30 Dec Indexing in Pandas
In this lesson, we will learn how Indexing works in Pandas. We can easily index and select data in Pandas.
Let’s say we have the following CSV file Students.csv:
Indexing
Indexing means selecting specific rows and columns of data from DataFrame. A DataFrame includes columns, index, and data. Let us see some examples:
- Indexing in Pandas using the indexing operator
- Indexing in Pandas using loc[]
- Indexing in Pandas using iloc[]
Indexing in Pandas using the indexing operator
We can directly use the [] i.e. the indexing operator in Pandas to retrieve records. Let us see an example:
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import pandas as pd # Input CSV file # Loading CSV in the DataFrame df = pd.read_csv('Students.csv',index_col ="Student") # Display the CSV file records print("Our DataFrame =\n", df) # Use the indexing operator res = df["Marks"] # Retrieving marks of students print("\n",res) |
Output
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Our DataFrame = Student Rank Marks Amit 1 95 Virat 2 90 David 3 80 Will 4 75 Steve 5 65 Student Amit 95 Virat 90 David 80 Will 75 Steve 65 |
Indexing in Pandas using loc[]
To retrieve a single row in Pandas, use the loc[] in Pandas. Let us see an example:
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import pandas as pd # Input CSV file # Loading CSV in the DataFrame df = pd.read_csv('Students.csv',index_col ="Student") # Display the CSV file records print("Our DataFrame =\n", df) # Retrieving a single row with Name Amit print("\n",df.loc["Amit"]) |
Output
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Our DataFrame Student Rank Marks Amit 1 95 Virat 2 90 David 3 80 Will 4 75 Steve 5 65 Rank 1 Marks 95 Student: Amit, dtype:object |
Indexing in Pandas using iloc[]
To retrieve the rows and columns by position, use the iloc[] in Pandas. Let us see an example:
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import pandas as pd # Input CSV file # Loading CSV in the DataFrame df = pd.read_csv('Students.csv',index_col ="Student") # Display the CSV file records print("Our DataFrame =\n",df) # Retrieving row 3 records res = df.iloc[2] print("\n",res) |
Output
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Our DataFrame = Student Rank Marks Amit 1 95 Virat 2 90 David 3 80 Will 4 75 Steve 5 65 Student David Rank 3 Marks 80 |
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