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PDS_UberDrive_Questions_Final - Jupyter Notebook

Import the dataset and Load the dataset. Load the necessary libraries.

In [22]:

1 2 3 4 5 6 7 8 9

import pandas as pd import numpy as np import seaborn as sns sns.set(color_codes=True) import matplotlib.pyplot as plt import seaborn as sns dataset = pd.read_csv('uberdrive.csv') dataset

Out[22]: START_DATE*

END_DATE*

CATEGORY*

START*

STOP*

MILES*

PURPOSE*

0

01-01-2016 21:11

01-01-2016 21:17

Business

Fort Pierce

Fort Pierce

5.1

Meal/Entertain

1

01-02-2016 01:25

01-02-2016 01:37

Business

Fort Pierce

Fort Pierce

5.0

NaN

2

01-02-2016 20:25

01-02-2016 20:38

Business

Fort Pierce

Fort Pierce

4.8

Errand/Supplies

3

01-05-2016 17:31

01-05-2016 17:45

Business

Fort Pierce

Fort Pierce

4.7

Meeting

4

01-06-2016 14:42

01-06-2016 15:49

Business

Fort Pierce

West Palm Beach

63.7

Customer Visit

...

...

...

...

...

...

...

...

1151

12/31/2016 13:24

12/31/2016 13:42

Business

Karachi

Unknown Location

3.9

Temporary Site

1152

12/31/2016 15:03

12/31/2016 15:38

Business

Unknown Location

Unknown Location

16.2

Meeting

1153

12/31/2016 21:32

12/31/2016 21:50

Business

Katunayake

Gampaha

6.4

Temporary Site

1154

12/31/2016 22:08

12/31/2016 23:51

Business

Gampaha

Ilukwatta

48.2

Temporary Site

1155

Totals

NaN

NaN

NaN

NaN

12204.7

NaN

1156 rows × 7 columns In [ ]:

1

Q1. Show the last 10 records of the dataset. (2 point)

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In [3]:

1 dataset.tail(10)

Out[3]: START_DATE*

END_DATE*

CATEGORY*

START*

STOP*

MILES*

PURPOSE*

1146

12/30/2016 11:31

12/30/2016 11:56

Business

Karachi

Karachi

2.9

Errand/Supplies

1147

12/30/2016 15:41

12/30/2016 16:03

Business

Karachi

Karachi

4.6

Errand/Supplies

1148

12/30/2016 16:45

12/30/2016 17:08

Business

Karachi

Karachi

4.6

Meeting

1149

12/30/2016 23:06

12/30/2016 23:10

Business

Karachi

Karachi

0.8

Customer Visit

1150

12/31/2016 1:07

12/31/2016 1:14

Business

Karachi

Karachi

0.7

Meeting

1151

12/31/2016 13:24

12/31/2016 13:42

Business

Karachi

Unknown Location

3.9

Temporary Site

1152

12/31/2016 15:03

12/31/2016 15:38

Business

Unknown Location

Unknown Location

16.2

Meeting

1153

12/31/2016 21:32

12/31/2016 21:50

Business

Katunayake

Gampaha

6.4

Temporary Site

1154

12/31/2016 22:08

12/31/2016 23:51

Business

Gampaha

Ilukwatta

48.2

Temporary Site

1155

Totals

NaN

NaN

NaN

NaN

12204.7

NaN

Q2. Show the first 10 records of the dataset. (2 points)

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1 dataset.head(10)

In [21]: Out[21]:

START_DATE*

END_DATE*

CATEGORY*

START*

STOP*

MILES*

PURPOSE*

0

01-01-2016 21:11

01-01-2016 21:17

Business

Fort Pierce

Fort Pierce

5.1

Meal/Entertain

1

01-02-2016 01:25

01-02-2016 01:37

Business

Fort Pierce

Fort Pierce

5.0

NaN

2

01-02-2016 20:25

01-02-2016 20:38

Business

Fort Pierce

Fort Pierce

4.8

Errand/Supplies

3

01-05-2016 17:31

01-05-2016 17:45

Business

Fort Pierce

Fort Pierce

4.7

Meeting

4

01-06-2016 14:42

01-06-2016 15:49

Business

Fort Pierce

West Palm Beach

63.7

Customer Visit

5

01-06-2016 17:15

01-06-2016 17:19

Business

West Palm Beach

West Palm Beach

4.3

Meal/Entertain

6

01-06-2016 17:30

01-06-2016 17:35

Business

West Palm Beach

Palm Beach

7.1

Meeting

7

01-07-2016 13:27

01-07-2016 13:33

Business

Cary

Cary

0.8

Meeting

8

01-10-2016 08:05

01-10-2016 08:25

Business

Cary

Morrisville

8.3

Meeting

9

01-10-2016 12:17

01-10-2016 12:44

Business

Jamaica

New York

16.5

Customer Visit

In [ ]:

