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Pyhon Solution

The document analyzes internet usage data from around the world contained in a CSV file with over 8,800 rows and columns for country, year, cellular subscriptions, internet users, broadband subscriptions, and other related metrics. Various data cleaning and exploration steps are shown including reading the file into a DataFrame, checking for duplicates and missing data, describing statistics, and visualizing the data.

Uploaded by

curiousnavin
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
69 views45 pages

Pyhon Solution

The document analyzes internet usage data from around the world contained in a CSV file with over 8,800 rows and columns for country, year, cellular subscriptions, internet users, broadband subscriptions, and other related metrics. Various data cleaning and exploration steps are shown including reading the file into a DataFrame, checking for duplicates and missing data, describing statistics, and visualizing the data.

Uploaded by

curiousnavin
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
You are on page 1/ 45

4/24/23, 9:36 PM internet - Jupyter Notebook

In [1]:

import pandas as pd

In [2]:

df = pd.read_csv('internet.csv')

In [3]:

df.head()

Out[3]:

No. of
Unnamed: Cellular Internet Broadband
Entity Code Year Internet
0 Subscription Users(%) Subscription
Users

0 0 Afghanistan AFG 1980 0.0 0.0 0 0.0

1 1 Afghanistan AFG 1981 0.0 0.0 0 0.0

2 2 Afghanistan AFG 1982 0.0 0.0 0 0.0

3 3 Afghanistan AFG 1983 0.0 0.0 0 0.0

4 4 Afghanistan AFG 1984 0.0 0.0 0 0.0

In [4]:

df.tail()

Out[4]:

No. of
Unnamed: Cellular Internet Broadband
Entity Code Year Internet
0 Subscription Users(%) Subscription
Users

8862 8862 Zimbabwe ZWE 2016 91.793457 23.119989 3341464 1.217633

8863 8863 Zimbabwe ZWE 2017 98.985077 24.400000 3599269 1.315694

8864 8864 Zimbabwe ZWE 2018 89.404869 25.000000 3763048 1.406322

8865 8865 Zimbabwe ZWE 2019 90.102287 25.100000 3854006 1.395818

8866 8866 Zimbabwe ZWE 2020 88.755806 29.299999 4591211 1.368916

In [5]:

df.shape

Out[5]:

(8867, 8)

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In [6]:

df.columns

Out[6]:

Index(['Unnamed: 0', 'Entity', 'Code', 'Year', 'Cellular Subscription',


'Internet Users(%)', 'No. of Internet Users', 'Broadband Subscripti
on'],
dtype='object')

In [7]:

df.duplicated().sum()

Out[7]:

In [8]:

df.isnull().sum()

Out[8]:

Unnamed: 0 0
Entity 0
Code 0
Year 0
Cellular Subscription 0
Internet Users(%) 0
No. of Internet Users 0
Broadband Subscription 0
dtype: int64

In [9]:

df = df.drop('Unnamed: 0', axis = 1)

In [10]:

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8867 entries, 0 to 8866
Data columns (total 7 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Entity 8867 non-null object
1 Code 8867 non-null object
2 Year 8867 non-null int64
3 Cellular Subscription 8867 non-null float64
4 Internet Users(%) 8867 non-null float64
5 No. of Internet Users 8867 non-null int64
6 Broadband Subscription 8867 non-null float64
dtypes: float64(3), int64(2), object(2)
memory usage: 485.0+ KB

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In [11]:

df.describe()

Out[11]:

Cellular Internet No. of Internet Broadband


Year
Subscription Users(%) Users Subscription

count 8867.000000 8867.000000 8867.000000 8.867000e+03 8867.000000

mean 2000.151799 39.989614 17.043606 1.089138e+07 4.440695

std 11.812151 51.981410 26.883498 1.248841e+08 9.755705

min 1980.000000 0.000000 0.000000 0.000000e+00 0.000000

25% 1990.000000 0.000000 0.000000 0.000000e+00 0.000000

50% 2000.000000 5.501357 0.855662 1.004700e+04 0.000000

75% 2010.000000 82.231594 25.449939 8.664195e+05 2.007603

max 2020.000000 436.103027 100.000000 4.699886e+09 78.524361

In [12]:

df.nunique()

