How to Start the Index of a Pandas DataFrame at 1

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# How to Start the Index of a Pandas DataFrame at 1

To start the index of a Pandas DataFrame at 1:

By default, the DataFrame index starts at 0.
Use the index attribute to select the current DataFrame index.
Set the index attribute to the current index plus 1.

main.py

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import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'experience': [1, 3, 1, 7],
    'salary': [175.1, 180.2, 190.3, 205.4],
})

print(df.index)

print('-' * 50)

df.index += 1

print(df.index)

print('-' * 50)

print(df)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

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RangeIndex(start=0, stop=4, step=1)
--------------------------------------------------
RangeIndex(start=1, stop=5, step=1)
--------------------------------------------------
    name  experience  salary
1  Alice           1   175.1
2  Bobby           3   180.2
3   Carl           1   190.3
4    Dan           7   205.4

start index of pandas dataframe at 1

The DataFrame.index attribute returns the index (row labels) of the DataFrame.

By default, the DataFrame index starts at 0.

You can set the index to its current value plus 1 to have the DataFrame index start at 1.

main.py

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df.index += 1

The expression df.index += 1 is the short-hand version of df.index = df.index + 1.

Both assignments set the index attribute of the DataFrame to the current index value plus 1.

# Start the Index of a Pandas DataFrame at 1 using `range()`

You can also use the range class to start the index of a Pandas DataFrame at 1.

main.py

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import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'experience': [1, 3, 1, 7],
    'salary': [175.1, 180.2, 190.3, 205.4],
})

df.index = range(1, len(df) + 1)

#     name  experience  salary
# 1  Alice           1   175.1
# 2  Bobby           3   180.2
# 3   Carl           1   190.3
# 4    Dan           7   205.4
print(df)

start index of pandas dataframe at 1 using range

The code for this article is available on GitHub

The range class is commonly used for looping a specific number of times.

The range() class takes the following arguments:

Name	Description
`start`	An integer representing the start of the range (defaults to `0`)
`stop`	Go up to, but not including the provided integer
`step`	Range will consist of every N numbers from `start` to `stop` (defaults to `1`)

We set the start parameter to 1 to have the index start at 1.

The stop parameter is set to the length of the DataFrame plus 1 because the stop value is exclusive (up to, but not including).

main.py

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import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'experience': [1, 3, 1, 7],
    'salary': [175.1, 180.2, 190.3, 205.4],
})

# 👇️ [1, 2, 3, 4]
print(list(range(1, len(df) + 1)))

We could've also used the DataFrame.shape attribute to achieve the same result.

main.py

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import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'experience': [1, 3, 1, 7],
    'salary': [175.1, 180.2, 190.3, 205.4],
})

df.index = range(1, df.shape[0] + 1)

#     name  experience  salary
# 1  Alice           1   175.1
# 2  Bobby           3   180.2
# 3   Carl           1   190.3
# 4    Dan           7   205.4
print(df)

The code for this article is available on GitHub

The DataFrame.shape attribute returns a tuple that represents the dimensionality of the DataFrame.

main.py

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import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'experience': [1, 3, 1, 7],
    'salary': [175.1, 180.2, 190.3, 205.4],
})

# 👇️ (4, 3)
print(df.shape)

The first element in the tuple is the number of rows and the second is the number of columns.

We added 1 to the number of rows because the stop value of the range() class is exclusive (up to, but not including).

main.py

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df.index = range(1, df.shape[0] + 1)

# Start the Index of a Pandas DataFrame at 1 using `numpy.arange()`

You can also use the numpy.arange() method to start the index of a Pandas DataFrame at 1.

First, make sure you have the numpy module installed.

shell

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pip install numpy

# or with pip3
pip3 install numpy

Now, import the numpy module and use the numpy.arange() method.

main.py

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import pandas as pd
import numpy as np

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'experience': [1, 3, 1, 7],
    'salary': [175.1, 180.2, 190.3, 205.4],
})

df.index = np.arange(1, len(df) + 1)

#     name  experience  salary
# 1  Alice           1   175.1
# 2  Bobby           3   180.2
# 3   Carl           1   190.3
# 4    Dan           7   205.4
print(df)

pandas dataframe start index at 1 using numpy arange

The code for this article is available on GitHub

The numpy.arange() method returns the values within the given range.

main.py

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import pandas as pd
import numpy as np

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'experience': [1, 3, 1, 7],
    'salary': [175.1, 180.2, 190.3, 205.4],
})

# [1 2 3 4]
print(np.arange(1, len(df) + 1))

The range in the example starts at 1 and goes up to, but not including len(df) + 1.

# Start the Index of a Pandas DataFrame at 1 using `RangeIndex`

You can also use the pandas.RangeIndex class to start the index of a Pandas DataFrame at 1.

main.py

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import pandas as pd

a_dict = {
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'experience': [1, 3, 1, 7],
    'salary': [175.1, 180.2, 190.3, 205.4],
}

first_key = list(a_dict.keys())[0]

row_length = len(a_dict[first_key])

df = pd.DataFrame(a_dict,
                  index=pd.RangeIndex(
                      start=1,
                      stop=row_length + 1,
                      name='index')
                  )

#         name  experience  salary
# index
# 1      Alice           1   175.1
# 2      Bobby           3   180.2
# 3       Carl           1   190.3
# 4        Dan           7   205.4
print(df)

start index of pandas dataframe at 1 using range index

The code for this article is available on GitHub

We set the index parameter when instantiating the pandas.DataFrame() class.

The RangeIndex class sets the index of the DataFrame using an integer range.

We set the start parameter to 1 to have the index start at 1.

The stop parameter is set to the number of rows in the DataFrame + 1.

We have to add 1 to the number of rows because the stop parameter is exclusive (up to, but not including).

I've also written an article on how to keep the Index when merging DataFrames.

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How to Start the Index of a Pandas DataFrame at 1

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