Exploring and Visualizing The Data Exploring your data using different types of visualizations is always a good practice when doing EDA. You'll start by plotting a histogram of the target column (w) so you can see the distribution of wins. [79] 1 #importing matplotlib

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
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python please for the commented steps. 

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Os
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Exploring and Visualizing The Data
Exploring your data using different types of visualizations is always a good practice when doing EDA.
You'll start by plotting a histogram of the target column (w) so you can see the distribution of wins.
[79]
[ ]
[]
1 #importing matplotlib
2
3 import matplotlib.pyplot as plt
4 %matplotlib inline.
5
6 #### the statement below ask matplotlib to use the 'ggplot' style
7 #### you should consider using that
8
plt.style.use('ggplot')
1 #### Complete your code below
2 #### create a histogram `hist()` over the column `df [ 'W']`
3
4 #### adding elements to your visualization to increase the readability
5 #### you should always have title and axis name (s) in your visualization
6 #### name your x-axis label as `Wins`
7
8
9 #### name your visualization title as `Distribution of Wins`
10
11
12 #### show your visualization
13
1 #### We can also check the descriptive stats of `df [ 'W']` using `.describe()
2
Transcribed Image Text:✓ Os Os Exploring and Visualizing The Data Exploring your data using different types of visualizations is always a good practice when doing EDA. You'll start by plotting a histogram of the target column (w) so you can see the distribution of wins. [79] [ ] [] 1 #importing matplotlib 2 3 import matplotlib.pyplot as plt 4 %matplotlib inline. 5 6 #### the statement below ask matplotlib to use the 'ggplot' style 7 #### you should consider using that 8 plt.style.use('ggplot') 1 #### Complete your code below 2 #### create a histogram `hist()` over the column `df [ 'W']` 3 4 #### adding elements to your visualization to increase the readability 5 #### you should always have title and axis name (s) in your visualization 6 #### name your x-axis label as `Wins` 7 8 9 #### name your visualization title as `Distribution of Wins` 10 11 12 #### show your visualization 13 1 #### We can also check the descriptive stats of `df [ 'W']` using `.describe() 2
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