Apply a function on the possible series in Pandas

The ability to apply functions across rows or columns is one of the strong points of pandas. This tutorial will show you how to apply functions to Series and DataFrames using the apply() method and related techniques.

1. Setup:

Start by installing pandas:

pip install pandas

2. Import the necessary library:

import pandas as pd

3. Create a Sample DataFrame:

data = {
    'A': [1, 2, 3, 4],
    'B': [10, 20, 30, 40],
    'C': [5, 15, 25, 35]
}

df = pd.DataFrame(data)
print(df)

4. Apply Function to Each Element of a Series:

If you have a Series and you want to apply a function to each element, you can simply use the apply() method:

def square(x):
    return x * x

df['A'] = df['A'].apply(square)
print(df)

5. Apply Function to Each Column as a Series:

Sometimes, you might want to apply a function across each column as a Series. For this, again use apply():

def range_of_series(series):
    return series.max() - series.min()

ranges = df.apply(range_of_series)
print(ranges)

6. Apply Function to Each Row as a Series:

If you want to apply a function across rows, set the axis parameter to 1:

def row_sum(row):
    return row.sum()

df['RowSum'] = df.apply(row_sum, axis=1)
print(df)

7. Apply a Function Element-wise to a DataFrame:

For element-wise operations on a DataFrame, you should use the applymap() method:

def halve(x):
    return x / 2

half_df = df.applymap(halve)
print(half_df)

8. Using Lambda Functions:

For simple operations, you can use lambda functions with apply() and applymap() instead of defining a separate function:

# Using lambda to square each element in column 'B'
df['B'] = df['B'].apply(lambda x: x**2)
print(df)

9. Using Built-in Functions:

You can also pass built-in functions directly to apply():

# Calculate the length of numbers in column 'C' as strings
df['Length'] = df['C'].apply(str).apply(len)
print(df)

Summary:

Pandas provides a variety of ways to apply functions to Series and DataFrames, enabling powerful and flexible data transformations. The key functions to remember are apply() for applying functions to entire rows or columns, and applymap() for element-wise operations on DataFrames. Using these in combination with lambda functions or predefined functions allows for a wide range of data manipulations.

Pandas apply function to series:
- The apply function is used to apply a function along the axis of a Pandas Series.
```
# Apply a function to each element of a Series
result_series = series.apply(lambda x: x * 2)
```
Apply custom function to Pandas series:
- Define a custom function and use apply to apply it to each element of the series.
```
def custom_function(x):
    return x ** 2

result_series = series.apply(custom_function)
```
How to use apply in Pandas for series:
- Use apply to transform each element of a Series using a specified function.
```
result_series = series.apply(lambda x: x + 10)
```
Python Pandas apply method examples:
- The apply method can be used with various functions, including built-in, custom, or lambda functions.
```
result_series = series.apply(np.sqrt)  # Apply square root function
```
Applying a lambda function to Pandas series:
- Lambda functions are convenient for applying quick transformations to each element.
```
result_series = series.apply(lambda x: x * 3)
```
Pandas apply function to multiple series:
- Use apply to apply a function to multiple series, combining their values.
```
result_series = df.apply(lambda row: row['A'] + row['B'], axis=1)
```
Using applymap on Pandas series:
- applymap is used for element-wise operations on entire DataFrames. For series, use apply.
```
result_series = series.apply(lambda x: x.upper())
```
Apply function element-wise to Pandas series:
- The apply function can be used for element-wise operations, transforming each element based on the applied function.
```
result_series = series.apply(lambda x: x / 2)
```
Apply function to each row in Pandas series:
- Use apply to apply a function to each row in a Pandas DataFrame.
```
result_series = df.apply(lambda row: row.sum(), axis=1)
```
Pandas apply vs map for series operations:
- apply is used for element-wise and row/column-wise operations on DataFrames and Series. map is typically used for element-wise transformations in Series.
```
result_series = series.map(lambda x: x * 2)
```