1

In [ ]:

1

Q3. Show the dimension of the dataset. (2 points) In [5]:

1 dataset.ndim

Out[5]: 2 In [ ]:

1

Q4. Show the size of the dataset. (2 points) In [6]:

1 dataset.size

Out[6]: 8092 In [ ]:

1

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Q5. Print the information about all the variables of the data set. (2 points) In [7]:

1 dataset.info() <class 'pandas.core.frame.DataFrame'> RangeIndex: 1156 entries, 0 to 1155 Data columns (total 7 columns): # Column Non-Null Count Dtype --- ------------------- ----0 START_DATE* 1156 non-null object 1 END_DATE* 1155 non-null object 2 CATEGORY* 1155 non-null object 3 START* 1155 non-null object 4 STOP* 1155 non-null object 5 MILES* 1156 non-null float64 6 PURPOSE* 653 non-null object dtypes: float64(1), object(6) memory usage: 63.3+ KB

In [ ]:

1

Q6. Check for missing values. (2 points) In [8]:

1 dataset.isnull().sum()

Out[8]: START_DATE* END_DATE* CATEGORY* START* STOP* MILES* PURPOSE* dtype: int64 In [ ]:

0 1 1 1 1 0 503

1 (END_DATE','CATEGORY','START','STOP','PURPOSE') These are the variables for which we have missing values

Q7. How many missing values are present? (2 points) In [9]:

1 dataset.isnull().sum().sum()

Out[9]: 507 In [ ]:

1 a total of 507 missing values 1 for each 'END_DATE','CATEGORY','START','STOP' variable and 503 in 'PURPOSE' variable

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Q8. Get the initial data (name it 'df') with dropping the NA values. (2 points) In [10]:

1 df=dataset.dropna() 2 print(df) 0 2 3 4 5 ...

1150 1151 1152 1153 1154

START_DATE* 01-01-2016 21:11 01-02-2016 20:25 01-05-2016 17:31 01-06-2016 14:42 01-06-2016 17:15 ...

12/31/2016 1:07 12/31/2016 13:24 12/31/2016 15:03 12/31/2016 21:32 12/31/2016 22:08

END_DATE* CATEGORY* 01-01-2016 21:17 Business 01-02-2016 20:38 Business 01-05-2016 17:45 Business 01-06-2016 15:49 Business 01-06-2016 17:19 Business ... ...

12/31/2016 1:14 Business 12/31/2016 13:42 Business 12/31/2016 15:38 Business 12/31/2016 21:50 Business 12/31/2016 23:51 Business

0 2 3 4 5 ...

1150 1151 1152 1153 1154

STOP* Fort Pierce Fort Pierce Fort Pierce West Palm Beach West Palm Beach ...

Karachi Unknown Location Unknown Location Gampaha Ilukwatta

MILES* 5.1 4.8 4.7 63.7 4.3 ...

0.7 3.9 16.2 6.4 48.2

START* Fort Pierce Fort Pierce Fort Pierce Fort Pierce West Palm Beach ...

Karachi Karachi Unknown Location Katunayake Gampaha

\

PURPOSE* Meal/Entertain Errand/Supplies Meeting Customer Visit Meal/Entertain ...

Meeting Temporary Site Meeting Temporary Site Temporary Site

[653 rows x 7 columns] In [ ]:

1

Q9. Get the summary of the original data (before dropping the 'na' values). (2 points) In [11]:

1 dataset.describe().T

Out[11]: MILES*

In [ ]:

count

mean

std

min

25%

50%

75%

max

1156.0

21.115398

359.299007

0.5

2.9

6.0

10.4

12204.7

1 it is showing descriptive information only for one column which is MILES, as it is the only column which has some numeric information, rest other columns hodls either string information or date values

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Q10. Check the information of the new dataframe. (2 points) In [12]:

1 df.info() <class 'pandas.core.frame.DataFrame'> Int64Index: 653 entries, 0 to 1154 Data columns (total 7 columns): # Column Non-Null Count Dtype --- ------------------- ----0 START_DATE* 653 non-null object 1 END_DATE* 653 non-null object 2 CATEGORY* 653 non-null object 3 START* 653 non-null object 4 STOP* 653 non-null object 5 MILES* 653 non-null float64 6 PURPOSE* 653 non-null object dtypes: float64(1), object(6) memory usage: 40.8+ KB

In [ ]:

1

Q11. Get the unique start destinations. (2 points) Note: This question is based on the dataframe with no 'na' values in the 'START' variable.

