Append rows to Dataframe in Pandas

Appending rows to a DataFrame in pandas is a common operation. Here's a simple tutorial on how you can do this.

1. Setup:

Make sure you have pandas installed:

pip install pandas

2. Import Necessary Libraries:

import pandas as pd

3. Create a Sample DataFrame:

data = {
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': ['p', 'q', 'r']
}
df = pd.DataFrame(data)
print(df)

4. Append Rows:

a. Append a Single Row:

You can append a single row using the loc indexer. Ensure you provide the correct index.

df.loc[3] = [4, 7, 's']
print(df)

b. Append Rows from Another DataFrame:

To append rows from another DataFrame, you can use the append method.

df2 = pd.DataFrame({
    'A': [5, 6],
    'B': [8, 9],
    'C': ['t', 'u']
})

df = df.append(df2, ignore_index=True)
print(df)

Note: The ignore_index=True argument resets the index of the DataFrame. If you set it to False, the original indices of the appended rows will be retained.

c. Append a Row using a Dictionary:

To append a single row, you can also use a dictionary, where the keys correspond to the columns and the values to the row data.

row_data = {'A': 7, 'B': 10, 'C': 'v'}
df = df.append(row_data, ignore_index=True)
print(df)

5. Append Rows Using Concatenation:

You can also append rows using the concat function, which provides more flexibility in certain scenarios.

df3 = pd.DataFrame({
    'A': [8, 9],
    'B': [11, 12],
    'C': ['w', 'x']
})

df = pd.concat([df, df3], ignore_index=True)
print(df)

Summary:

Appending rows in a DataFrame is straightforward, but it's worth noting that constantly appending rows, especially in a loop, can be inefficient since pandas creates a new DataFrame each time you append. If you have a lot of rows to append, it's more efficient to create a list of data and append it all at once rather than one-by-one.

Appending rows to Pandas DataFrame:

Use the append() method to add rows to a DataFrame.

import pandas as pd

data = {'Name': ['Alice', 'Bob'],
        'Age': [25, 30]}

df = pd.DataFrame(data)

# Append a new row
new_row = {'Name': 'Charlie', 'Age': 28}
df = df.append(new_row, ignore_index=True)

How to add rows to a DataFrame in Pandas:
- Use the loc[] method to add a new row to the DataFrame.
```
df.loc[len(df)] = ['David', 35]
```

Append DataFrame to another in Pandas:

Append one DataFrame to another using the append() method.

df1 = pd.DataFrame({'Name': ['Alice'], 'Age': [25]})
df2 = pd.DataFrame({'Name': ['Bob'], 'Age': [30]})

result_df = df1.append(df2, ignore_index=True)

Concatenate DataFrames vertically in Pandas:
- Use concat() to concatenate DataFrames vertically.
```
df_concatenated = pd.concat([df1, df2], ignore_index=True)
```
Appending a row to a Pandas DataFrame:
- Use the loc[] method to append a row.
```
df.loc[len(df)] = ['Eva', 27]
```

Pandas append vs concat for adding rows:

Understand the difference between append() and concat() for row addition.

# Using append
df_append = df1.append(df2, ignore_index=True)

# Using concat
df_concat = pd.concat([df1, df2], ignore_index=True)

Inserting a new row to Pandas DataFrame:
- Use the loc[] method to insert a new row at a specific index.
```
df.loc[2] = ['Frank', 32]
```

Appending DataFrames with different columns in Pandas:

Append DataFrames with different columns and handle missing values.

df1 = pd.DataFrame({'Name': ['Alice'], 'Age': [25]})
df2 = pd.DataFrame({'Name': ['Bob'], 'Salary': [50000]})

result_df = df1.append(df2, ignore_index=True, sort=False)

Efficient ways to append rows in Pandas DataFrame:

Use the concat() method for efficient row addition.

new_data = pd.DataFrame({'Name': ['Grace'], 'Age': [29]})
df = pd.concat([df, new_data], ignore_index=True)