Pandas Exercises

TASK: Import pandas

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TASK: Read in the bank.csv file that is located under the 01-Crash-Course-Pandas folder. Pay close attention to where the .csv file is located!

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TASK: Display the first 5 rows of the data set

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age job marital education default balance housing loan contact day month duration campaign pdays previous poutcome y
0 30 unemployed married primary no 1787 no no cellular 19 oct 79 1 -1 0 unknown no
1 33 services married secondary no 4789 yes yes cellular 11 may 220 1 339 4 failure no
2 35 management single tertiary no 1350 yes no cellular 16 apr 185 1 330 1 failure no
3 30 management married tertiary no 1476 yes yes unknown 3 jun 199 4 -1 0 unknown no
4 59 blue-collar married secondary no 0 yes no unknown 5 may 226 1 -1 0 unknown no

TASK: What is the average (mean) age of the people in the dataset?

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41.17009511170095

TASK: What is the marital status of the youngest person in the dataset?

HINT

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'single'

TASK: How many unique job categories are there?

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12

TASK: How many people are there per job category? (Take a peek at the expected output)

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management       969
blue-collar      946
technician       768
admin.           478
services         417
retired          230
self-employed    183
entrepreneur     168
unemployed       128
housemaid        112
student           84
unknown           38
Name: job, dtype: int64

TASK: What percent of people in the dataset were married? 

#CODE HERE
61.86684361866843

TASK: There is a column labeled “default”. Use pandas’ .map() method to create a new column called “default code” which contains a 0 if there was no default, or a 1 if there was a default. Then show the head of the dataframe with this new column.

Helpful Hint Link One

Helpful Hint Link Two

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age job marital education default balance housing loan contact day month duration campaign pdays previous poutcome y default code
0 30 unemployed married primary no 1787 no no cellular 19 oct 79 1 -1 0 unknown no 0
1 33 services married secondary no 4789 yes yes cellular 11 may 220 1 339 4 failure no 0
2 35 management single tertiary no 1350 yes no cellular 16 apr 185 1 330 1 failure no 0
3 30 management married tertiary no 1476 yes yes unknown 3 jun 199 4 -1 0 unknown no 0
4 59 blue-collar married secondary no 0 yes no unknown 5 may 226 1 -1 0 unknown no 0

TASK: Using pandas .apply() method, create a new column called “marital code”. This column will only contained a shortened code of the possible marital status first letter. (For example “m” for “married” , “s” for “single” etc… See if you can do this with a lambda expression. Lots of ways to do this one!

Hint Link

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age job marital education default balance housing loan contact day month duration campaign pdays previous poutcome y default code marital code
0 30 unemployed married primary no 1787 no no cellular 19 oct 79 1 -1 0 unknown no 0 m
1 33 services married secondary no 4789 yes yes cellular 11 may 220 1 339 4 failure no 0 m
2 35 management single tertiary no 1350 yes no cellular 16 apr 185 1 330 1 failure no 0 s
3 30 management married tertiary no 1476 yes yes unknown 3 jun 199 4 -1 0 unknown no 0 m
4 59 blue-collar married secondary no 0 yes no unknown 5 may 226 1 -1 0 unknown no 0 m

TASK: What was the longest lasting duration?

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3025

TASK: What is the most common education level for people who are unemployed?

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secondary    68
tertiary     32
primary      26
unknown       2
Name: education, dtype: int64

TASK: What is the average (mean) age for being unemployed?

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40.90625