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Add a row at top in pandas DataFrame

Adding a row at the top of a pandas DataFrame involves a few steps. Here's a step-by-step tutorial on how to do this:

1. Setup

First, let's set up our environment and create a sample DataFrame:

# Import required libraries
import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [7, 8, 9]
})
print("Original DataFrame:")
print(df)

2. Create a new row

Let's create a new row that we want to add to the top of the DataFrame:

new_row = {'A': 10, 'B': 11, 'C': 12}

3. Add the new row at the top of the DataFrame

There are multiple methods to do this, but one of the simplest ways is to concatenate the new row with the original DataFrame:

# Convert the new row to a DataFrame and concatenate
df = pd.concat([pd.DataFrame([new_row]), df], ignore_index=True)
print("\nDataFrame after adding the new row at the top:")
print(df)

Full Code:

Combining everything, we have:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [7, 8, 9]
})
print("Original DataFrame:")
print(df)

# New row to be added
new_row = {'A': 10, 'B': 11, 'C': 12}

# Convert the new row to a DataFrame and concatenate
df = pd.concat([pd.DataFrame([new_row]), df], ignore_index=True)
print("\nDataFrame after adding the new row at the top:")
print(df)

When you run the above code, you'll notice that the new row is added to the top of the original DataFrame. The ignore_index=True argument ensures that the index values are reset and the rows are numbered sequentially.

  1. Add row at the beginning of pandas DataFrame:

    import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# New row to be added
new_row = pd.Series({'A': 0, 'B': 0})

# Concatenate the new row at the beginning
df = pd.concat([new_row, df]).reset_index(drop=True)

  2. Insert row at the top of pandas DataFrame:

    import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# New row to be added
new_row = pd.Series({'A': 0, 'B': 0})

# Use the loc method to insert the new row at index 0
df.loc[-1] = new_row
df.index = df.index + 1
df = df.sort_index()

  3. Python pandas DataFrame add row at index 0:

    import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# New row to be added
new_row = pd.Series({'A': 0, 'B': 0})

# Use the append method to add the new row at index 0
df = df.append(new_row, ignore_index=True)

  4. Add header row to pandas DataFrame:

    import pandas as pd

# Sample DataFrame without header
df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])

# Header row to be added
header_row = pd.Series({'A': 'Header_A', 'B': 'Header_B'})

# Concatenate the header row at the beginning
df = pd.concat([header_row, df]).reset_index(drop=True)

  5. Prepend row to pandas DataFrame in Python:

    import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# New row to be added
new_row = pd.Series({'A': 0, 'B': 0})

# Use the loc method to prepend the new row
df.loc[len(df)] = new_row

  6. Inserting a new row at the beginning of a pandas DataFrame:

    import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# New row to be added
new_row = pd.Series({'A': 0, 'B': 0})

# Use the loc method to insert the new row at index 0
df.loc[-1] = new_row
df.index = df.index + 1
df = df.sort_index()

  7. Add a row to the top of pandas DataFrame with values:

    import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Values of the new row
values = [0, 0]

# Use the loc method to insert the new row at index 0 with specified values
df.loc[-1] = values
df.index = df.index + 1
df = df.sort_index()

  8. DataFrame insert row at the first position in Python:

    import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# New row to be added
new_row = pd.Series({'A': 0, 'B': 0})

# Use the loc method to insert the new row at index 0
df.loc[-1] = new_row
df.index = df.index + 1
df = df.sort_index()

  9. Adding a row to the top of a pandas DataFrame with specified columns:

    import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Values and columns of the new row
values = [0, 0]
columns = ['A', 'B']

# Create a DataFrame for the new row
new_row = pd.DataFrame([values], columns=columns)

# Concatenate the new row at the beginning
df = pd.concat([new_row, df]).reset_index(drop=True)

  10. Shift DataFrame rows down and add a new row at the top:

    import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# New row to be added
new_row = pd.Series({'A': 0, 'B': 0})

# Shift existing rows down
df = df.shift(1)

# Assign the new row at the top
df.loc[0] = new_row

  11. Pandas DataFrame insert row at position 0:

    import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# New row to be added
new_row = pd.Series({'A': 0, 'B': 0})

# Use the loc method to insert the new row at index 0
df.loc[-1] = new_row
df.index = df.index + 1
df = df.sort_index()

  12. Python pandas DataFrame add row with index 0:

    import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# New row to be added
new_row = pd.Series({'A': 0, 'B': 0})

# Use the loc method to insert the new row at index 0
df.loc[-1] = new_row
df.index = df.index + 1
df = df.sort_index()

  13. DataFrame concat to add a row at the top in pandas:

    import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Values and columns of the new row
values = [0, 0]
columns = ['A', 'B']

# Create a DataFrame for the new row
new_row = pd.DataFrame([values], columns=columns)

# Concatenate the new row at the beginning
df = pd.concat([new_row, df]).reset_index(drop=True)

  14. Inserting a row with NaN values at the top of pandas DataFrame:

    import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Columns of the new row
columns = ['A', 'B']

# Create a DataFrame with NaN values for the new row
new_row = pd.DataFrame([None, None], index=columns).T

# Concatenate the new row at the beginning
df = pd.concat([new_row, df]).reset_index(drop=True)
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