Pandas

Question 1

Primary data structures in pandas

Answer 1

Series: one dimensional labeled array that can hold any data type.

DataFrame: two dimensional labeled data structure.

Question 2

Creating dataframes in pandas could be from?

Answer 2

Dictionary

Numpy array

Comma separated values (csv)

Question 3

How do you import a CSV file using pd

Answer 3

df = pd read_csv(‘/file_path/file_name.csv)

Question 4

What is an attribute

Answer 4

A value associated with an object or class that is referenced by name using dotted expression (like characteristics of the object)

Question 5

What are methods?

Answer 5

A method is a function that is defined inside a class body and typically performs an action

Question 6

Common DataFrame attribute;

Answer 6

Columns: column labels

Dtypes: data types in dataframe

iloc: access grp of rows & columns using integer based indexing

loc: accesses a group of rows and columns by labels or a boolean array

shape: returns a tuple representing the dimensionality of the DataFrame

values: returns a numpy representation of the dataframe

Question 7

methods in dataframes;

Answer 7

apply: applies a function over an axis of the dataframe

copy: makes a copy of the dataframe’s indices and data

describe: returns descriptive statistics of the dataframe

drop: drops specified labels from rows or columns

groupby: splits the dataframe, applies a function, and combines the results

head(n=5): returns the first n rows of a data frame( default = 5)

Question 8

DataFrame methods 2;

Answer 8

info: returns a concise summary of the dataframe

isna: returns a same sized boolean dataframe indicating whether each value is null(can also use isnull() as an alias)

sort values: sorts by the values across a given axis

value counts: returns a series containing counts of unique rows in dataframe

where: replaces values in the dataframe where a given condition is false

Question 9

Selecting rows from a dataframe

Answer 9

print(df.loc[‘row_1]): this will return a
series object.

print(df.loc[[‘row_1]]): returns a dataframe object

Question 10

How do you change date object to datetime?

Answer 10

df[date] = pd.to_datetime(df[date])
All columns in quotations marks

Question 11

Create year column from datetime column?

Answer 11

df[year] = df[date].Dr.year
All columns in quotations

Question 12

How do you take a sample from a data frame?

Answer 12

sample = df.sample(n=50, random_stae = 42)

Question 13

Using the unicorn df, create years till unicorn column?

Answer 13

sample[years_till_unicorn] = sample[Year Joined] - sample[Year Founded]

Question 14

Group data(unicorn) by industry, for each industry get the max value in the year till unicorn column

Answer 14

grouped = (sample[[Industry, years_till_unicorn]]
.groupby(Industry)
.max()
.sort_values(by = years_till_unicorn)
)

grouped