , welcome to Data Visualization python tutorial . so in this post we will learn an important topic of data science that is Data Visualization . Data visualization is the study to visualize data . so lets start how to visualize data in python.

Data Visualization

• Data visualization is the process of converting raw data into easily understandable pictorial representation, that enables fast and effective decisions.
• It is both an Art and Science.
• Data visualization is a strategy where we represent the quantitative information in a graphical form.

Why Data Visualization?

Now the question is that why we visualize data? So, the answer is that the pictorial form of data is easily understandable rather than huge numbers of numerical data.

To get a little overview here are a few popular plotting libraries:

• Matplotlib: low level, provides lots of freedom
• Pandas Visualization: easy to use interface, built on Matplotlib
• Seaborn: high-level interface, great default styles
• ggplot: based on R’s ggplot2, uses Grammar of Graphics
• Plotly: can create interactive plots

In this article , we will learn how to create basic plots using Matplotlib , Pandas visualization and seaborn as well as how to use specific features of each library.

Importing Datasets

In this article, we will use two datasets which are freely available. The Iris and Wine Reviews dataset, which we can both load in using pandas read_csv method.

import pandas as pd
iris = pd.read_csv('iris.csv', names=['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'class'])
print(iris.head())

wine_reviews = pd.read_csv('winemag-data-130k-v2.csv', index_col=0)
wine_reviews.head()

Matplotlib

Matplotlib is the most popular python plotting library. It is a low-level library with a Matlab like interface which offers lots of freedom at the cost of having to write more code.

Matplotlib is specifically good for creating basic graph like line chart , bar charts , histograms and may more.

import matplotlib.pyplot as plt

Scatter Plot

To create a scatter plot in Matplotlib we can use the scatter method. We will also create a figure and an axis using plt.subplots so we can give our plot a title and labels.

# create a figure and axisfig, 
ax = plt.subplots() 
# scatter the sepal_length against the sepal_width
ax.scatter(iris['sepal_length'], iris['sepal_width'])
# set a title and labels
ax.set_title('Iris Dataset')
ax.set_xlabel('sepal_length')
ax.set_ylabel('sepal_width')

We can give the graph more meaning by coloring in each data-point by its class. This can be done by creating a dictionary which maps from class to color and then scattering each point on its own using a for-loop and passing the respective color.

# create color dictionary
colors = {'Iris-setosa':'r', 'Iris-versicolor':'g', 'Iris-virginica':'b'}
# create a figure and axis
fig, ax = plt.subplots()
# plot each data-point
for i in range(len(iris['sepal_length'])): 
   ax.scatter(iris['sepal_length'][i], iris['sepal_width'][i],color=colors[iris['class'][i]])
# set a title and labels
ax.set_title('Iris Dataset')
ax.set_xlabel('sepal_length')
ax.set_ylabel('sepal_width')

Line Chart

In Matplotlib we can create a line chart by calling the plot method. We can also plot multiple columns in one graph, by looping through the columns we want and plotting each column on the same axis.

# get columns to plot
columns = iris.columns.drop(['class'])
# create x data
x_data = range(0, iris.shape[0])
# create figure and axis
fig, ax = plt.subplots()
# plot each column
for column in columns:
     ax.plot(x_data, iris[column], label=column)
# set title and legend
ax.set_title('Iris Dataset')
ax.legend()

Histogram

In Matplotlib we can create a Histogram using the hist method. If we pass it categorical data like the points column from the wine-review dataset it will automatically calculate how often each class occurs.

# create figure and axis
fig, ax = plt.subplots()
# plot histogram
ax.hist(wine_reviews['points'])
# set title and labels
ax.set_title('Wine Review Scores')
ax.set_xlabel('Points')
ax.set_ylabel('Frequency')

Bar Chart

A bar chart can be created using the bar method. The bar-chart isn’t automatically calculating the frequency of a category so we are going to use pandas value_counts function to do this. The bar-chart is useful for categorical data that doesn’t have a lot of different categories (less than 30) because else it can get quite messy.

# create a figure and axis 
fig, ax = plt.subplots() 
# count the occurrence of each class 
data = wine_reviews['points'].value_counts() 
# get x and y data 
points = data.index 
frequency = data.values 
# create bar chart
ax.bar(points, frequency) 
# set title and labels 
ax.set_title('Wine Review Scores') 
ax.set_xlabel('Points') 
ax.set_ylabel('Frequency')

Pandas Visualization

Pandas is an open source high-performance, easy-to-use library providing data structures, such as dataframes, and data analysis tools like the visualization tools we will use in this article.

