Basic plots with Matplotlib

A beautiful sight

Matplotlib is undeniably the most prevalent name in the family of visualization libraries in Python. In this blog post, we are going to make a head-first start with this beloved collection of graphs and plots.

For the purpose of introduction, we will have a short journey with the common charts:

  • Scatter plot
  • Histogram
  • Line plot
  • Horizontal bar chart

with the help of the 2 datasets:

  • Iris (from sklearn)
  • Boston house price (also from sklearn)

Firstly, let’s import pyplot from matplotlib:

import matplotlib.pyplot as plt

and load the 2 datasets:

from sklearn import datasets
import pandas as pd
import numpy as np

# iris
iris = datasets.load_iris()
iris_df = pd.DataFrame(, columns=iris.feature_names)
iris_df['iris-type'] =
iris_df['iris-type'] = iris_df['iris-type'].apply(lambda i : iris.target_names[i])

print ('shape of iris dataframe: {}'.format(iris_df.shape))

# boston
from sklearn.datasets import load_boston
boston = load_boston()
boston_df = pd.DataFrame(, columns=boston.feature_names)
boston_df['MEDV'] =

print ('shape of boston dataframe: {}'.format(boston_df.shape))
shape of iris dataframe: (150, 5)
   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm) iris-type
0                5.1               3.5                1.4               0.2    setosa
1                4.9               3.0                1.4               0.2    setosa
2                4.7               3.2                1.3               0.2    setosa
3                4.6               3.1                1.5               0.2    setosa
4                5.0               3.6                1.4               0.2    setosa
shape of boston dataframe: (506, 14)
      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  PTRATIO       B  LSTAT  MEDV
0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0     15.3  396.90   4.98  24.0
1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0     17.8  396.90   9.14  21.6
2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0     17.8  392.83   4.03  34.7
3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0     18.7  394.63   2.94  33.4
4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0     18.7  396.90   5.33  36.2

Before moving on, let us have a short introduction to these 2 datasets.


Iris is a small dataset with 150 samples, 4 predictor variables, and a classification target. The intention of this dataset is to predict the type of iris flower (target variable) using the characteristics of its sepal and petal (4 predictors).

The variable names do a good job of explaining themselves, so we don’t need any further description.

Boston House Price

Boston dataset’s intention is to predict the median house price of a location (inside Boston), given various characteristics of that location. It has 13 predictor variables and a regression target.

Here is the description of the variables:

CRIMper capita crime rate by town
ZNthe proportion of residential land zoned for lots over 25,000 sq.ft.
INDUSproportion of non-retail business acres per town
CHASCharles River dummy variable (= 1 if tract bounds river; 0 otherwise)
NOXnitric oxides concentration (parts per 10 million)
RMthe average number of rooms per dwelling
AGEproportion of owner-occupied units built prior to 1940
DISweighted distances to five Boston employment centers
RADindex of accessibility to radial highways
TAXfull-value property-tax rate per $10,000
PTRATIOpupil-teacher ratio by town
B1000(Bk – 0.63)^2 where Bk is the proportion of blacks by town
LSTAT% lower status of the population
MEDVThe median value of owner-occupied homes in $1000’s

In Python, you can do “print (iris)” and “print (boston)” to see more details about these data.

Scatter plot

fig, ax = plt.subplots()
ax.scatter(iris_df['iris-type'], iris_df['sepal length (cm)'])
ax.set_title('The distribution of sepal length over different Iris-types')
ax.set_xticks(range(0, 3))
ax.set_ylabel('sepal length (cm)')
illustration of Scatter plot

The Scatter Plot is a very useful tool to visualize data.
Above, we just use it to see the effect of sepal length to the target type. That is, we used it to visualize the effect of 1 variable to another variable.

In fact, scatter plots are more powerful than that. They support checking the effect of 2 variables to 1 other variable. As in the plot below:

df_setosa = iris_df[iris_df['iris-type'] == 'setosa']
df_versicolor = iris_df[iris_df['iris-type'] == 'versicolor']
df_virginica = iris_df[iris_df['iris-type'] == 'virginica']

fig, ax = plt.subplots()
ax.scatter(df_setosa['petal length (cm)'], df_setosa['petal width (cm)'], label='setosa')
ax.scatter(df_versicolor['petal length (cm)'], df_versicolor['petal width (cm)'], label='versicolor')
ax.scatter(df_virginica['petal length (cm)'], df_virginica['petal width (cm)'], label='virginica')
ax.set_title('How petal length and width distributes according to iris types')
ax.set_xlabel('petal length (cm)')
ax.set_ylabel('petal width (cm)')
illustration of Scatter plot 2


A Histogram shows us the distribution of a variable.
However, we can use the same trick as in the scatter plot to extend it to 2 variables. The trick is to split the data frame into sub-data-frames, then draw all these sub-data-frames with different colors.

fig, ax = plt.subplots()
ax.hist(df_setosa['petal length (cm)'], alpha=0.7, label='setosa')
ax.hist(df_versicolor['petal length (cm)'], alpha=0.7, label='versicolor')
ax.hist(df_virginica['petal length (cm)'], alpha=0.7, label='virginica')
ax.set_title('Histogram of petal length')
ax.set_xlabel('petal length (cm)')
illustration of Histogram

Line plot

Line plots are best used for time-series (or related to time-series) data. They can help us capture the change of a variable over time.

Unfortunately, we don’t have a dataset with time-series here. But what we are doing is just for demonstration, so let’s just focus on the code and the way to use Line plot, instead of judging if using a line plot in this example is optimal or not.

Let’s switch to the Boston dataset. We will be drawing a line plot on the median price of the houses.

fig, ax = plt.subplots()
ax.plot(sorted(boston_df['MEDV']), color='green')
ax.set_title('Median House Price in different place of Boston')
ax.set_ylabel('Price ($1000)')
ax.set_xlabel('House index')
illustration of Line plot

From the plot, we can see a break-point at the price of about 25000 dollars. House price increases steeply after this point. Seemingly, the houses that are less than 25000 dollars are for average people, while above 25000 are for the rich ones.

Horizontal bar-chart

A feature that caught my attention was RM – which indicates the average number of rooms. This seems to be a very strong predictor because it is related to the area of the house, and the house’s price, as in my experience, depends quite a lot on the area. Let’s draw a horizontal bar chart to see if it is right!

boston_df['RM_rounded'] = boston_df['RM'].round() # we round RM to integer
MEDV_by_RM = boston_df.groupby('RM_rounded')['MEDV'].median() # and take the median house price of each RM_rounded

fig, ax = plt.subplots()
ax.barh(MEDV_by_RM.index, MEDV_by_RM)
ax.set_title('House price by Number of rooms')
ax.set_xlabel('Median House price ($1000)')
ax.set_ylabel('Rounded median number of rooms')
illustration of Horizontal bar chart


The above are 4 of the common charts that are often used in practice. Admittedly, the charts we drew up there were not so attractive, however, as this is our first time using Matplotlib, we shouldn’t have a too high expectation, should we?

In the next blog posts, we will have a series of charts for some specific purposes. Those latter charts, hopefully, will have a better look than the ones we have today.

Happy learning!


  • Matplotlib official site: link

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