Analyzing DataFrames

Posted under » Python Data Analysis on 12 June 2023

From Read data into Dataframe

The head() method returns the headers and a specified number of rows, starting from the top. If the number of rows is not specified, the head() method will return the top 5 rows.

import pandas as pd

df = pd.read_csv('data.csv')

print(df.head(10)) 

The opposite of head is tail. The tail() method returns the headers and a specified number of rows, starting from the bottom.

print(df.tail()) 

The DataFrames object has a method called info(), that gives you more information about the data set.

print(df.info()) 

  <class 'pandas.core.frame.DataFrame'>
  RangeIndex: 169 entries, 0 to 168
  Data columns (total 4 columns):
   #   Column    Non-Null Count  Dtype  
  ---  ------    --------------  -----  
   0   Duration  169 non-null    int64  
   1   Pulse     169 non-null    int64  
   2   Maxpulse  169 non-null    int64  
   3   Calories  164 non-null    float64
  dtypes: float64(1), int64(3)
  memory usage: 5.4 KB
  None

The info() method also tells us how many Non-Null values there are present in each column, and in our data set it seems like there are 164 of 169 Non-Null values in the "Calories" column.

Empty values, or Null values, can be bad when analyzing data, and you should consider removing rows with empty values.