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In [13]:

1 dataset['START*'].dropna().unique()

Out[13]: array(['Fort Pierce', 'West Palm Beach', 'Cary', 'Jamaica', 'New York', 'Elmhurst', 'Midtown', 'East Harlem', 'Flatiron District', 'Midtown East', 'Hudson Square', 'Lower Manhattan', "Hell's Kitchen", 'Downtown', 'Gulfton', 'Houston', 'Eagan Park', 'Morrisville', 'Durham', 'Farmington Woods', 'Whitebridge', 'Lake Wellingborough', 'Fayetteville Street', 'Raleigh', 'Hazelwood', 'Fairmont', 'Meredith Townes', 'Apex', 'Chapel Hill', 'Northwoods', 'Edgehill Farms', 'Tanglewood', 'Preston', 'Eastgate', 'East Elmhurst', 'Jackson Heights', 'Long Island City', 'Katunayaka', 'Unknown Location', 'Colombo', 'Nugegoda', 'Islamabad', 'R?walpindi', 'Noorpur Shahan', 'Heritage Pines', 'Westpark Place', 'Waverly Place', 'Wayne Ridge', 'Weston', 'East Austin', 'West University', 'South Congress', 'The Drag', 'Congress Ave District', 'Red River District', 'Georgian Acres', 'North Austin', 'Coxville', 'Convention Center District', 'Austin', 'Katy', 'Sharpstown', 'Sugar Land', 'Galveston', 'Port Bolivar', 'Washington Avenue', 'Briar Meadow', 'Latta', 'Jacksonville', 'Couples Glen', 'Kissimmee', 'Lake Reams', 'Orlando', 'Sand Lake Commons', 'Sky Lake', 'Daytona Beach', 'Ridgeland', 'Florence', 'Meredith', 'Holly Springs', 'Chessington', 'Burtrose', 'Parkway', 'Mcvan', 'Capitol One', 'University District', 'Seattle', 'Redmond', 'Bellevue', 'San Francisco', 'Palo Alto', 'Sunnyvale', 'Newark', 'Menlo Park', 'Old City', 'Savon Height', 'Kilarney Woods', 'Townes at Everett Crossing', 'Huntington Woods', 'Seaport', 'Medical Centre', 'Rose Hill', 'Soho', 'Tribeca', 'Financial District', 'Oakland', 'Emeryville', 'Berkeley', 'Kenner', 'CBD', 'Lower Garden District', 'Lakeview', 'Storyville', 'New Orleans', 'Metairie', 'Chalmette', 'Arabi', 'Pontchartrain Shores', 'Marigny', 'Covington', 'Mandeville', 'Jamestown Court', 'Summerwinds', 'Parkwood', 'Pontchartrain Beach', 'St Thomas', 'Banner Elk', 'Elk Park', 'Newland', 'Boone', 'Stonewater', 'Lexington Park at Amberly', 'Arlington Park at Amberly', 'Arlington', 'Kalorama Triangle', 'K Street', 'West End', 'Connecticut Avenue', 'Columbia Heights', 'Washington', 'Wake Forest', 'Lahore', 'Karachi', 'SOMISSPO', 'West Berkeley', 'North Berkeley Hills', 'San Jose', 'Eagle Rock', 'Winston Salem', 'Asheville', 'Topton', 'Hayesville', 'Bryson City', 'Almond', 'Mebane', 'Agnew', 'Cory', 'Renaissance', 'Santa Clara', 'NOMA', 'Sunnyside', 'Ingleside', 'Central', 'Tenderloin', 'College Avenue', 'South', 'Southside', 'South Berkeley', 'Mountain View', 'El Cerrito', 'Krendle Woods', 'Wake Co.', 'Fuquay-Varina', 'Rawalpindi', 'Katunayake', 'Gampaha'], dtype=object)

