Pandas

Question 1

What are the commands to load and save cvs files?

Answer 1

pd.read_csv(path) and pd.to_csv(path, index = False)

Question 2

Name the three methods to combine DataFrames.

Answer 2

Pandas.concat([DataFrame1, DataFrame2], axis=0/1)
Pandas.merge([DataFrame1, DataFrame2], how=’outer’/’inner’/’right’/’left’, on=column_name)
DataFrame1.join(DataFrame2, how=’outer’/’inner’/’right’/’left’)

Question 3

What is the key difference (other than syntax) between merge and join?

Answer 3

merge requires a column to merge.
join combines data based on index.
(note: you probably can force a column to be an index value when creating a DataFrame)

Question 4

What does the inplace=True/False parameter do?

Answer 4

It forces the changes made to the DataFrame rather than returning a value and leaving the original untouched (if set to true; default is false)

Question 5

when axis parameter is called, what is the x value and what is the y value?

Answer 5

x = 0
y = 1

Question 6

Describe the difference between the and iloc methods.

Answer 6

loc requires index labels, meaning the names of rows and columns (which can be numbers, but don’t have to be). iloc requires the integer values of those indexes.

Question 7

What would DataFrame.iloc[[1,2], [‘date’, ‘stock’]] return?

Answer 7

Index Error. The iloc method requires the integer values of the index labels, and will error if the labels are given.

Question 8

Explain the groupby() method

Answer 8

DataFrame.groupby() function is used to group occurances of common values in a particular column and can further split the data of another column based on some criterial (like mean, median, etc.).

Question 9

What method can you use to execute a custom function across the entirety of a DataFrame?

Answer 9

DataFrame[column_name].apply(function)

Question 10

What does strftime stand for

Answer 10

String Format Time

Question 11

Describe how to format a loc call.

Answer 11

Take the DataFrame with a ‘.’ behind it and then put your parameters in [] after the loc call. Within the [], there should be other [] with values. The first set of [] will have the row values listed, separated by a , between each value. the second [] will have the same, but with columns.
i.e. DataFrame.loc[[row1, row2, row3],[‘col1’,’col2’,’col3’]]

Question 12

Write an example of a conditional DataFrame call

Answer 12

DataFrame.loc[DataFrame[‘column’]>x]

Question 13

Execute the .value_counts() function on the ‘Name’ column of a DataFrame. Return the respective counts of each distinct value in relation to the whole set.

Answer 13

DataFrame[‘Name’].value_counts(normalize=True)

Question 14

Extract month from the date column in the data DataFrame.

Answer 14

data[‘date’].dt.month

Question 15

Explain what the .map() function does.

Answer 15

.map() function will map values produced by a function, dictionary, or series to their appropriate counterparts in the series the .map() is transforming.
import pandas as pd

Sample Series
s = pd.Series([‘apple’, ‘banana’, ‘cherry’, ‘date’])

Mapping dictionary
fruit_codes = {‘apple’: 1, ‘banana’: 2, ‘cherry’: 3}

Apply the mapping
coded_s = s.map(fruit_codes)

print(coded_s)
# Expected output
# 0 1.0
# 1 2.0
# 2 3.0
# 3 NaN
# dtype: float64