Pandas Get Index from DataFrame?

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  • Post category:Pandas
  • Post last modified:October 5, 2023

How to get an index from Pandas DataFrame? DataFrame.index property is used to get the index from the DataFrame. Pandas Index is an immutable sequence used for indexing DataFrame and Series. The DataFrame index is also referred to as the row index, by default index is created on DataFrame as a sequence number that starts from 0 and increments by 1. You can also assign custom values to Index.

Using the index we can select the rows from the given DataFrame or add the row at a specified Index. we can also get the index itself of the given DataFrame by using the .index property. In this article, I will explain the index property and using this property how we can get an index of DataFrame and also explain how to get the index as a list object using index.values.

1. Quick Examples of getting Index from Pandas DataFrame

If you are in a hurry, below are some quick examples of how to get an index from DataFrame.

# Below are the quick examples
# Example 1: Get the index use df.index property

# Example 2: Get the index use index.values

# Example 3: Get the index use tolist()

# Example 4: Get the column index use get_loc()

# Example 5: Get the index values using np.where()
print(list(np.where(df["Discount"] > 1200)))

2. Get Index from Pandas DataFrame

You can get the Index from the pandas DataFrame by using .index property, this index property returns Series object. Let’s create DataFrame using data from the Python dictionary then call the index property on DataFrame to get the index. When we call index property with no specified index, it will return the complete index.

# Create DataFrame
import pandas as pd
technologies = {
        'Fee' :[20000,25000,22000,30000],
df = pd.DataFrame(technologies)

# Get the index of DataFrame

Yields below output.

# Output:
   Courses    Fee Duration  Discount
0    Spark  20000   30days      1000
1  PySpark  25000   40days      2300
2   Python  22000   35days      1200
3   pandas  30000   50days      2000

RangeIndex(start=0, stop=4, step=1)

By default it returns the type of Index, since we have a range index it returned RangeIndex(). Using any looping (Python for loop) technique we can access individual indexes of a given DataFrame in Python.

# Get the index of Dataframe use for loop
for i in df.index:

# Output:
# 0
# 1
# 2
# 3

3. Get Pandas Index as a List

Sometimes you may be required to get the pandas DataFrame index as a list, we can do this by using df.index.values. Let’s pass tthis into a list, it will return the index as a list.

# Get the index use index.values

# Output:
# [0, 1, 2, 3]

4. Get Pandas Index using tolist()

Alternatively using the Pandas tolist() function we can return the index of DataFrame as a list. For example,

# Get the index as List using tolist()

# Output:
# [0, 1, 2, 3]

5. Get Column Index Using the get_loc()

From the above, we came to know how to retrieve the row index of DataFrame. However, we can also get the index of DataFrame column using the get_loc() function. For that, we have to pass the column label which we want to get its index to the get_loc() function. It will return the index location.

# Get the column index use get_loc()

# Output:
# 1

6. Get Row Index Using the Numpy Where() Function:

We can also get the index by specifying a condition passed into numpy.where() function. Let’s use NumPy library to use its functions.

# Get the index values using np.where()
print(list(np.where(df["Discount"] > 1200)))

# Output:
# [array([1, 3], dtype=int64)]

7. Conclusion

In this article, I have explained how to get the index of Pandas DataFrame by using the .index property, index.values, tolist(), function, and NumPy where() function with well-defined examples.

Happy Learning !!

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I am a Data Engineer with 20+ years of experience in transforming data into actionable insights. Over the years, I have honed my expertise in designing, implementing, and maintaining data pipelines with frameworks like Apache Spark, PySpark, Pandas, R, Hive and Machine Learning. My journey in the field of data engineering has been a continuous learning, innovation, and a strong commitment to data integrity. I have started this to share my experiences with the data as I come across. You can learn more about me at LinkedIn
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