How can a row be added to a python dataframe based on different conditions?

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A row can be added to a DataFrame based on different conditions using the following steps:

1. Identify the conditions that need to be met for the new row to be added.
2. Create a dictionary with the data for the new row. The keys in the dictionary should match the column names in the DataFrame.
3. Use the append() method to add the new row to the DataFrame.

Here is an example:

import pandas as pd

# create a DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35]}
df = pd.DataFrame(data)

# add a new row if age is less than 30
if df['Age'].max() < 30:
    new_row = {'Name': 'David', 'Age': 21}
    df = df.append(new_row, ignore_index=True)

# add a new row if name is 'Bob'
if 'Bob' in df['Name'].tolist():
    new_row = {'Name': 'Eve', 'Age': 27}
    df = df.append(new_row, ignore_index=True)

print(df)

In this example, a new row is added to the DataFrame if the maximum age is less than 30 or if the name 'Bob' is already in the DataFrame. The append() method is used to add the new row, and the ignore_index=True parameter is used to reset the index of the DataFrame.