Question

Python Programming language. Complete the problems below using Jupyter Notebook.

Problem Needs to be solved from number #1

link provided for the Data: -----> https://docs.google.com/spreadsheets/d/1TqhyxFKQlOHAyXpQBL-4C96kgZFBoMwUgE8-b33CqPQ/edit?usp=sharing

PROBLEMS

# 0.0 Import the libraries (pandas, matplotlib, and seaborn) Include the code line: %matplotlib inline #Include the code line: plt.style.use(“ggplot”) #Load the data using pandas #Inspect the data using head(), dtypes

ANSWERD:

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
%matplotlib inline
df = pd.read_csv()
print(df.head())
print(df.dtypes)

# 1

Create two subset dataframes: one for those with an MBA and those without an MBA

Use matplotlib (not seaborn) to display two box plots side by side (in one figure/axes) comparing the salaries for those with an MBA and those without one.

Include the labels: x-axis, y-axis, chart title.

# 2

Use seaborn's catplot function to display a box plot comparing the salaries between those

with an MBA and those without. Hint: Do not use two subsets created in the previous problem and use the col parameter instead.

# 3

Use matplotlib (not seaborn) to display a scatter plot between age and salary.

Include the labels: x-axis, y-axis, chart title.

# 4

#Use seaborn's jointplot function between age and salaries.

# 5

#Use matplotlib to create two histograms on salary--one for those employees with an MBA and one for those employees without an MBA. Reference the same subset variables from problem 1.

The figure should contain one row and two columns for the two subplots.

Include the labels: x-axis, y-axis, chart titles for both histograms, and a figure title.

# 6

Use seaborn's stripplot function to compare the salaires between those with and

without an MBA on salary. A legend should be apparent using hue.

# 7

Use matplotlib to create a pie chart on the MBA data column.

Include the labels for each slice by providing the percentage and include a chart title.

# 8

Use seaborn's barplot function on the MBA and Salary data columns.

Include a chart title.

# 9

Use matplotlib’s plt.bar function to create a bar chart that displays the frequency of those with an MBA and those without one. Hint: Use the value_counts() method on the MBA data column.

Include the labels: x-axis, y-axis, chart title.

# 10

#Create one figure displaying four subplots/axes. Specifically, the subplot should contain two rows and two columns. The first row will contain any two the matplotlib charts. While the second row will contain any two seaborn charts. You may reuse the code/charts from previous problems above.

Include the labels on each chart: x-axis, y-axis, chart titles. Format as needed and adjust the figure size

Answer 1

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
%matplotlib inline
df = pd.read_csv('/content/HomeworkLib_Data.csv')
print(df.head())
print(df.dtypes)

#1. 
df_with_MBA = df.loc[df['MBA']=='Yes']
df_without_MBA = df.loc[df['MBA']=='No']

fig = plt.figure(figsize =(10, 7)) 
data = [df_with_MBA['Salary'], df_without_MBA['Salary']]
plt.figure(figsize=(10, 6))
box = plt.boxplot(data,
                  positions=[1,2],
                  labels=['MBA','Non-MBA'])
plt.xlabel('MBA or Non-MBA')
plt.ylabel('Salaries')
plt.title("SALARY coparison MBA vs Non-MBA")
plt.show()

SALARY coparison MBA vs Non-MBA o 80000 70000 60000 Salaries 50000 40000 30000 MBA Non-MBA MBA or Non-MBA

g = sns.catplot(x='MBA', y="Salary", hue="MBA",data=df, kind="box")

80000 70000 60000 Salary 50000 40000 30000 No Yes MBA

#3. 
plt.scatter(df['Age'],df['Salary'],c='blue') 
plt.xlabel('Age')
plt.ylabel('Salaries')
plt.title("Scatter plot between age & salary")
plt.show() 

Scatter plot between age & salary 80000 - 70000 60000 Salaries 50000 40000 30000 25 30 35 45 50 55 60 40 Age

#4.
sns.jointplot(data=df, x="Age", y="Salary",color='blue')

