Python | Pythonic way of constructing a dictionary of lists by column

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Dictionary of lists - by column

In Python, you can create a dictionary of lists where each list represents a column of data. This is a common way to represent data when working with Pandas dataframes, as each list represents a single column of data in the dataframe.

Here's an example of how to create a dictionary of lists by column:

# create a dictionary of lists by column

data = {

'name': ['John', 'Jane', 'Mike', 'Susan'],

'age': [30, 25, 35, 40],

'gender': ['M', 'F', 'M', 'F']

}

# loop through the dictionary of lists and print each column of data

for col in data:

print(col, data[col])

This will output each column of data as a list:

name ['John', 'Jane', 'Mike', 'Susan']

age [30, 25, 35, 40]

gender ['M', 'F', 'M', 'F']

In this example, the data dictionary contains three lists, each representing a single column of data with the keys name, age, and gender.

You can also add or modify data in a dictionary of lists by column using the dictionary keys and list indexing:

# add a new column of data to the dictionary of lists

data['salary'] = [50000, 60000, 70000, 80000]

# modify the age of an existing column of data

data['age'][1] = 30

# loop through the updated dictionary of lists and print each column of data

for col in data:

print(col, data[col])

This will output the updated dictionary of lists:

name ['John', 'Jane', 'Mike', 'Susan']

age [30, 30, 35, 40]

gender ['M', 'F', 'M', 'F']

salary [50000, 60000, 70000, 80000]

In this example, we added a new column of data to the data dictionary by creating a new key and list, and modified the age of an existing column of data by accessing it with its key and list index.