In [ ]:

1

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In [14]:

1 start_destination = dataset['START*'].dropna() 2 unique_start = set(start_destination) # set function provides unique values 3 unique_start

Out[14]: {'Agnew', 'Almond', 'Apex', 'Arabi', 'Arlington', 'Arlington Park at Amberly', 'Asheville', 'Austin', 'Banner Elk', 'Bellevue', 'Berkeley', 'Boone', 'Briar Meadow', 'Bryson City', 'Burtrose', 'CBD', 'Capitol One', 'Cary', 'Central', 'Ch l tt ' In [ ]:

1

Q12. What is the total number of unique start destinations? (2 points) Note: This question is based on the dataframe with no 'na' values in the 'START' variable.

In [15]:

1 dataset['START*'].dropna().nunique()

Out[15]: 176 In [ ]:

1 There are 176 unique start destinations in total and there names are:-

Q13. Print the total number of unique stop destinations. (2 points) Note: This question is based on the dataframe with no 'na' values in the 'STOP' variable.

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In [16]:

1 dataset['STOP*'].dropna().unique()

Out[16]: array(['Fort Pierce', 'West Palm Beach', 'Palm Beach', 'Cary', 'Morrisville', 'New York', 'Queens', 'East Harlem', 'NoMad', 'Midtown', 'Midtown East', 'Hudson Square', 'Lower Manhattan', "Hell's Kitchen", 'Queens County', 'Gulfton', 'Downtown', 'Houston', 'Jamestown Court', 'Durham', 'Whitebridge', 'Lake Wellingborough', 'Raleigh', 'Umstead', 'Hazelwood', 'Westpark Place', 'Meredith Townes', 'Leesville Hollow', 'Apex', 'Chapel Hill', 'Williamsburg Manor', 'Macgregor Downs', 'Edgehill Farms', 'Northwoods', 'Tanglewood', 'Preston', 'Walnut Terrace', 'Jackson Heights', 'East Elmhurst', 'Midtown West', 'Long Island City', 'Jamaica', 'Unknown Location', 'Colombo', 'Nugegoda', 'Katunayaka', 'Islamabad', 'R?walpindi', 'Noorpur Shahan', 'Heritage Pines', 'Waverly Place', 'Wayne Ridge', 'Depot Historic District', 'Weston', 'West University', 'South Congress', 'Arts District', 'Congress Ave District', 'Red River District', 'The Drag', 'Convention Center District', 'North Austin', 'Coxville', 'Katy', 'Alief', 'Sharpstown', 'Sugar Land', 'Galveston', 'Port Bolivar', 'Washington Avenue', 'Briar Meadow', 'Greater Greenspoint', 'Latta', 'Jacksonville', 'Kissimmee', 'Isles of Buena Vista', 'Orlando', 'Lake Reams', 'Vista East', 'Sky Lake', 'Sand Lake Commons', 'Daytona Beach', 'Ridgeland', 'Florence', 'Cedar Hill', 'Holly Springs', 'Harden Place', 'Chessington', 'Burtrose', 'Parkway', 'Capitol One', 'University District', 'Redmond', 'Bellevue', 'Seattle', 'Mcvan', 'Palo Alto', 'Sunnyvale', 'Newark', 'Menlo Park', 'San Francisco', 'Parkway Museums', 'Hog Island', 'Savon Height', 'Kildaire Farms', 'Kilarney Woods', 'Gramercy-Flatiron', 'Tudor City', 'Soho', 'Tribeca', 'Financial District', 'Kips Bay', 'Emeryville', 'Berkeley', 'Oakland', 'Bay Farm Island', 'New Orleans', 'Lower Garden District', 'Lakeview', 'Storyville', 'Faubourg Marigny', 'Metairie', 'Kenner', 'Bywater', 'Chalmette', 'Arabi', 'Pontchartrain Shores', 'Marigny', 'Covington', 'Mandeville', 'Summerwinds', 'Parkwood', 'Pontchartrain Beach', 'CBD', 'St Thomas', 'Banner Elk', 'Elk Park', 'Newland', 'Boone', 'Stonewater', 'Lexington Park at Amberly', 'Arlington Park at Amberly', 'Washington', 'K Street', 'Kalorama Triangle', 'Northwest Rectangle', 'Columbia Heights', 'Arlington', 'Farmington Woods', 'Wake Forest', 'Lahore', 'Karachi', 'French Quarter', 'North Berkeley Hills', 'Southside', 'San Jose', 'Eagle Rock', 'Huntington Woods', 'Winston Salem', 'Asheville', 'Topton', 'Hayesville', 'Bryson City', 'Almond', 'Mebane', 'Santa Clara', 'Cory', 'Agnew', 'Renaissance', 'West Berkeley', 'Central', 'Sunnyside', 'Ingleside', 'Potrero Flats', 'SOMISSPO', 'Tenderloin', 'College Avenue', 'South', 'Southwest Berkeley', 'South Berkeley', 'Mountain View', 'El Cerrito', 'Wake Co.', 'Fuquay-Varina', 'Rawalpindi', 'Gampaha', 'Ilukwatta'], dtype=object) In [ ]:

