Pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
Pandas introduces two new data types to Python: Series and DataFrame.
Visit here to know about DataFrame and how to Create DataFrame.
Sometimes data are available in millions or billions or even more than that. And you don’t want to see the whole data, you glimpse of data of what you are working with. For this purpose you need head() & tail() method.
head() Function
Suppose we want to extract the data of only the top 5 rows from our dataset. When this type of problem arises, we can use the head() method, which is defined in the Pandas library to extract the top n rows of a dataset.
The head() method is used for returning top n (by default value 5) rows of a DataFrame or Series.
#by default the read_csv function will read a comma separated file
import pandas as pd
df = pd.read_csv('CompleteCricketData.csv')
#we use the head function with argument 3 so Python only shows us the first 3 rows
print(df.head(3))
#Output
Unnamed: 0 Player Country ... Ground Date
0 0 RG Sharma INDIA ... Kolkata 11/13/2014
1 1 MJ Guptill NZ ... Wellington 3/21/2015
2 2 V Sehwag INDIA ... Indore 12/8/2011
The head() method in above example contains only one parameter, which is 3. It is an optional parameter. By setting it, we fix the number of rows we want from the DataFrame.
This is useful to see if our data loaded properly, get a sense of the columns, its name and its contents.
tail() Function
The tail() function returns last n rows from the object. It is useful for quickly verifying data.
If argument is not provided then it returns the last 5 rows of the data and with specified n arguments, gets the last n rows of data.
#example of tail method without argument
import pandas as pd
df = pd.read_csv('CompleteCricketData.csv')
#we use the tail function without argument so it only shows us the last 5 rows
print(df.tail())
#Output
Unnamed: 0 Player ... Date Unnamed: 15
92847 92847 KA Maharaj ... 3/7/2020 NaN
92848 92848 AL Phehlukwayo ... 3/7/2020 NaN
92849 92849 A Nortje ... 3/7/2020 NaN
92850 92850 A Zampa ... 3/13/2020 NaN
92851 92851 JR Hazlewood ... 3/13/2020 NaN
[5 rows x 17 columns]
#example of tail method with argument
import pandas as pd
df = pd.read_csv('CompleteCricketData.csv')
#we use the tail function with argument 2 so it only shows us the last 2 rows
print(df.tail(2))
unnamed: 0 Player Country ... Ground Date Unnamed: 15
92850 92850 A Zampa AUS ... Sydney 3/13/2020 NaN
92851 92851 JR Hazlewood AUS ... Sydney 3/13/2020 NaN
[2 rows x 17 columns]
The tail() method in above example is an optional parameter. By setting it, we fix the number of rows we want from the DataFrame.
Head() & tail() functions are not only used to get the top and bottom lines but also are used every time changes that you have made.
Lets see and example, I have created a dataframe from dictionary.
#Example of head and tail method
#Creating a dataframe from dictionary
import pandas as pd
sample_data = {'Model Number':['1101', '1102', '1103', '1104', '1105', '1106', '1107', '1108', '1109','1110'],
'Price':[10000, 20000, 30000, 40000, 7000, 50000, 500, 4500, 6800, 5500],
'Quantity':[2, 3, 4, 5, 2, 8, 10, 15, 20, 4]}
df = pd.DataFrame(sample_data)
#Creating Revenue
df['Revenue'] = df['Quantity'] * df['Price']
#Get data from head method
df.head(3)
#Output
Model Number Price Quantity Revenue
0 1101 10000 2 20000
1 1102 20000 3 60000
2 1103 30000 4 120000
#get data from tail method
df.tail()
#Output
Model Number Price Quantity Revenue
5 1106 50000 8 400000
6 1107 500 10 5000
7 1108 4500 15 67500
8 1109 6800 20 136000
9 1110 5500 4 22000
Here, I have 3 columns and 10 rows. Then I want to add a Revenue column, for that I specified Revenue as Quantity * Price.
Now to check whether modification is done successfully or not we used head() and tail() method.
These functions saves a lot of time as instead of fetching whole data we can use these methods.
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Thanks dear. Glad you like it!!!