Social Media Analytics

Python Pandas — Reading, Writing and Viewing Data

Pandas can perform a range of I/O Operations such as reading and writing data from various file formats (CSV, Excel, JSON, etc.). The commands for reading CSV and JSON data are read_csv() and read_json().

For viewing data, you can use head() and tail() methods. The head() method returns the headers and a specified number of rows, starting from the top, and the tail() method returns the headers and a specified number of rows, starting from the bottom.

These operations for reading and viewing the data are illustrated in the code in Exhibit 25.54.

Reading and Viewing Data from CSV and JSON datasets

import pandas as pd

# Load a CSV file into a DataFrame:
df1 = pd.read_csv('data/exercise_metrics.csv')

# Load the JSON file into a DataFrame:
df2 = pd.read_json('data/exercise_metrics.json')

'''
Viewing:
The head() method returns the headers and a specified number of rows, starting from the top.
The tail() method returns the headers and a specified number of rows, starting from the bottom.
Default is 5 rows.
'''
# Get a quick overview by printing the first 10 rows of data.cvs
print(df1.head(10)) 
print("\nRow 3:") # Go to the next line and print "Row 3:" 
print(df1.iloc[3]) # Print the 3rd row

# Print the last 5 rows of data.json
print("\n") # leave 1 blank line
print(df2.tail())

    
   Duration  Pulse  Maxpulse  Calories
0        60    110       130     409.1
1        60    117       145     479.0
2        60    103       135     340.0
3        45    109       175     282.4
4        45    117       148     406.0
5        60    102       127     300.0
6        60    110       136     374.0
7        45    104       134     253.3
8        30    109       133     195.1
9        60     98       124     269.0

Row 3:
Duration     45.0
Pulse       109.0
Maxpulse    175.0
Calories    282.4
Name: 3, dtype: float64


     Duration  Pulse  Maxpulse  Calories
164        60    105       140     290.8
165        60    110       145     300.4
166        60    115       145     310.2
167        75    120       150     320.4
168        75    125       150     330.4

Exhibit 25.55 Reading and viewing data from CSV and JSON datasets, exercise_metrics.csv and exercise_metrics.json on the duration of exercise and the pulse rate, maximum pulse rate, and calories consumed. Jupyter notebook.

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