Creating columns anew in a Pandas DataFrame using seven distinct functions
When working with data in a Pandas DataFrame, there are several methods available to create new columns. These methods include , , , , , , and more.
Using
The method returns a new DataFrame with the added column(s). You provide the new column name and its values as keyword arguments. This is useful for chaining operations without modifying the original DataFrame directly.
For example:
Using operator
The most straightforward way to assign a new column is by using the operator. This modifies the DataFrame in place and allows assignment of lists, arrays, or scalar values.
For example:
Using
The method inserts a new column at a specified position. It’s helpful when you want the new column in a specific place rather than at the end.
For example:
Using
While not used for adding columns alone, creates conditional columns by applying a condition. This keeps values where the condition is True and replaces others, effectively creating a new conditional column.
For example:
Using
For string columns, you can split strings into lists and store them in a new column using the method. This is useful for parsing and expanding parts of string data into a new column.
For example:
Using
To concatenate strings from a column or multiple columns into one new column, you can use the method. This concatenates all elements of the column into a single string or can combine columns elementwise.
For example:
Example Usage
Here's an example demonstrating the usage of each method:
```python import pandas as pd import numpy as np
df = pd.DataFrame({ 'A': np.random.randint(1, 10, 5), 'B': ['foo', 'bar', 'baz', 'qux', 'quux'] })
df = df.assign(C = df['A'] * 2)
df['D'] = np.where(df['A'] > 5, 'high', 'low') # where for conditional new column
df.insert(2, 'E', list('abcde'))
df['Split_B'] = df['B'].str.split('a')
concatenated = df['B'].str.cat(sep=' ') print(df) print("Concatenated B column:", concatenated) ```
In this example, each method creates or manipulates new columns effectively.
The NumPy select function
In addition to Pandas, the NumPy library also provides a function that can be used to create new columns based on multiple conditions, with different values assigned for each set of conditions.
For instance, if division is A and mes1 is higher than 10, the value is 1; if division is B and mes1 is higher than 10, the value is 2; otherwise, the value is 0.
Creating new columns is a common task in data analysis, data cleaning, and feature engineering for machine learning, and both Pandas and NumPy make it easy with their respective functions and methods.
Technology plays a crucial role in data-and-cloud computing, as tools like Pandas and NumPy simplify the process of working with data.
The NumPy select function can be utilized to create new columns based on multiple conditions, making it a valuable tool in data analysis, data cleaning, and feature engineering for machine learning.