Pandas Visualization makes it really easy to create plots out of a pandas dataframe and series. It also has a higher level API than Matplotlib and therefore we need less code for the same results.

pip install pandas

Scatter Plot

To create a scatter plot in Pandas we can call <dataset>.plot.scatter() and pass it two arguments, the name of the x-column as well as the name of the y-column. Optionally we can also pass it a title.

iris.plot.scatter(x='sepal_length', y='sepal_width', title='Iris Dataset')

As you can see in the image it is automatically setting the x and y label to the column names.

Line Chart

To create a line-chart in Pandas we can call <dataframe>.plot.line(). Whilst in Matplotlib we needed to loop-through each column we wanted to plot, in Pandas we don’t need to do this because it automatically plots all available numeric columns (at least if we don’t specify a specific column/s).

iris.drop(['class'], axis=1).plot.line(title='Iris Dataset')

Histogram

In Pandas, we can create a Histogram with the plot.hist method. There aren’t any required arguments but we can optionally pass some like the bin size.

It’s also really easy to create multiple histograms.

iris.plot.hist(subplots=True, layout=(2,2), figsize=(10, 10), bins=20)

The subplots argument specifies that we want a separate plot for each feature and the layout specifies the number of plots per row and column.

Bar Chart

To plot a bar-chart we can use the plot.bar() method, but before we can call this we need to get our data. For this we will first count the occurrences using the value_count() method and then sort the occurrences from smallest to largest using the sort_index() method.

wine_reviews['points'].value_counts().sort_index().plot.bar()

It’s also really simple to make a horizontal bar-chart using the plot.barh() method.

wine_reviews['points'].value_counts().sort_index().plot.barh()

wine_reviews.groupby("country").price.mean().sort_values(ascending=False)[:5].plot.bar()

In the example above we grouped the data by country and then took the mean of the wine prices, ordered it, and plotted the 5 countries with the highest average wine price.

Seaborn

Seaborn is a Python data visualization library based on Matplotlib. It provides a high-level interface for creating attractive graphs.

Seaborn has a lot to offer. You can create graphs in one line that would take you multiple tens of lines in Matplotlib. Its standard designs are awesome and it also has a nice interface for working with pandas dataframes.

It can be imported by typing:

import seaborn as sns

Scatter plot

We can use the .scatterplot method for creating a scatterplot, and just as in Pandas we need to pass it the column names of the x and y data, but now we also need to pass the data as an additional argument because we aren’t calling the function on the data directly as we did in Pandas.

sns.scatterplot(x='sepal_length', y='sepal_width', data=iris)

We can also highlight the points by class using the hue argument, which is a lot easier than in Matplotlib.

sns.scatterplot(x='sepal_length', y='sepal_width', hue='class', data=iris)

Line chart

To create a line-chart the sns.lineplot method can be used. The only required argument is the data, which in our case are the four numeric columns from the Iris dataset. We could also use the sns.kdeplot method which rounds of the edges of the curves and therefore is cleaner if you have a lot of outliers in your dataset.

sns.lineplot(data=iris.drop(['class'], axis=1))

Histogram

To create a histogram in Seaborn we use the sns.distplot method. We need to pass it the column we want to plot and it will calculate the occurrences itself. We can also pass it the number of bins, and if we want to plot a gaussian kernel density estimate inside the graph.

sns.distplot(wine_reviews['points'], bins=10, kde=False)

sns.distplot(wine_reviews['points'], bins=10, kde=True)

Bar chart

In Seaborn a bar-chart can be created using the sns.countplot method and passing it the data.

sns.countplot(wine_reviews['points'])

Other graphs

Now that you have a basic understanding of the Matplotlib, Pandas Visualization and Seaborn syntax I want to show you a few other graph types that are useful for extracting insides.

For most of them, Seaborn is the go-to library because of its high-level interface that allows for the creation of beautiful graphs in just a few lines of code.

Box plots

A Box Plot is a graphical method of displaying the five-number summary. We can create box plots using seaborns sns.boxplot method and passing it the data as well as the x and y column name.

df = wine_reviews[(wine_reviews['points']>=95) & (wine_reviews['price']<1000)]
sns.boxplot('points', 'price', data=df)

Box Plots, just like bar-charts are great for data with only a few categories but can get messy really quickly.

Heatmap

A Heatmap is a graphical representation of data where the individual values contained in a matrix are represented as colors. Heatmaps are perfect for exploring the correlation of features in a dataset.

sns.heatmap(iris.corr(), annot=True)