1

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In [17]:

1 stop_destination = dataset['STOP*'].dropna() 2 unique_stop = set(stop_destination) # set function provides unique values 3 unique_stop

Out[17]: {'Agnew', 'Alief', 'Almond', 'Apex', 'Arabi', 'Arlington', 'Arlington Park at Amberly', 'Arts District', 'Asheville', 'Banner Elk', 'Bay Farm Island', 'Bellevue', 'Berkeley', 'Boone', 'Briar Meadow', 'Bryson City', 'Burtrose', 'Bywater', 'CBD', 'C it l O ' In [ ]:

1

Q14. Print all the Uber trips that has the starting point of San Francisco. (2 points) Note: Use the original dataframe without dropping the 'na' values.

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In [18]:

1 dataset[dataset['START*']=='San Francisco']

Out[18]:

In [ ]:

START_DATE*

END_DATE*

CATEGORY*

START*

STOP*

MILES*

PURPOSE*

362

05-09-2016 14:39

05-09-2016 15:06

Business

San Francisco

Palo Alto

20.5

Between Offices

440

6/14/2016 16:09

6/14/2016 16:39

Business

San Francisco

Emeryville

11.6

Meeting

836

10/19/2016 14:02

10/19/2016 14:31

Business

San Francisco

Berkeley

10.8

NaN

917

11-07-2016 19:17

11-07-2016 19:57

Business

San Francisco

Berkeley

13.2

Between Offices

919

11-08-2016 12:16

11-08-2016 12:49

Business

San Francisco

Berkeley

11.3

Meeting

927

11-09-2016 18:40

11-09-2016 19:17

Business

San Francisco

Oakland

12.7

Customer Visit

933

11-10-2016 15:17

11-10-2016 15:22

Business

San Francisco

Oakland

9.9

Temporary Site

966

11/15/2016 20:44

11/15/2016 21:00

Business

San Francisco

Berkeley

11.8

Temporary Site

1 Not sure about the difference between a Print and Display, so I am doing both here for this question

In [19]:

In [ ]:

1 print(dataset[dataset['START*']=='San Francisco']) 362 440 836 917 919 927 933 966

START_DATE* 05-09-2016 14:39 6/14/2016 16:09 10/19/2016 14:02 11-07-2016 19:17 11-08-2016 12:16 11-09-2016 18:40 11-10-2016 15:17 11/15/2016 20:44

362 440 836 917 919 927 933 966

MILES* 20.5 11.6 10.8 13.2 11.3 12.7 9.9 11.8

END_DATE* CATEGORY* 05-09-2016 15:06 Business 6/14/2016 16:39 Business 10/19/2016 14:31 Business 11-07-2016 19:57 Business 11-08-2016 12:49 Business 11-09-2016 19:17 Business 11-10-2016 15:22 Business 11/15/2016 21:00 Business

START* San Francisco San Francisco San Francisco San Francisco San Francisco San Francisco San Francisco San Francisco

STOP* Palo Alto Emeryville Berkeley Berkeley Berkeley Oakland Oakland Berkeley

\

PURPOSE* Between Offices Meeting NaN Between Offices Meeting Customer Visit Temporary Site Temporary Site

1

Q15. What is the most popular starting point for the Uber

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Q15. What is the most popular starting point for the Uber drivers? (2 points) Note: This question is based on the dataframe with no 'na' values in the 'START' variable.