80000 70000 60000 Salary 50000 40000 30000 25 30 35 50 55 60 40 45 Age

#5. 
fig = plt.figure(figsize=(20,7))
plt.subplot(1, 2, 1)
plt.hist(df_with_MBA['Salary'], bins=30)
plt.xlabel('Salaries')
plt.ylabel('Count')
plt.title('MBA salary histogram')
plt.subplot(1, 2, 2)
plt.hist(df_without_MBA['Salary'], bins=50)
plt.xlabel('Salaries')
plt.ylabel('Count')
plt.title('Non-MBA salary histogram')
fig.suptitle("MBA and Non-MBA Salary Histogram")
plt.show()

MBA and Non-MBA Salary Histogram MBA salary histogram Non-MBA salary histogram 10 3.0 25 0.8 20 0.6 15 0.4 10 0.2 05 00 40000 50000 60000 70000 80000 0.0 25000 30000 35000 40000 50000 55000 60000 65000 Salaries 45000 Salaries

#6.
sns.stripplot(x="MBA", y="Salary",hue='MBA', data=df)

80000 - MBA No Yes 70000 60000 - Salary 50000 - 40000 - 30000 - No Yes MBA

#7.
fig = plt.figure(figsize=(10,7))
label=['MBA','Non-MBA']
data = [len(df.loc[df['MBA']=='Yes']),len(df.loc[df['MBA']=='No'])]
explode = (0.1, 0)
plt.pie(data, labels = label,explode=explode,autopct='%1.2f%%')
plt.title('Pie chart MBA vs Non-MBA')
plt.show()

Pie chart MBA vs Non-MBA MBA 17.14% 82.86% Non-MBA

#8.
sns.barplot(x="MBA", y="Salary", data=df)

60000 50000 - 40000 - Salary 30000 20000 - 10000 0 No Yes MBA

#9.
plt.bar(['No','Yes'],df['MBA'].value_counts())
plt.xlabel('MBA or Non-MBA')
plt.ylabel('Counts')
plt.title("Bar plot MBA-Non-MBA counts")
plt.show() 

Bar plot MBA-Non-MBA counts 30 25 - 20 Counts 15 10 5 - 0 No Yes MBA or Non-MBA

fig = plt.figure(figsize=(20,10))
plt.subplot(2, 2, 1)
plt.scatter(df['Age'],df['Salary'],c='blue')
plt.xlabel('Age')
plt.ylabel('Salaries')
plt.title("Matplotlib Scatter plot(Age & Salary")
plt.subplot(2, 2, 2)
plt.bar(['No','Yes'],df['MBA'].value_counts())
plt.xlabel('MBA or Non-MBA')
plt.ylabel('Counts')
plt.title("Matpltlib Bar plot MBA-Non-MBA counts")
plt.subplot(2, 2, 3)
sns.stripplot(x="MBA", y="Salary",hue='MBA', data=df)
plt.xlabel('MBA or Non-MBA')
plt.ylabel('Salry')
plt.title("Seaborn strip plot MBA vs non-MBA salary")
plt.subplot(2, 2, 4)
sns.barplot(x="MBA", y="Salary", data=df)
plt.xlabel('MBA or Non-MBA')
plt.ylabel('Salry')
plt.title("Seaborn Bar plot MBA vs non-MBA salary")
fig.suptitle("Matplotlib & Seaborn plots")
plt.show() 

Matplotlib & Seaborn plots Matplotlib Scatter plot(Age & Salary Matpltlib Bar plot MBA-Non-MBA counts 30 80000 25 70000 20 60000 Salaries Counts 50000 10 40000 5 30000 0 55 Age Seaborn strip plot MBA vs non-MBA salary No MBA or Non-MBA Seaborn Bar plot MBA vs non-MBA salary MBA • No 80000 60000 70000 - 50000 60000 40000 Salry Salry 50000 30000 40000 20000 10000 30000 0. No Yes No Yes MBA or Non-MBA MBA or Non-MBA