Pd Basics

Question 1

How to read a CSV using pandas?

Answer 1

Pd.read_csv(‘link’)

Question 2

How to get the NUM of rows and columns?

Answer 2

Df.shape

Question 3

How to get more specified information about the data frame for example index type

Answer 3

Df.info()

Question 4

How to display the list of columns?

Answer 4

Df.columns

Question 5

How to set a limit of columns to 85?

Answer 5

Pd.set_option(‘display.max_columns’,85)

Question 6

How to print first 3 values?

Answer 6

Df.head(3)

Question 7

How to print last 3 values?

Answer 7

Df.tail(3)

Question 8

What is a data frame?

Answer 8

Data frame is a two dimensional array with additional functions

Question 9

What is a series?

Answer 9

Series is a one dimensional array with additional functions

Question 10

How would you print the first 3 elements using iloc where you want to return only column 2 as output

Answer 10

Df.iloc[[0,1,2],2] or df.iloc[0:3,2]

Question 11

What’s the difference between loc and iloc

Answer 11

Iloc uses indices as columns loc uses their actual names

Question 12

What does Inplace=True do?

Answer 12

Applies the change to the data frame

Question 13

How would you change the index of a column with a different one?

Answer 13

Df.set_index(‘column’)

Question 14

How would you reset the indices?

Answer 14

Df.reset_index()

Question 15

How would you sort the indices?

Answer 15

Df.sort_index()

Question 16

What is a filter mask? Give one example

Answer 16

A filter mask is a Boolean expression, if a row is True compared to this expression, it will be displayed. Filt = (df[‘cost’] == 0)

Question 17

What do these signs mean: & |~

Answer 17

And or not

Question 18

How to check if a series contains a certain value?

Answer 18

Df[column].isin()

Question 19

How to check if a string contains a certain combination of signs? How to change a value from notanumber to false?

Answer 19

Df[column].str.contains(“”, na=false)

Question 20

How would you change values for a column named email to lowercase

Answer 20

Df[email] = df[email].str.lower()

Question 21

How does apply work?

Answer 21

Apply works on series by changing every single value one by one
Df.apply(pd.Series.min)

Question 22

How does applymap works?

Answer 22

Applymap works like apply but not just for elements of series, for elements of dataframes.
Df.applymap(str.lower)

Question 23

How does map work?

Answer 23

Map changes chosen values and lives everything else as NAN
Df[column].map({key:value})

Question 24

How does replace work?

Answer 24

Replace changes chosen values and leaves other values as they were
Df[column].replace({key:value})

Question 25

How would you create a new column from two other columns containing strings?

Answer 25

Df[new] = df[old1] + ‘ ‘ + df[old2]

Question 26

How would you delete a column?

Answer 26

Df.drop(columns=[columnname],Inplace=true)

Question 27

How would you expand one column into two

Answer 27

Df[column].star.split(‘ ‘, expand=true)

Question 28

How would you connect an array back

Answer 28

Df = pd.concat([df,row])

Question 29

What does ignore index do and how would you use it?

Answer 29

Ignore index makes the two concatenated dataframes’ indices merge into one list. Df = pd.concat([df,row],ignore_index=true)

Question 30

How would you sort values of a certain column?

Answer 30

Df[column].sort_values()

Question 31

How would you sort values of a certain dataframe?

Answer 31

Df.sort_values(by=[c1,c2],inplace=True)

Question 32

How would you sort a dataframe by index

Answer 32

Df.sort_index()

Question 33

How would you get 10 of the largest and smalles value of a column?

Answer 33

Df[c1].nlargest(10) df[c1].nsmallest()

Question 34

How would you get a median and mean of a column

Answer 34

Df[c1].median() Df[c2].mean()

Question 35

How would you get a detailed list of data like median and mean for a certain dataframe

Answer 35

Df.describe()

Question 36

How would you get a number of times a certain response appears in a column? How would you make it so all the numbers sum up to 1

Answer 36

Df[c1].value_counts(), Df[c1].value_counts(normalize=True)

Question 37

How do you create a group object from a dataframe? What exactly does it group?

Answer 37

Group = df.groupby(df[c1]). It groups rows with identical value for the chosen column

Question 38

How do you use multiple functions on one column?

Answer 38

Group[c1].agg([‘median’,’mean’])

Question 39

How do you get a unique set of values for chosen column?

Answer 39

Df[c1].unique()

Question 40

How do you check if values in a dataframe arent numbers?

Answer 40

Df.isna()

Question 41

How do you change values that are not numbers to something different?

Answer 41

Df.fillna(0, Inplace=true)

Question 42

How do you change the type of value in a column?

Answer 42

Df[column] = Df[column].astype(type)

Question 43

How would you format a date string to an actual date

Answer 43

Df[date] =pd.to_datetime(df[date], format=checkongoogle)

Question 44

How would you check the name of a day on a specific date? How would you do it on a series of days?

Answer 44

Df.loc[0,date].dayname(),

Df[date].dt.dayname()

Question 45

How would you find the oldest and newest date?

Answer 45

Df[date].min()
Df[date].max()

Question 46

How would you segregate data by the highest value in a given month?

Answer 46

Highs = Df[high].resample(M).max()

Question 47

How would you get rid of rows of data that have missing values in a a given column? How would you get rid of columns? How does ‘how’ parameter work?

Answer 47

Df.dropna(axis=index,how=all,subset=[last]),
Df.dropna(axis=column,how=all,subset=[last])
How parameter determines whether to delete a row if there is a value missing in ANY of the listed columns or if to delete it if there is a value missing in ALL of the listed columns