For the challenge, use the Google Analytics Demo Account, Master View.
Exercise:
-
Create one geolocation segment that combines traffic originating from either RI or NY.
-
Export January 2019 users and pageviews by day for the segment to a csv file.
-
Use Python to read the csv file and create the following charts:
a. Pageviews by week for the segment
b. Pageviews/user by day for the segment
Requirements:
- All code must be written in Python and must be in a Jupyter notebook
- Charts must be embedded in the Jupyter notebook
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('classic')
pageviews = pd.read_csv("/Users/--/Deskptop/January 2019 pageviews.csv", skiprows=5, sep=',',
thousands=',', nrows=31)
pageviews.head()
| Day Index | Pageviews | |
|---|---|---|
| 0 | 1/1/19 | 229 |
| 1 | 1/2/19 | 521 |
| 2 | 1/3/19 | 467 |
| 3 | 1/4/19 | 572 |
| 4 | 1/5/19 | 426 |
pageviews.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 31 entries, 0 to 30
Data columns (total 2 columns):
Day Index 31 non-null object
Pageviews 31 non-null int64
dtypes: int64(1), object(1)
memory usage: 576.0+ bytes
pageviews['Day Index'] = pd.to_datetime(pageviews['Day Index'])
pageviews.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 31 entries, 0 to 30
Data columns (total 2 columns):
Day Index 31 non-null datetime64[ns]
Pageviews 31 non-null int64
dtypes: datetime64[ns](1), int64(1)
memory usage: 576.0 bytes
new_pageviews = pageviews.groupby([
pd.Grouper(key='Day Index',
freq='W-MON')])['Pageviews'].sum().reset_index().sort_values('Day Index')
new_pageviews
| Day Index | Pageviews | |
|---|---|---|
| 0 | 2019-01-07 | 3431 |
| 1 | 2019-01-14 | 4676 |
| 2 | 2019-01-21 | 3240 |
| 3 | 2019-01-28 | 4109 |
| 4 | 2019-02-04 | 2355 |
new_pageviews.plot(x='Day Index', y='Pageviews', figsize=(15,8),
title="Pageviews by week", legend=False)
plt.xlabel('Week')
plt.ylabel('Day Index')
plt.savefig('Pageviews by week.jpg')
users = pd.read_csv("/Users/--/Desktop/January 2019 users.csv", skiprows=5, sep=',',
thousands=',', nrows=31)
users.head()
| Day index | Users | |
|---|---|---|
| 0 | 1/1/19 | 38 |
| 1 | 1/2/19 | 98 |
| 2 | 1/3/19 | 82 |
| 3 | 1/4/19 | 88 |
| 4 | 1/5/19 | 50 |
users.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 31 entries, 0 to 30
Data columns (total 2 columns):
Day Index 31 non-null object
Users 31 non-null int64
dtypes: int64(1), object(1)
memory usage: 576.0+ bytes
users['Day Index'] = pd.to_datetime(users['Day Index'])
users.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 31 entries, 0 to 30
Data columns (total 2 columns):
Day Index 31 non-null datetime64[ns]
Users 31 non-null int64
dtypes: datetime64[ns](1), int64(1)
memory usage: 576.0 bytes
users.plot(figsize=(15,8),
x='Day Index',
y='Users',
title='Users by day')
plt.ylabel('Number of pageviews/users')
plt.xlabel('Date')
plt.savefig("Pageviews/users per day.jpg")
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