Out[12]:

Entity 229
Code 216
Year 41
Cellular Subscription 6344
Internet Users(%) 4702
No. of Internet Users 5058
Broadband Subscription 3858
dtype: int64

In [13]:

obj_cols = df.select_dtypes(include=['object']).columns

In [14]:

num_cols = df.select_dtypes(include=['int64', 'float64']).columns

In [15]:

import matplotlib.pyplot as plt


import seaborn as sns

In [16]:

import numpy as np

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In [17]:

import warnings
warnings.filterwarnings('ignore')

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In [18]:

df['Entity'].unique()

Out[18]:

array(['Afghanistan', 'Albania', 'Algeria', 'American Samoa', 'Andorra',


'Angola', 'Antigua and Barbuda', 'Argentina', 'Armenia', 'Aruba',
'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain',
'Bangladesh', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin',
'Bermuda', 'Bhutan', 'Bolivia', 'Bosnia and Herzegovina',
'Botswana', 'Brazil', 'British Virgin Islands', 'Brunei',
'Bulgaria', 'Burkina Faso', 'Burundi', 'Cambodia', 'Cameroon',
'Canada', 'Cape Verde', 'Cayman Islands',
'Central African Republic', 'Chad', 'Chile', 'China', 'Colombia',
'Comoros', 'Congo', 'Costa Rica', "Cote d'Ivoire", 'Croatia',
'Cuba', 'Curacao', 'Cyprus', 'Czechia',
'Democratic Republic of Congo', 'Denmark', 'Djibouti', 'Dominica',
'Dominican Republic', 'East Asia and Pacific', 'Ecuador', 'Egypt',
'El Salvador', 'Equatorial Guinea', 'Eritrea', 'Estonia',
'Eswatini', 'Ethiopia', 'Europe and Central Asia',
'European Union', 'Faeroe Islands', 'Fiji', 'Finland', 'France',
'French Polynesia', 'Gabon', 'Gambia', 'Georgia', 'Germany',
'Ghana', 'Gibraltar', 'Greece', 'Greenland', 'Grenada', 'Guam',
'Guatemala', 'Guinea', 'Guinea-Bissau', 'Guyana', 'Haiti',
'High income', 'Honduras', 'Hong Kong', 'Hungary', 'Iceland',
'India', 'Indonesia', 'Iran', 'Iraq', 'Ireland', 'Israel', 'Italy',
'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya', 'Kiribati',
'Kosovo', 'Kuwait', 'Kyrgyzstan', 'Laos',
'Latin America and Caribbean', 'Latvia', 'Lebanon', 'Lesotho',
'Liberia', 'Libya', 'Liechtenstein', 'Lithuania',
'Low and middle income', 'Low income', 'Lower middle income',
'Luxembourg', 'Macao', 'Madagascar', 'Malawi', 'Malaysia',
'Maldives', 'Mali', 'Malta', 'Marshall Islands', 'Mauritania',
'Mauritius', 'Mexico', 'Micronesia (country)',
'Middle East and North Africa', 'Middle income', 'Moldova',
'Monaco', 'Mongolia', 'Montenegro', 'Morocco', 'Mozambique',
'Myanmar', 'Namibia', 'Nauru', 'Nepal', 'Netherlands',
'New Caledonia', 'New Zealand', 'Nicaragua', 'Niger', 'Nigeria',
'North America', 'North Korea', 'North Macedonia',
'Northern Mariana Islands', 'Norway', 'Oman', 'Pakistan', 'Palau',
'Palestine', 'Panama', 'Papua New Guinea', 'Paraguay', 'Peru',
'Philippines', 'Poland', 'Portugal', 'Puerto Rico', 'Qatar',
'Romania', 'Russia', 'Rwanda', 'Saint Kitts and Nevis',
'Saint Lucia', 'Saint Vincent and the Grenadines', 'Samoa',
'San Marino', 'Sao Tome and Principe', 'Saudi Arabia', 'Senegal',
'Serbia', 'Seychelles', 'Sierra Leone', 'Singapore',
'Sint Maarten (Dutch part)', 'Slovakia', 'Slovenia',
'Solomon Islands', 'Somalia', 'South Africa', 'South Asia',
'South Korea', 'South Sudan', 'Spain', 'Sri Lanka',
'Sub-Saharan Africa', 'Sudan', 'Suriname', 'Sweden', 'Switzerland',
'Syria', 'Tajikistan', 'Tanzania', 'Thailand', 'Timor', 'Togo',
'Tonga', 'Trinidad and Tobago', 'Tunisia', 'Turkey',
'Turkmenistan', 'Turks and Caicos Islands', 'Tuvalu', 'Uganda',
'Ukraine', 'United Arab Emirates', 'United Kingdom',
'United States', 'United States Virgin Islands',
'Upper middle income', 'Uruguay', 'Uzbekistan', 'Vanuatu',
'Venezuela', 'Vietnam', 'World', 'Yemen', 'Zambia', 'Zimbabwe'],
dtype=object)

