22 Dec String Operations on Text Data in Pandas
We can easily perform operations on strings in Pandas using the string methods. In this lesson, we will see how to perform the following string operations on text data in Pandas Series:
- lower(): Perform lowercase on text data
- upper(): Perform uppercase on text data
- title(): Convert text data to camel case
- len(): To get the length of each element in the Series.
- count(): Count the non-empty cells for each column or row
- contain(): Search for a value in a column.
lower() method
To lowercase text data, use the lower() method in Pandas. Let us see an example:
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import pandas as pd # Data to be stored in the Pandas Series data = ['Jacob', 'Amit', 'TRENT', 'Nathan', 'MaRtIN'] # Create a Series using the Series() method s = pd.Series(data) # Display the Series print("Series: \n", s) # Convert the text data to lowercase print("\nLowercase data:\n",s.str.lower()) |
Output
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Series: 0 Jacob 1 Amit 2 TRENT 3 Nathan 4 MaRtIN Lowercase data: 0 jacob 1 amit 2 trent 3 nathan 4 martin |
upper() method
To uppercase text data, use the upper() method in Pandas. Let us see an example:
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import pandas as pd # Data to be stored in the Pandas Series data = ['jaCoB', 'Amit', 'trent', 'Nathan', 'MaRtIN'] # Create a Series using the Series() method s = pd.Series(data) # Display the Series print("Series: \n", s) # Convert the text data to uppercase print("\nUppercase data:\n",s.str.upper()) |
Output
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Series: 0 jaCoB 1 Amit 2 trent 3 Nathan 4 MaRtIN Uppercase data: 0 JACOB 1 AMIT 2 TRENT 3 NATHAN 4 MARTIN |
title() method
To convert the text data to camel case, use the title() method in Pandas. Let us see an example:
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import pandas as pd # Data to be stored in the Pandas Series data = ['jaCoB', 'Amit', 'trent', 'NATHan', 'MaRtIN'] # Create a Series using the Series() method s = pd.Series(data) # Display the Series print("Series: \n", s) # Convert the text data to camel case print("\nCamel case data:\n",s.str.title()) |
Output
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Series: 0 jaCoB 1 Amit 2 trent 3 NATHan 4 MaRtIN Camel case data: 0 Jacob 1 Amit 2 Trent 3 Nathan 4 Martin |
len() method
To get the length of each element in the Series, use the len() method in Pandas. Let us see an example:
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import pandas as pd # Data to be stored in the Pandas Series data = ['Jacob Oram', 'Amit', 'Trent', 'Nathan Lyon', 'Martin'] # Create a Series using the Series() method s = pd.Series(data) # Display the Series print("Series: \n", s) # Get the length of each element print("\nLength:\n",s.str.len()) |
Output
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Series: 0 Jacob Oram 1 Amit 2 Trent 3 Nathan Lyon 4 Martin Length: 0 10 1 4 2 5 3 11 |
count() method
To count the non-empty cells for each column or row in a Series, use the count() method. Let us see an example. We have stored the data in the series with some NaN values:
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import numpy as np import pandas as pd # Data to be stored in the Pandas Series data = [np.nan, "Amit Diwan", "Trent", "Nathan Lyon", np.nan] # Create a Series using the Series() method series = pd.Series(data) # Display the Series print("Series:\n", series) # Get the count print("\nCount:\n", series.count()) |
Output
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Series: 0 NaN 1 Amit Diwan 2 Trent 3 Nathan Lyon 4 NaN dtype: object Count: 3 |
contains() method
The contains() method is used in Pandas to search for a value in a column. Let us see an example:
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import pandas as pd # Data to be stored in the Pandas Series data = ['Jacob Oram', 'Amit', 'Trent', 'Nathan Lyon', 'Martin'] # Create a Series using the Series() method s = pd.Series(data) # Display the Series print("Series: \n", s) # Search for a specific value print("\nDoes the specific value exist?\n",s.str.contains('Amit')) |
Output
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Series: 0 Jacob Oram 1 Amit 2 Trent 3 Nathan Lyon 4 Martin Does the specific value exist? 0 False 1 True 2 False 3 False 4 False |
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