Pandas str.join() to join string/list elements with passed delimiter

Pandas str.join() to Join String/List Elements with Passed Delimiter

In data analysis, it's often necessary to manipulate data structures like Pandas DataFrames. One common operation is adding a new row at the top of an existing DataFrame. This can be useful when you need to insert header information or prioritize certain data entries.

Method 1: Using pd.concat()

The pd.concat() function is a powerful tool for combining DataFrames. To add a row at the top, you can create a new DataFrame with the row you want to insert and concatenate it with the existing DataFrame.

import pandas as pd

# Existing DataFrame
df = pd.DataFrame({
    'Name': ['John', 'Jane'],
    'Age': [28, 34],
    'City': ['New York', 'Los Angeles']
})

# New row to be added
new_row = pd.DataFrame({
    'Name': ['Alice'],
    'Age': [30],
    'City': ['Chicago']
})

# Concatenate new row at the top
df = pd.concat([new_row, df], ignore_index=True)
print(df)

    Name  Age         City
0  Alice   30      Chicago
1   John   28    New York
2   Jane   34  Los Angeles

Method 2: Using df.loc[]

Another approach is to use the df.loc[] method, which allows you to assign a new row at a specific index. By assigning to a negative index, you can effectively add a row at the top.

import pandas as pd

# Existing DataFrame
df = pd.DataFrame({
    'Name': ['John', 'Jane'],
    'Age': [28, 34],
    'City': ['New York', 'Los Angeles']
})

# New row to be added
df.loc[-1] = ['Alice', 30, 'Chicago']

# Shift the index
df.index = df.index + 1

# Sort the index
df = df.sort_index()

print(df)

    Name  Age         City
0  Alice   30      Chicago
1   John   28    New York
2   Jane   34  Los Angeles

Method 3: Using df.append()

The df.append() method can also be used to add rows. However, it's important to note that this method is deprecated and may be removed in future versions of Pandas. It's recommended to use pd.concat() instead.

import pandas as pd

# Existing DataFrame
df = pd.DataFrame({
    'Name': ['John', 'Jane'],
    'Age': [28, 34],
    'City': ['New York', 'Los Angeles']
})

# New row to be added
new_row = pd.DataFrame({
    'Name': ['Alice'],
    'Age': [30],
    'City': ['Chicago']
})

# Append new row at the top
df = new_row.append(df, ignore_index=True)
print(df)

    Name  Age         City
0  Alice   30      Chicago
1   John   28    New York
2   Jane   34  Los Angeles

Conclusion

Adding a row at the top of a Pandas DataFrame can be accomplished using various methods, each with its own advantages. The pd.concat() method is versatile and efficient, making it a preferred choice for many. The df.loc[] method offers precise control over index placement, while the df.append() method provides a straightforward approach, though it's deprecated.

Choose the method that best fits your specific use case and ensure your code remains efficient and maintainable.