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In [19]:

df['Entity'].value_counts()

Out[19]:

Afghanistan 41
Mongolia 41
Mali 41
Malta 41
Mauritania 41
..
Palau 16
Curacao 12
South Sudan 11
Kosovo 6
Sint Maarten (Dutch part) 1
Name: Entity, Length: 229, dtype: int64

In [20]:

df['Code'].unique()

Out[20]:

array(['AFG', 'ALB', 'DZA', 'ASM', 'AND', 'AGO', 'ATG', 'ARG', 'ARM',


'ABW', 'AUS', 'AUT', 'AZE', 'BHS', 'BHR', 'BGD', 'BRB', 'BLR',
'BEL', 'BLZ', 'BEN', 'BMU', 'BTN', 'BOL', 'BIH', 'BWA', 'BRA',
'VGB', 'BRN', 'BGR', 'BFA', 'BDI', 'KHM', 'CMR', 'CAN', 'CPV',
'CYM', 'CAF', 'TCD', 'CHL', 'CHN', 'COL', 'COM', 'COG', 'CRI',
'CIV', 'HRV', 'CUB', 'CUW', 'CYP', 'CZE', 'COD', 'DNK', 'DJI',
'DMA', 'DOM', 'Region', 'ECU', 'EGY', 'SLV', 'GNQ', 'ERI', 'EST',
'SWZ', 'ETH', 'FRO', 'FJI', 'FIN', 'FRA', 'PYF', 'GAB', 'GMB',
'GEO', 'DEU', 'GHA', 'GIB', 'GRC', 'GRL', 'GRD', 'GUM', 'GTM',
'GIN', 'GNB', 'GUY', 'HTI', 'HND', 'HKG', 'HUN', 'ISL', 'IND',
'IDN', 'IRN', 'IRQ', 'IRL', 'ISR', 'ITA', 'JAM', 'JPN', 'JOR',
'KAZ', 'KEN', 'KIR', 'OWID_KOS', 'KWT', 'KGZ', 'LAO', 'LVA', 'LBN',
'LSO', 'LBR', 'LBY', 'LIE', 'LTU', 'LUX', 'MAC', 'MDG', 'MWI',
'MYS', 'MDV', 'MLI', 'MLT', 'MHL', 'MRT', 'MUS', 'MEX', 'FSM',
'MDA', 'MCO', 'MNG', 'MNE', 'MAR', 'MOZ', 'MMR', 'NAM', 'NRU',
'NPL', 'NLD', 'NCL', 'NZL', 'NIC', 'NER', 'NGA', 'PRK', 'MKD',
'MNP', 'NOR', 'OMN', 'PAK', 'PLW', 'PSE', 'PAN', 'PNG', 'PRY',
'PER', 'PHL', 'POL', 'PRT', 'PRI', 'QAT', 'ROU', 'RUS', 'RWA',
'KNA', 'LCA', 'VCT', 'WSM', 'SMR', 'STP', 'SAU', 'SEN', 'SRB',
'SYC', 'SLE', 'SGP', 'SXM', 'SVK', 'SVN', 'SLB', 'SOM', 'ZAF',
'KOR', 'SSD', 'ESP', 'LKA', 'SDN', 'SUR', 'SWE', 'CHE', 'SYR',
'TJK', 'TZA', 'THA', 'TLS', 'TGO', 'TON', 'TTO', 'TUN', 'TUR',
'TKM', 'TCA', 'TUV', 'UGA', 'UKR', 'ARE', 'GBR', 'USA', 'VIR',
'URY', 'UZB', 'VUT', 'VEN', 'VNM', 'OWID_WRL', 'YEM', 'ZMB', 'ZW
E'],
dtype=object)

