Python Pandas Question Exercise Practice Solutions | Data Science and Machine Learning with Python

Question

Assignment/Task 5

Pandas - Data Analysis of IMDB movies data

As we have a basic understanding of the different data structures in Pandas, let’s explore the fun and interesting ‘IMDB-movies-dataset’ and get our hands dirty by performing practical data analysis on real data.

It is an open-source dataset and you can download it from this link.

We will read the data from the .csv file and perform the following basic operations on movies data

1. Load the IMDb Dataset and read
2. View the dataset
3. Understand some basic information about the dataset and Inspect the dataframe Inspect the dataframe's columns, shapes, variable types etc.
4. Data Selection – Indexing and Slicing data
5. Data Selection – Based on Conditional filtering
6. Groupby operations
7. Sorting operation
8. Dealing with missing values
9. Dropping columns and null values
10. Apply( ) functions

Answer 1

1. Load the IMDb Dataset and read

import numpy as np

import pandas as pd

df = pd.read_csv('IMDB-Movie-Data.csv')

2. View the dataset

df.head(10)

3. Understand some basic information about the dataset and Inspect the dataframe Inspect the dataframe's columns, shapes, variable types etc.

df.columns

type(df)

df.dtypes

df.shape

df.size

df.ndim

df.values

df1 = df.values

type(df1)

df.describe()

4. Data Selection – Indexing and Slicing data

df.iloc[0]

df[1:20]

df[['Rating','Votes']].agg(['min','max','mean'])

5. Data Selection – Based on Conditional filtering

df.filter(items=['Rank', 'Votes'])

df['Rating']>7

top_rank = df[df["Rating"] > 8.0]["Title"].count()

print(top_rank)

6. Groupby operations

df2 = df.groupby('Genre')

df2.mean()

df[df['Rating']>7].groupby('Genre')[['Rating']].count()

 top_movie = df[df["Rating"] > 8.0]

top_movie.groupby(["Title"])["Votes"].mean()

7. Sorting operation

x = df.sort_values(by='Rating')

x.head(10)

most_votes = df.groupby(["Votes"]).mean()

most_votes.sort_values(by = ["Votes"], ascending = False).head()

8. Dealing with missing values

df.isnull().sum()

9. Dropping columns and null values

df.dropna()

x = df.drop(['Metascore'], axis='columns', inplace=True)

10. Apply( ) functions

rank = df.apply(lambda n: n*5)

print(rank.head())

Answer 2

GO_STP_6266

https://www.linkedin.com/posts/sahil-parmar-4099391bb_google-colaboratory-activity-6804016581916676096-TxzC

Answer 3

GO_STP_6734

Hello Goeduhub 

here is the answer for task 5: https://www.linkedin.com/pulse/assignment-task-5-aswin-kumar