Classwork 12

Pandas Fundamental III: Data Types; Filtering Data By a Condition

Direction

The netflix.csv file (with its pathname https://bcdanl.github.io/data/netflix.csv) contains a list of 6,000 titles that were available to watch in November 2019 on the video streaming service Netflix. It includes four variables: the video’s title, director, the date Netflix added it (date_added), and its type (category).

import pandas as pd 
import numpy as np

# Below is for an interactive display of DataFrame in Colab
from google.colab import data_table
data_table.enable_dataframe_formatter()


netflix = pd.read_csv(
    "https://bcdanl.github.io/data/netflix.csv"
)

Question 1

Read netflix.csv as a DataFrame named netflix.

Answer:

Question 2

Inspect the data types of all variables in netflix.

Answer:

Question 3

Convert the type variable to the category data type.

Answer:

Question 4

Convert the date_added variable to a proper datetime format.

Answer:

Question 5

Optimize the DataFrame for limited memory use and maximum utility by converting type to category and date_added to datetime.

Answer:

Question 6

Find all observations with a director of “Martin Scorsese”.

Answer:

Question 7

Find all observations whose type is not "Movie".

Answer:

Question 8

Find all observations with a title of “Limitless” and a type of “Movie”.

Answer:

Question 9

Find all observations with either a date_added of "2018-06-15" or a director of “Bong Joon Ho”.

Answer:

Question 10

Find all observations with a director of “Ethan Coen”, “Joel Coen”, or “Quentin Tarantino”.

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Question 11

Find all observations with a date_added value between January 1, 2019 and February 1, 2019.

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Question 12

Find all observations whose title begins with a letter between "S" and "T" using between().

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Question 13

Use the query() method to find all movies added on or after January 1, 2019.

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Question 14

Create a new variable named recent_addition using np.where(). Let it be "Yes" if date_added is on or after January 1, 2019, and "No" otherwise.

Answer:

Discussion

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