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In [21]:

df['Entity'].value_counts()

Out[21]:

Afghanistan 41
Mongolia 41
Mali 41
Malta 41
Mauritania 41
..
Palau 16
Curacao 12
South Sudan 11
Kosovo 6
Sint Maarten (Dutch part) 1
Name: Entity, Length: 229, dtype: int64

In [22]:

df['Year'].unique()

Out[22]:

array([1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990,
1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001,
2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020], dtype=int64)

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In [23]:

df['Year'].value_counts()

Out[23]:

2020 223
2019 223
2015 222
2012 222
2010 222
1990 222
2004 222
2003 221
2002 221
2014 220
2001 219
2017 219
2013 219
2009 219
2007 219
2005 219
2000 219
1999 218
2011 218
2016 217
1998 217
2008 217
2006 216
1996 216
1997 215
1980 215
1995 215
1992 213
1993 213
1994 213
1981 213
1982 213
1983 212
1991 211
1986 211
1984 211
1988 210
1985 210
1987 209
1989 208
2018 205
Name: Year, dtype: int64

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In [24]:

for i in num_cols:
plt.figure(figsize=(15,6))
sns.histplot(df[i], kde = True, bins = 20, palette = 'hls')
plt.xticks(rotation = 90)
plt.show()

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In [25]:

for i in num_cols:
plt.figure(figsize=(15,6))
sns.distplot(df[i], kde = True, bins = 20)
plt.xticks(rotation = 90)
plt.show()

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In [26]:

for i in num_cols:
plt.figure(figsize=(15,6))
sns.boxplot(df[i], data = df, palette = 'hls')
plt.xticks(rotation = 90)
plt.show()

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In [27]:

for i in num_cols:
plt.figure(figsize=(15,6))
sns.violinplot(df[i], data = df, palette = 'hls')
plt.xticks(rotation = 90)
plt.show()

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In [28]:

import plotly.express as px

for i in num_cols:
fig = px.histogram(df, x=i, nbins=20)
fig.show()

1000

800
count

600

400

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In [29]:

for col in num_cols:


fig = px.box(df, y=col, title=f"{col} Boxplot")
fig.show()

Year Boxplot

2020

2015

2010

2005
Year

2000

1995

In [30]:

for col in num_cols:


fig = px.violin(df, y=col, title=f"{col} Boxplot")
fig.show()

Year Boxplot

2020

2010
Year

2000

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In [31]:

for i in num_cols:
if i != 'Year':
plt.figure(figsize=(15,6))
sns.barplot(x = df['Year'], y = df[i], data = df, ci = None, palette = 'hls')
plt.xticks(rotation = 90)
plt.show()

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In [32]:

for i in num_cols:
if i != 'Year':
fig = px.bar(df, x='Year', y=i, color='Year')
fig.show()

25k

20k
Cellular Subscription

15k

10k

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In [33]:

for i in num_cols:
for j in num_cols:
if i != j:
plt.figure(figsize=(15,6))
sns.lineplot(x = df[i], y = df[j], data = df, ci = None, palette = 'hls')
plt.xticks(rotation = 90)
plt.show()

In [34]:

temp=df[~(df['Code']=='Region')]

In [35]:

temp1= temp.groupby(['Entity','Year']).sum().reset_index()

In [36]:

df1=temp1[(temp1['Entity']=='India')|(temp1['Entity']=='China')|(temp1['Entity']=='Unite

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In [37]:

df1

Out[37]:

Cellular Internet No. of Internet Broadband


Entity Year
Subscription Users(%) Users Subscription

1579 China 1980 0.000000 0.000000 0 0.000000

1580 China 1981 0.000000 0.000000 0 0.000000

1581 China 1982 0.000000 0.000000 0 0.000000

1582 China 1983 0.000000 0.000000 0 0.000000

1583 China 1984 0.000000 0.000000 0 0.000000

... ... ... ... ... ... ...

