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Pandas: Create a smaller dataframe with a subset of all features

Pandas: IMDb Movies Exercise-8 with Solution

Write a Pandas program to create a smaller dataframe with a subset of all features.

Sample Solution:

Python Code :

import pandas as pd
df = pd.read_csv('movies_metadata.csv')
# Create a smaller dataframe
small_df = df[['title', 'release_date', 'budget', 'revenue', 'runtime']]
print("Smaller DataFrame:")
print(small_df.head())

Sample Output:

Smaller DataFrame:
                         title release_date    budget    revenue  runtime
0                    Toy Story   1995-10-30  30000000  373554033     81.0
1                      Jumanji   1995-12-15  65000000  262797249    104.0
2             Grumpier Old Men   1995-12-22         0          0    101.0
3            Waiting to Exhale   1995-12-22  16000000   81452156    127.0
4  Father of the Bride Part II   1995-02-10         0   76578911    106.0

Python-Pandas Code Editor:


Sample Table:


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Python: Tips of the Day

Find current directory and file's directory:

To get the full path to the directory a Python file is contained in, write this in that file:

import os 
dir_path = os.path.dirname(os.path.realpath(__file__))

(Note that the incantation above won't work if you've already used os.chdir() to change your current working directory, since the value of the __file__ constant is relative to the current working directory and is not changed by an os.chdir() call.)

To get the current working directory use

import os
cwd = os.getcwd()

Documentation references for the modules, constants and functions used above:

  • The os and os.path modules.
  • The __file__ constant
  • os.path.realpath(path) (returns "the canonical path of the specified filename, eliminating any symbolic links encountered in the path")
  • os.path.dirname(path) (returns "the directory name of pathname path")
  • os.getcwd() (returns "a string representing the current working directory")
  • os.chdir(path) ("change the current working directory to path")

Ref: https://bit.ly/3fy0R6m