Bloomberg 2nd Technical

Question 1

how do you import pandas?

Answer 1

import pandas as pd

Question 2

how do you read in a csv?

Answer 2

df = pd.read_csv(“data.csv”)

Question 3

How do I get the first 4 rows of a df?

Answer 3

df.head(4)

Question 4

What is the default number of rows for head()?

Answer 4

5

Question 5

How do I get the last 5 rows for df?

Answer 5

df.tail()

Question 6

How do i get a random sample of the df?

Answer 6

df.sample(5)

Question 7

How do I get the number of rows and columns of a df?

Answer 7

df.shape

Question 8

how do i get the dtypes + non null counts?

Answer 8

df.info()

Question 9

how do i get column names of a df?

Answer 9

df.columns

Question 10

how do i get the data types per column?

Answer 10

df.dtypes

Question 11

how do i get the count missing per column?

Answer 11

df.isna().sum()

Question 12

how do we get % missing per column?

Answer 12

df.isna().mean() * 100

Question 13

how do we get total missing columns out of all the df?

Answer 13

df.isna().sum().sum()

Question 14

how do we drop rows with any missing values?

Answer 14

df.dropna()

Question 15

how do we do a simple fill in missing values?

Answer 15

df.fillna(0)

Question 16

how do we get descriptive statistics like count, mean, std, quantiles, etc. for every numerical column?

Answer 16

df.describe()

Question 17

how do we get descriptive statistics like count, mean, std, quantiles, etc. for every column?

Answer 17

df.describe(include=”all”)

Question 18

how do we get descriptive statistics like count, mean, std, quantiles, etc. for a specific column?

Answer 18

df[‘col_name’].describe()

Question 19

how do we get the mean of a column?

Answer 19

df[‘col’].mean()

Question 20

how do we get the median of a column?

Answer 20

df[‘col’].median()

Question 21

how do we get the standard deviation of a column?

Answer 21

df[‘col’].std()

Question 22

how do we get the minimum value of a column?

Answer 22

df[‘col’].min()

Question 23

how do we get the max value of a column?

Answer 23

df[‘col’].max()

Question 24

how do we get the index for the maximum value in each column?

Answer 24

df.idxmax()

Question 25

how do we get the index for the maximum value in each row?

Answer 25

df.idxmax(axis="columns")

Question 26

how would we query to get the student with the highest gpa?

Answer 26

df.loc[df['gpa'].idxmax()]

Question 27

how would we query to get the student with the lowest gpa?

Answer 27

df.loc[df['gpa'].idxmin()]

Question 28

if we are trying to query into a df with multiple conditions what should we do?

Answer 28

wrap each condition with parentheses

Question 29

how do we query when both all conditions should be met?

Answer 29

&

Question 30

how do we query when both either/or conditions should be met?

Answer 30

|

Question 31

how do we select multiple columns?

Answer 31

df[['col1', 'col2', 'col3']]

Question 32

how do we sort by one column?

Answer 32

df.sort_values(by='gpa', ascending=False)

Question 33

how do we sort by multiple columns?

Answer 33

df.sort_values(by=['year', 'gpa'], ascending=[True, False])

Question 34

What is the default setting of sort_values()?

Answer 34

ascending=False

Question 35

how would we group by a category and compute a statistic like mean?

Answer 35

df.groupby("col1")["gpa"].mean()

Question 36

how would we group by a category and get counts per category?

Answer 36

df.groupby("col1")["id"].count()

Question 37

how would we get multiple aggregations like min/max/mean per a group?

Answer 37

df.groupby("major")["gpa"].agg(['min', 'max', 'mean'])

Question 38

how would we filter to group on major and find majors with average gpa greater than 3.5?

Answer 38

df.groupby("major")['gpa'].mean().loc[lambda x: x>3.5]

Question 39

how do we compute outliers using the IQR method?

Answer 39

q1 = df['gpa'].quantile(0.25) q3 = df['gpa'].quantile(0.75) iqr = q3 - q1 lower = q1 - 1.5 * iqr upper = q3 + 1.5 * iqr

Question 40

how do we actually use IQR to query for all outliers?

Answer 40

outliers = df[(df['gpa'] < lower) | (df['gpa'] > upper)]

Question 41

what visualizations can we use for numeric variables?

Answer 41

histograms and box plots

Question 42

what are histograms good for?

Answer 42

to show distribution shape

Question 43

how would we create a histogram of gpa scores?

Answer 43

df['gpa'].plot(kind='hist')

Question 44

what are box plots good for?

Answer 44

for outliers and to show quartiles and median

Question 45

how do we create a boxplot for the gpa column?

Answer 45

df.boxplot(column='gpa')

Question 46

how do we visualize categorical variables?

Answer 46

bar plot and horizontal bar plots

Question 47

how would we visualize counts per major with a bar plot?

Answer 47

df['major'].value_counts().plot(kind='bar')

Question 48

how would we visualize counts per major with a horizontal bar plot?

Answer 48

df['major'].value_counts().plot(kind='barh')

Question 49

how do we do an inner merge?

Answer 49

pd.merge(df1, df2, on="student_id", how="inner")

Question 50

how do we do a left merge?

Answer 50

pd.merge(df1, df2, on="id", how="left")

Question 51

how would we define a join that's a union of both?

Answer 51

'outer'

Question 52

what is loc based on?

Answer 52

label based

Question 53

what is iloc based on?

Answer 53

index-based

Question 54

how would we get the first 5 rows and the first 3 columns of a df?

Answer 54

df.iloc[0:5, 0:3]

Question 55

how would we get the average beak length of a penguin?

Answer 55

df['beak_length_mm'].mean()

Question 56

how would we get a count of how many students per major?

Answer 56

df['major'].value_counts()

Question 57

how would we get the average salary per department?

Answer 57

df.groupby('department')['salary'].mean()

Question 58

how would we find departments with missing salary values?

Answer 58

df[df['salary'].isna()]

Question 59

how would we get the 6th row of a df?

Answer 59

df.iloc[5]

Question 60

how would i read in an excel sheet?

Answer 60

pd.read_excel("titanic.xlsx")

Question 61

how would i read in a json file?

Answer 61

pd.read_json("students.json")