United
7900 2016 122.594551 85.544418 279910069 32.727173
States

United
7901 2017 123.044838 87.274887 287824925 33.283627
States

United
7902 2018 106.464684 88.498901 293940279 33.860367
States

United
7903 2019 108.092651 89.430283 298983035 34.725369
States

United
7904 2020 106.185554 90.900002 305371298 36.608768
States

120 rows × 6 columns

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In [38]:

fig = px.line(df1, x='Year', y='Cellular Subscription', color='Entity', markers=True,


title='Mobile phone subscriptions per 100 people 1980 to 2020')
fig.update_layout(hovermode='x unified')
fig.show()

Mobile phone subscriptions per 100 people 1980 to 2020

120

100
Cellular Subscription

80

60

40

20

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In [39]:

fig = px.scatter(df1, x="Year", y="Cellular Subscription", color="Entity", trendline="ol


fig.show()

Mobile phone subscriptions per 100 people 1980 to 2020

120

100

80
Cellular Subscription

60

40

20

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In [40]:

fig = px.area(df1, x="Year", y="Cellular Subscription", color="Entity", title="Mobile ph


fig.show()

Mobile phone subscriptions per 100 people 1980 to 2020

300

250
Cellular Subscription

200

150

100

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In [41]:

fig = px.area(df1, x="Year", y="Cellular Subscription", color="Entity", title="Mobile ph


labels={"Year":"Year", "Cellular Subscription":"Cellular Subscription per 1
fig.update_layout(yaxis_range=[0,100])
fig.show()

Mobile phone subscriptions per 100 people 1980 to 2020

100
llular Subscription per 100 people

80

60

40

20

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In [42]:

fig = px.box(df1, x="Entity", y="Cellular Subscription", color="Entity", title="Mobile p


fig.show()

Mobile phone subscriptions per 100 people 1980 to 2020

120

100
Cellular Subscription

80

60

40

20

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In [43]:

fig = px.violin(df1, x="Entity", y="Cellular Subscription", color="Entity", box=True, po


fig.show()

Mobile phone subscriptions per 100 people 1980 to 2020

150
Cellular Subscription

100

50

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In [44]:

fig = px.line(df1, x='Year', y='Broadband Subscription', color='Entity', markers=True,


title='Landline Internet subscriptions per 100 people, 1998 to 2020')
fig.update_layout(hovermode='x unified')
fig.show()

Landline Internet subscriptions per 100 people, 1998 to 2020

35

30
Broadband Subscription

25

20

15

10

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In [45]:

fig = px.scatter(df1, x="Year", y="Broadband Subscription", color="Entity", trendline="o


fig.show()

Broadband subscriptions per 100 people 1980 to 2020

30
Broadband Subscription

20

10

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In [46]:

fig = px.area(df1, x="Year", y="Broadband Subscription", color="Entity", title="Broadban


fig.show()

Broadband subscriptions per 100 people 1980 to 2020

70

60
Broadband Subscription

50

40

30

20

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In [47]:

fig = px.area(df1, x="Year", y="Broadband Subscription", color="Entity", title="Broadban


labels={"Year":"Year", "Broadband Subscription":"Broadband Subscription per
fig.update_layout(yaxis_range=[0,100])
fig.show()

Broadband subscriptions per 100 people 1980 to 2020

100
adband Subscription per 100 people

80

60

40

20

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In [48]:

fig = px.box(df1, x="Entity", y="Broadband Subscription", color="Entity", title="Broadba


fig.show()

Broadband subscriptions per 100 people 1980 to 2020

35

30
Broadband Subscription

25

20

15

10

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In [49]:

fig = px.violin(df1, x="Entity", y="Broadband Subscription", color="Entity", title="Broa


fig.show()

Broadband subscriptions per 100 people 1980 to 2020

50

40
Broadband Subscription

30

20

10

In [50]:

df2 = df[df['Code']=='Region']