In [20]:

1 dataset['START*'].dropna().value_counts().head(1)

Out[20]: Cary 201 Name: START*, dtype: int64 In [ ]:

1

In [54]:

1 print('Most popular starting point for the Uber driver is :-',dataset['START Most popular starting point for the Uber driver is :- Cary Name: START*, dtype: int64

In [57]:

1 2 3 4 5 6

201

popular_starting_point = dataset["START*"].dropna() df = pd.DataFrame(popular_starting_point.value_counts()) df.sort_values(["START*"], ascending = False) df = df.reset_index() df = df.rename(columns = {'index':'starting_destination', 'START*':'Count'}) df.loc[df['Count'] == max(df['Count'])]

Out[57]: 0

starting_destination

Count

Cary

201

Q16. What is the most popular dropping point for the Uber drivers? (2 points) Note: This question is based on the dataframe with no 'na' values in the 'STOP' variable.

In [36]:

1 dataset['STOP*'].dropna().value_counts().head(1)

Out[36]: Cary 203 Name: STOP*, dtype: int64 In [56]:

1 print('Most popular dropping point for the Uber driver is :-',dataset['STOP* Most popular dropping point for the Uber driver is :- Cary Name: STOP*, dtype: int64

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In [58]:

1 2 3 4 5 6

popular_stopping_point = dataset["STOP*"].dropna() df = pd.DataFrame(popular_stopping_point.value_counts()) df.sort_values(["STOP*"], ascending = False) df = df.reset_index() df = df.rename(columns = {'index':'stopping_destination', 'STOP*':'Count'}) df.loc[df['Count'] == max(df['Count'])]

Out[58]: stopping_destination

Count

Cary

203

0

Q17. List the most frequent route taken by Uber drivers. (3 points) Note: This question is based on the dataframe with no 'na' values.

In [76]:

1 2 3 4 5

df=dataset.dropna() df_groupby_Start_Stop = df.groupby(['START*','STOP*']) df_Start_Stop_Cnt = pd.DataFrame({'count' : df_groupby_Start_Stop.size()}).r print('List the most frequent route taken by Uber drivers is :-',end="") df_Start_Stop_Cnt

List the most frequent route taken by Uber drivers is :Out[76]: START* 26

In [78]:

1 2 3 4 5 6

STOP*

count

Cary Morrisville

52

df = dataset.dropna() df = pd.DataFrame(df.groupby(['START*', 'STOP*']).size()) df = df.rename(columns = {0:'Count'}) df = df.sort_values(['Count'], ascending = False) print('List the most frequent route taken by Uber drivers is :-',end="") df.loc[df['Count'] == max(df['Count'])]

List the most frequent route taken by Uber drivers is :Out[78]: Count START*

STOP*

Cary

Morrisville

52

Q18. Print all types of purposes for the trip in an array. (3 points) Note: This question is based on the dataframe with no 'na' values in the 'PURPOSE' variable. localhost:8888/notebooks/~zGL/Project/Uber_Driver/PDS_UberDrive_Questions_Final.ipynb

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In [154]:

1 2 3 4 5 6 7 8 9

print(np.array(dataset['PURPOSE*'].dropna().unique())) print("\n") print(dataset.dropna().groupby(['PURPOSE*']).agg({'MILES*':sum})) dataset.dropna().groupby(['PURPOSE*']).agg({'MILES*':sum}) #Not sure what is the difference between a print and display

['Meal/Entertain' 'Errand/Supplies' 'Meeting' 'Customer Visit' 'Temporary Site' 'Between Offices' 'Charity ($)' 'Commute' 'Moving' 'Airport/Travel'] MILES* PURPOSE* Airport/Travel Between Offices Charity ($) Commute Customer Visit Errand/Supplies Meal/Entertain Meeting Moving Temporary Site

16.5 197.0 15.1 180.2 2089.5 508.0 911.7 2851.3 18.2 523.7

Out[154]: MILES* PURPOSE*

In [ ]:

Airport/Travel

16.5

Between Offices

197.0

Charity ($)

15.1

Commute

180.2

Customer Visit

2089.5

Errand/Supplies

508.0

Meal/Entertain

911.7

Meeting

2851.3

Moving

18.2

Temporary Site

523.7

1

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print(np.array(dataset['PURPOSE*'].dropna().unique())) print("\n") print(dataset['MILES*'].groupby(dataset['PURPOSE*']).sum()) dataset['MILES*'].groupby(dataset['PURPOSE*']).sum() #Not sure what is the difference between a print and display

