14.4 Visualization

Example: The dataset below (URL) contains students test1_score, test2_score,test3_score, gender, level, section etc.

Write a program to display a frequency bar graph for gender.

my_csv_file.csv

Bar graphs

import pandas as pd

url='my_csv_file.csv'

x_df = pd.read_csv(url)

x_df.groupby(['gender']).size().plot(kind='bar')

import matplotlib.pyplot as plt

plt.suptitle("Bar Graph")
plt.ylabel("Frequency")

Show expected output

Text(0, 0.5, 'Frequency')

Bar graph titled Bar Graph of student counts by gender in my_csv_file.csv: female about 82 and male about 98; y-axis labeled Frequency.

Example: The dataset below (URL) contains students test1_score, test2_score,test3_score, gender, level, section etc.

Write a program to display a frequency pie chart for section.

my_csv_file.csv

Pie Chart

import pandas as pd

url='my_csv_file.csv'

x_df = pd.read_csv(url)

x_df.groupby(['section']).size().plot(kind='pie')

import matplotlib.pyplot as plt

plt.suptitle("Pie Chart")
plt.ylabel("sections")

Show expected output

Text(0, 0.5, 'sections')

Pie chart titled Pie Chart of the section shares in my_csv_file.csv: sections A (46), B (48) and D (48) are near-quarter slices and C (37) is smaller.

Example: The dataset below (URL) contains students test1_score, test2_score,test3_score, gender, level, section etc.

Write a program to display a box plot for test1_socre.

my_csv_file.csv

Box Plots

import pandas as pd

url='my_csv_file.csv'

x_df = pd.read_csv(url)

x_df.boxplot(column=['test1_score'], showmeans = True)

Show expected output

<matplotlib.axes._subplots.AxesSubplot at 0x7f9c424fd650>

Vertical box plot of test1_score with the mean marked (showmeans): scores run from about 59 to 90 with the median near 74.

Example: The dataset below (URL) contains students test1_score, test2_score,test3_score, gender, level, section etc.

Write a program to display box plot for test1_score grouped by level.

my_csv_file.csv

import pandas as pd

url='my_csv_file.csv'

x_df = pd.read_csv(url)

x_df.boxplot(column=['test1_score'], by = ['level'], showmeans = True, vert = False)

Show expected output

<matplotlib.axes._subplots.AxesSubplot at 0x7f9c329f73d0>

Horizontal box plots of test1_score grouped by level, means marked with triangles: the high group centers near 80, medium near 75 and low near 69.

Example: The dataset below (URL) contains students test1_score, test2_score,test3_score, gender, level, section etc.

Write a program to display box plot for test1_score grouped by gender.

my_csv_file.csv

import pandas as pd

url='my_csv_file.csv'

x_df = pd.read_csv(url)

x_df.boxplot(column=['test1_score'], by = ['gender'], showmeans = True, vert = False,)

Show expected output

/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning:

Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray

<matplotlib.axes._subplots.AxesSubplot at 0x7f9c32a1de10>

Horizontal box plots of test1_score grouped by gender, means marked: the female and male distributions overlap heavily around the low-to-mid 70s.

Alternative solution to the above example using the seaborn visualization library

import pandas as pd
import seaborn as sns
sns.set_theme(style="ticks", palette="pastel")

# Load the example tips dataset
url='my_csv_file.csv'

x_df = pd.read_csv(url)

sns.boxplot(x="test1_score", y="gender", hue_order="smoker", palette=["m", "g"], data=x_df)
sns.despine(offset=10, trim=True)

Seaborn box plot of test1_score by gender in a pastel palette — the alternative rendering of the gender comparison above.

Example: The dataset below (URL) contains students test1_score, test2_score,test3_score, gender, level, section etc.

Write a program to display a histogram for test1_score with 6 bins(classes).

my_csv_file.csv

Histograms

import pandas as pd

url='my_csv_file.csv'

x_df = pd.read_csv(url)

x_df.test1_score.hist(bins=10, ec='k')

Show expected output

<matplotlib.axes._subplots.AxesSubplot at 0x7f8fcd56bf50>

Histogram of test1_score with 10 bins and black bar edges: scores span the upper 50s to about 90 with the tallest bars in the mid-70s.

Scatter plots

Example: The dataset in the website (URL below) includes 20 columns about home sale, including price, number of bedrooms and bathrooms, areas etc. The data includes 21,613 entries.

Write a program to read the dataset and display the basic information.
Write a program to display a scatter plot of sqft_living and price.
Write a program to display a scatter plot of bathrooms and bedrooms

kc_house_data.csv

import pandas as pd

url = 'kc_house_data.csv'

x_df = pd.read_csv(url)

x_df.info()

Show expected output

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 21613 entries, 0 to 21612
Data columns (total 21 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   id             21613 non-null  int64  
 1   date           21613 non-null  object 
 2   price          21613 non-null  float64
 3   bedrooms       21613 non-null  int64  
 4   bathrooms      21613 non-null  float64
 5   sqft_living    21613 non-null  int64  
 6   sqft_lot       21613 non-null  int64  
 7   floors         21613 non-null  float64
 8   waterfront     21613 non-null  int64  
 9   view           21613 non-null  int64  
 10  condition      21613 non-null  int64  
 11  grade          21613 non-null  int64  
 12  sqft_above     21613 non-null  int64  
 13  sqft_basement  21613 non-null  int64  
 14  yr_built       21613 non-null  int64  
 15  yr_renovated   21613 non-null  int64  
 16  zipcode        21613 non-null  int64  
 17  lat            21613 non-null  float64
 18  long           21613 non-null  float64
 19  sqft_living15  21613 non-null  int64  
 20  sqft_lot15     21613 non-null  int64  
dtypes: float64(5), int64(15), object(1)
memory usage: 3.5+ MB

Example: The dataset in the website (URL below) includes 20 columns about home sale, including price, number of bedrooms and bathrooms, areas etc. The data includes 21,613 entries.

Write a program to display a scatter plot of sqft_living and price.

kc_house_data.csv

import pandas as pd

url = 'kc_house_data.csv'

x_df = pd.read_csv(url)

x_df.plot('sqft_living','price',style='o', xlabel='Living Room Square Foot', ylabel='Price in Millions')

Show expected output

<matplotlib.axes._subplots.AxesSubplot at 0x7f8fcd489c90>

Scatter plot from kc_house_data.csv of living-room square footage against price (axes labeled Living Room Square Foot and Price in Millions): a dense cloud of over 21,000 points rising with size, most prices below two million.

Example: The dataset in the website (URL below) includes 20 columns about home sale, including price, number of bedrooms and bathrooms, areas etc. The data includes 21,613 entries.

Write a program to display a scatter plot of bathrooms and bedrooms

kc_house_data.csv

import pandas as pd

url = 'kc_house_data.csv'

x_df = pd.read_csv(url)

x_df.plot('bathrooms','bedrooms',style='o')

Show expected output

<matplotlib.axes._subplots.AxesSubplot at 0x7f9c323b21d0>

Scatter plot of bathrooms against bedrooms from kc_house_data.csv: points fall on a grid because both variables take a small set of discrete values; one extreme point near 33 bedrooms stands apart.

Adapted from Python for Introductory Statistics, by Simon Aman (Truman College, City Colleges of Chicago), licensed under CC BY 4.0. Changes were made: reformatted as an accessible XYZ web edition with live in-browser code cells. License: CC-BY-4.0.