In [51]:

df2['Entity'].unique()

Out[51]:

array(['East Asia and Pacific', 'Europe and Central Asia',


'European Union', 'High income', 'Latin America and Caribbean',
'Low and middle income', 'Low income', 'Lower middle income',
'Middle East and North Africa', 'Middle income', 'North America',
'South Asia', 'Sub-Saharan Africa', 'Upper middle income'],
dtype=object)

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In [52]:

temp=df2[(df2['Entity']=='East Asia and Pacific')|(df2['Entity']=='Europe and Central As

In [53]:

fig = px.line(df2, x='Year', y='Cellular Subscription', color='Entity', markers=True,


title='Mobile Phone subscriptions per 100 people, 1998 to 2020')
fig.update_layout(hovermode='x unified')
fig.show()

Mobile Phone subscriptions per 100 people, 1998 to 2020

En
120

100
Cellular Subscription

80

60

40

20

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In [54]:

fig = px.scatter(df2, x="Year", y="Cellular Subscription", color="Entity", trendline="ol


fig.show()

Mobile phone subscriptions per 100 people 1980 to 2020

150
En

100
Cellular Subscription

50

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In [55]:

fig = px.area(df2, x="Year", y="Cellular Subscription", color="Entity", title="Mobile ph


fig.show()

Mobile phone subscriptions per 100 people 1980 to 2020

En
1400

1200
Cellular Subscription

1000

800

600

400

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In [56]:

fig = px.area(df2, x="Year", y="Cellular Subscription", color="Entity", title="Mobile ph


labels={"Year":"Year", "Cellular Subscription":"Cellular Subscription per 1
fig.update_layout(yaxis_range=[0,100])
fig.show()

Mobile phone subscriptions per 100 people 1980 to 2020

100
En
llular Subscription per 100 people

80

60

40

20

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In [57]:

fig = px.box(df2, x="Entity", y="Cellular Subscription", color="Entity", title="Mobile p


fig.show()

Mobile phone subscriptions per 100 people 1980 to 2020

En
120

100
Cellular Subscription

80

60

40

20

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In [58]:

fig = px.violin(df2, x="Entity", y="Cellular Subscription", color="Entity", box=True, po


fig.show()

Mobile phone subscriptions per 100 people 1980 to 2020

En

150
Cellular Subscription

100

50

−50

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In [59]:

fig = px.line(df2, x='Year', y='Broadband Subscription', color='Entity', markers=True,


title='Landline Internet subscriptions per 100 people, 1998 to 2020')
fig.update_layout(hovermode='x unified')
fig.show()

Landline Internet subscriptions per 100 people, 1998 to 2020

En
35

30
Broadband Subscription

25

20

15

10

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In [60]:

fig = px.scatter(df2, x="Year", y="Broadband Subscription", color="Entity", trendline="o


fig.show()

Landline Internet subscriptions per 100 people 1980 to 2020

40
En

30
Broadband Subscription

20

10

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In [61]:

fig = px.area(df2, x="Year", y="Broadband Subscription", color="Entity", title="Landline


fig.show()

Landline Internet subscriptions per 100 people 1980 to 2020

250 En

200
Broadband Subscription

150

100

50

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In [62]:

fig = px.area(df2, x="Year", y="Broadband Subscription", color="Entity", title="Landline


labels={"Year":"Year", "Broadband Subscription":"Landline Internet per 100
fig.update_layout(yaxis_range=[0,100])
fig.show()

Landline Internet subscriptions per 100 people 1980 to 2020

100
En
Landline Internet per 100 people

80

60

40

20

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In [63]:

fig = px.box(df2, x="Entity", y="Broadband Subscription", color="Entity", title="Landlin


fig.show()

Landline Internet subscriptions per 100 people 1980 to 2020

En

30
Broadband Subscription

20

10

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In [64]:

fig = px.violin(df2, x="Entity", y="Broadband Subscription", color="Entity", title="Land


fig.show()

Landline Internet subscriptions per 100 people 1980 to 2020

En
50

40
Broadband Subscription

30

20

10

−10

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