['Meal/Entertain' 'Errand/Supplies' 'Meeting' 'Customer Visit' 'Temporary Site' 'Between Offices' 'Charity ($)' 'Commute' 'Moving' 'Airport/Travel'] PURPOSE* Airport/Travel 16.5 Between Offices 197.0 Charity ($) 15.1 Commute 180.2 Customer Visit 2089.5 Errand/Supplies 508.0 Meal/Entertain 911.7 Meeting 2851.3 Moving 18.2 Temporary Site 523.7 Name: MILES*, dtype: float64 Out[155]: PURPOSE* Airport/Travel 16.5 Between Offices 197.0 Charity ($) 15.1 Commute 180.2 Customer Visit 2089.5 Errand/Supplies 508.0 Meal/Entertain 911.7 Meeting 2851.3 Moving 18.2 Temporary Site 523.7 Name: MILES*, dtype: float64

Q19. Plot a bar graph of Purposes vs Distance. (3 points) Note: This question is based on the original dataframe.

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In [128]:

1 df=(dataset.dropna().groupby(['PURPOSE*']).agg({'MILES*':sum})) 2 df.plot(kind = 'bar') 3 plt.show()

In [130]:

1 df = pd.DataFrame(dataset['MILES*'].groupby(dataset['PURPOSE*']).sum()) 2 df.plot(kind = 'bar') 3 plt.show()

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1 d2f=(dataset.dropna().groupby(['PURPOSE*']).agg({'MILES*':sum})) 2 d2f = d2f.reset_index() 3 sns.barplot(y = d2f['MILES*'], x = d2f['PURPOSE*'])

Out[140]: <matplotlib.axes._subplots.AxesSubplot at 0x18b54cceac8>

Q20. Print a dataframe of Purposes and the distance travelled for that particular Purpose. (3 points) Note: This question is based on the original dataframe.

In [143]:

1 print(dataset.groupby(['PURPOSE*']).agg({'MILES*':sum})) MILES* PURPOSE* Airport/Travel Between Offices Charity ($) Commute Customer Visit Errand/Supplies Meal/Entertain Meeting Moving Temporary Site

16.5 197.0 15.1 180.2 2089.5 508.0 911.7 2851.3 18.2 523.7

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In [144]:

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1 print(dataset['MILES*'].groupby(dataset['PURPOSE*']).sum()) PURPOSE* Airport/Travel 16.5 Between Offices 197.0 Charity ($) 15.1 Commute 180.2 Customer Visit 2089.5 Errand/Supplies 508.0 Meal/Entertain 911.7 Meeting 2851.3 Moving 18.2 Temporary Site 523.7 Name: MILES*, dtype: float64

Q21. Plot number of trips vs Category of trips. (3 points) Note: This question is based on the original dataframe.

In [167]:

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df_groupby_Category = dataset.groupby(['CATEGORY*']) df_Start_Stop_Cnt = pd.DataFrame({'count' : df_groupby_Category.size()}).res df_Start_Stop_Cnt sns.barplot(df_Start_Stop_Cnt['CATEGORY*'], df_Start_Stop_Cnt['count'])

Out[167]: <matplotlib.axes._subplots.AxesSubplot at 0x18b567c10c8>

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df_groupby_Category = pd.DataFrame(dataset['CATEGORY*'].value_counts()) df_groupby_Category.reset_index() df_groupby_Category.plot(kind = 'bar') plt.show() df_groupby_Category

Out[169]: CATEGORY* Business

1078

Personal

77

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In [170]:

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1 df = pd.DataFrame(dataset.reset_index()) 2 df = df.rename(columns = {'index':'Category', 'CATEGORY*': 'Count'}) 3 sns.barplot(x = df['Category'], y = df['Count'])

Out[170]: <matplotlib.axes._subplots.AxesSubplot at 0x18b56915188>

Q22. What is proportion of trips that is Business and what is the proportion of trips that is Personal? (3 points) Note: The proportion calculation is with respect to the 'miles' variable.

Note: This question is based on the original dataframe.

In [177]:

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df = dataset.groupby(['CATEGORY*']).sum() Business = df.iloc[0,0]/(df.iloc[0,0] + df.iloc[1,0]) Personal = df.iloc[1,0]/(df.iloc[0,0] + df.iloc[1,0]) print("Business:", Business) print("Personal:", Personal)

Business: 0.9411947856153777 Personal: 0.05880521438462235

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