Pandas Pt 1 (Wk 4 UCSD)

Question 1

What is pandas

Answer 1

a library built on numpy, with flexible data structures

Question 2

how to use the pandas library

Answer 2

import pandas as pd

Question 3

What is a series in pandas

Answer 3

A one dimensional dict like structure (index, values), that allows for diff data types, and works w/ most numpy functions

Question 4

how to declare a series

Answer 4

ser = pd.series( data=[ values], index = [indices] ) (don’t have to say data = and index =)

Question 5

print the in dices of a series

Answer 5

print (ser.index)

Question 6

retrieve data from a series at a given index ‘Bob’

Answer 6

ser[ ‘Bob’ ] or ser.loc[ ‘Bob’ ]

Question 7

retrieve multiple data points in a series with index values

Answer 7

ser[ [ ‘bob’, ‘nancy’ ] ]

Question 8

retrieve data from series by indexing on position

Answer 8

ser[ [ 1, 2, 3 ] ]

Question 9

test if a given index is present in a series

Answer 9

‘bob’ in ser&raquo_space; returns boolean

Question 10

can you perform operations on a series, like you can with arrays?

Answer 10

yes. ser * 2 multiplies all values in series by 2

Question 11

What is a dataframe

Answer 11

it’s like a 2d series, where indices become row names, and name of each series becomes col names

Question 12

How do you create a dictionary with multiple sets of series, which you could then assign to a dataframe?

Answer 12

d = {‘one’ : pd.Series([values], index=[indices]),

‘two’ : pd.Series([values], index=[indices])}

Question 13

create a dataframe using a dictionary of series

Answer 13

pd_dataframe = pd. dataframe(dict_of_series)

Question 14

retrieve the row names from a dataframe

Answer 14

df.index

Question 15

retrieve the column names from a dataframe

Answer 15

df.columns

Question 16

print a dataframe in a nice tabular format

Answer 16

df_var (just list the var, which prints the value of the variable)

Question 17

create a dataframe with a subset of the rows (indices)

Answer 17

pd.dataframe(df_var, index = [indices] )

Question 18

create a dataframe with a subset of rows and columns

Answer 18

pd.dataframe(df_var, index = [indices], columns= [column_values] )

Question 19

what happens if you create a data frame from a list of dictionaries?

Answer 19

the indices from each dictionary become the column names, and the rows represent each dict (opp. of when you build w/ dict. of series)

Question 20

retrieve the values of a column from a dataframe, w/ a column name of ‘two’

Answer 20

df [ ‘two’ ]

Question 21

create a third col in a df that equals the product of df columns ‘one’ and ‘two’

Answer 21

df[ ‘three’ ] = df[‘one’] * df[‘two’]

Question 22

create a bool col ‘flag’ based on the values of col ‘two’ that are greater than 100

Answer 22

df[‘flag’] = df[‘two’] > 100

Question 23

retrieve the value and delete a column from a df

Answer 23

three = df.pop(‘three’)

Question 24

how to delete col ‘two’ from a dataframe

Answer 24

del df[‘two’]

Question 25

append a new column onto the end of a df

Answer 25

df.insert(2, ‘copy_of_one’, df[‘one’]) (where two would be the position of the next column)

Question 26

function to read csvs, json, html into pandas

Answer 26

read_csv, read_json, read_html, read_sql_query, read_sql_table

Question 27

function to read json into pandas

Answer 27

read_json

Question 28

retrieve the values in a dataframe from row 1 and 2

Answer 28

df_var[ 1:2 ] df_var.iloc[ [1,2] ] ## without iloc, have to slice (can’t just say [1] for instance

Question 29

ingest csv data into a data frame

Answer 29

df_var_csv = pd.read_csv(‘filename’, sep = ‘ , ‘ ) ## comma separated

Question 30

slice out column with name ‘ratings’ from a dataframe

Answer 30

df[ ‘ratings’ ]

Question 31

specify a column as the index, and then retrieve values on that index

Answer 31

movies = movies.set_index(“movieId”)

movies.loc[ 1 ] ## using the movieId as index now