Part 1

Question#1:

Suppose you are provided the Dataframe below. Compute the mean of the data1 column using the labels from key1.

NOte: access data1 and call groupby with the column (a Series) at key1

• My Output may not match with your’s since we are using `np.random.randn(5)

`

In [0]:

import numpy as np

import pandas as pd

df = pd.DataFrame({‘key1’ : [‘a’, ‘a’, ‘b’, ‘b’, ‘a’],

                   ‘key2’ : [‘one’, ‘two’, ‘one’, ‘two’, ‘one’],

                   ‘data1’ : np.random.randn(5),

                   ‘data2’ : np.random.randn(5)})

df

Out[5]:

In [0]:

grouped =  #TO DO — Compute groupby

grouped  #TO DO — Compute mean

Out[9]:

key1

a    0.170724

b   -0.247155

Name: data1, dtype: float64

Question#2:

Derive the mean for the series below using Group by into states, years

In [0]:

states = np.array([‘Ohio’, ‘California’, ‘California’, ‘Ohio’, ‘Ohio’])

years = np.array([2005, 2005, 2006, 2005, 2006])

df[‘data1’] #TO DO — Compute groupby and mean

Out[12]:

California  2005   -1.028313

            2006    0.887314

Ohio        2005   -0.443082

            2006    1.045025

Name: data1, dtype: float64

In [0]:

​

Question#3:

Explain the code and output below

In [0]:

df[‘data1’].describe() #TO DO — Complete the Code

Out[14]:

count    5.000000

mean     0.003572

std      1.128177

min     -1.381624

25%     -1.028313

50%      0.495459

75%      0.887314

max      1.045025

Name: data1, dtype: float64

Question#4: Explain the code and output below

In [0]:

df[‘data2’].describe()

Out[15]:

count    5.000000

mean    -0.220463

std      0.628949

min     -1.014234

25%     -0.577313

50%     -0.325994

75%      0.296270

max      0.518958

Name: data2, dtype: float64

Reference(s) title & URL:

Part 2

Question#1:

Write a Python program to draw a line with suitable label in the x axis, y axis and a title.

In [0]:

import matplotlib.pyplot as plt

X = range(1, 50)

Y = [value * 3 for value in X]

print("Values of X:")

print(*range(1,50)) 

print("Values of Y (thrice of X):")

print(Y)

 # TO DO - Plot lines and/or markers to the Axes.

 #  TO DO - Set the x axis label of the current axis.

 #  TO DO - Set the y axis label of the current axis.

 #  TO DO - Set a title 

 #  TO DO - Display the figure.

Values of X:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49

Values of Y (thrice of X):

[3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42, 45, 48, 51, 54, 57, 60, 63, 66, 69, 72, 75, 78, 81, 84, 87, 90, 93, 96, 99, 102, 105, 108, 111, 114, 117, 120, 123, 126, 129, 132, 135, 138, 141, 144, 147]

Question#2:

Write a Python programming to display a bar chart of the popularity of programming Languages.

Sample data: Programming languages: Java, Python, PHP, JavaScript, C#, C++ Popularity: 22.2, 17.6, 8.8, 8, 7.7, 6.7

In [0]:

import matplotlib.pyplot as plt

x = ['Java', 'Python', 'PHP', 'JavaScript', 'C#', 'C++']

popularity = [22.2, 17.6, 8.8, 8, 7.7, 6.7]

x_pos = [i for i, _ in enumerate(x)]

plt.bar(x_pos, popularity, color='blue')

plt.xlabel("Languages")

plt.ylabel("Popularity")

plt.title("PopularitY of Programming Language\n" + "Worldwide, Oct 2017 compared to a year ago")

plt.xticks(x_pos, x)

# Turn on the grid

plt.minorticks_on()

plt.grid(which='major', linestyle='-', linewidth='0.5', color='red')

# Customize the minor grid

plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')

 #  TO DO - draw the plot

Question#3:

Write a Python programming to display a horizontal bar chart of the popularity of programming Languages.

In [0]:

import matplotlib.pyplot as plt

x = ['Java', 'Python', 'PHP', 'JS', 'C#', 'C++']

popularity = [22.2, 17.6, 8.8, 8, 7.7, 6.7]

x_pos = [i for i, _ in enumerate(x)]

plt.barh(x_pos, popularity, color='green')

plt.xlabel("Popularity")

plt.ylabel("Languages")

plt.title("PopularitY of Programming Language\n" + "Worldwide, Oct 2017 compared to a year ago")

plt.yticks(x_pos, x)

# Turn on the grid

plt.minorticks_on()

plt.grid(which='major', linestyle='-', linewidth='0.5', color='red')

# Customize the minor grid

plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')

#  TO DO - draw the plot

Question#4:

Write a Python programming to display a bar chart of the popularity of programming Languages. Use different color for each bar

In [0]:

import matplotlib.pyplot as plt

x = ['Java', 'Python', 'PHP', 'JavaScript', 'C#', 'C++']

popularity = [22.2, 17.6, 8.8, 8, 7.7, 6.7]

x_pos = [i for i, _ in enumerate(x)]

​

plt.bar(x_pos, popularity, color=['red', 'black', 'green', 'blue', 'yellow', 'cyan'])

​

plt.xlabel("Languages")

plt.ylabel("Popularity")

plt.title("PopularitY of Programming Language\n" + "Worldwide, Oct 2017 compared to a year ago")

plt.xticks(x_pos, x)

# Turn on the grid

plt.minorticks_on()

plt.grid(which='major', linestyle='-', linewidth='0.5', color='red')

# Customize the minor grid

plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')

 #  TO DO - draw the plot

Question#5:

Write a Python programming to create a pie chart of the popularity of programming Languages.

Sample data: Programming languages: Java, Python, PHP, JavaScript, C#, C++ Popularity: 22.2, 17.6, 8.8, 8, 7.7, 6.7

In [0]:

import matplotlib.pyplot as plt

# Data to plot

languages = 'Java', 'Python', 'PHP', 'JavaScript', 'C#', 'C++'

popuratity = [22.2, 17.6, 8.8, 8, 7.7, 6.7]

colors = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b"]

# explode 1st slice

explode = (0.1, 0, 0, 0,0,0)  

# Plot

plt.pie(popuratity, explode=explode, labels=languages, colors=colors,

autopct='%1.1f%%', shadow=True, startangle=140)

​

plt.axis('equal')

 #  TO DO - draw the plot

Question#6:

Population pyramid can be used to show either the distribution of the groups ordered by the volumne. Or it can also be used to show the stage-by-stage filtering of the population as it is used below to show how many people pass through each stage of a marketing funnel.

In [0]:

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

​

# Read data

df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/email_campaign_funnel.csv")

​

# Draw Plot

plt.figure(figsize=(13,10), dpi= 80)

group_col = 'Gender'

order_of_bars = df.Stage.unique()[::-1]

colors = [plt.cm.Spectral(i/float(len(df[group_col].unique())-1)) for i in range(len(df[group_col].unique()))]

​

for c, group in zip(colors, df[group_col].unique()):

    sns.barplot(x='Users', y='Stage', data=df.loc[df[group_col]==group, :], order=order_of_bars, color=c, label=group)

​

 #  TO DO - draw the plot  xlabel

 #  TO DO - draw the plot  ylabel

plt.yticks(fontsize=12)

 #  TO DO -  plot the title

 #  TO DO -  plot the legend

 #  TO DO -  plot the graph

Reference(s) title & URL:

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