To change cell values to list format in a pandas dataframe, you can use the apply method along with a lambda function. You can create a lambda function that converts the cell value to a list and then use the apply method to apply this lambda function to each cell in the dataframe. This will transform the cell values into list format.

How to transform pandas dataframe columns to list?

You can transform pandas dataframe columns to a list by using the tolist() method. Here is an example:

import pandas as pd

Create a sample dataframe

data = {'A': [1, 2, 3, 4], 'B': ['apple', 'banana', 'cherry', 'date']} df = pd.DataFrame(data)

Transform column A to a list

column_A_list = df['A'].tolist() print('Column A as list:', column_A_list)

Transform column B to a list

column_B_list = df['B'].tolist() print('Column B as list:', column_B_list)

This will output:

Column A as list: [1, 2, 3, 4] Column B as list: ['apple', 'banana', 'cherry', 'date']

How to convert pandas dataframe to list of arrays with specific shape?

You can convert a pandas DataFrame to a list of arrays with a specific shape by first converting the DataFrame to a numpy array and then reshaping it to the desired shape. Here's an example code snippet to demonstrate how to do this:

import pandas as pd import numpy as np

Create a sample DataFrame

df = pd.DataFrame({'A': [1, 2, 3, 4, 5], 'B': [6, 7, 8, 9, 10]})

Convert the DataFrame to a numpy array

arr = df.to_numpy()

Reshape the array to the desired shape

desired_shape = (2, -1) # This will reshape the array to have 2 rows and the appropriate number of columns arr_reshaped = np.reshape(arr, desired_shape)

Convert the reshaped array to a list of arrays

list_of_arrays = arr_reshaped.tolist()

print(list_of_arrays)

In this example, the DataFrame df is first converted to a numpy array arr, which is then reshaped to have 2 rows and the appropriate number of columns (determined by the -1 in the desired_shape). Finally, the reshaped array is converted to a list of arrays list_of_arrays.

What is the fastest way to convert pandas dataframe to list in Python?

One of the fastest ways to convert a pandas DataFrame to a list in Python is to use the values.tolist() method. This method converts the DataFrame to a list of lists containing the data from the DataFrame.

Here is an example:

import pandas as pd

Create a sample DataFrame

df = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'c']})

Convert DataFrame to a list of lists

list_of_lists = df.values.tolist()

print(list_of_lists)

This will output:

[[1, 'a'], [2, 'b'], [3, 'c']]

Using the values.tolist() method is a fast and efficient way to convert a pandas DataFrame to a list in Python.

How to convert a pandas dataframe to list?

You can convert a pandas DataFrame to a list by using the values attribute of the DataFrame. Here's how you can do it:

import pandas as pd

Create a sample DataFrame

data = {'A': [1, 2, 3], 'B': ['a', 'b', 'c']} df = pd.DataFrame(data)

Convert the DataFrame to a list

df_list = df.values.tolist()

print(df_list)

This will output:

[[1, 'a'], [2, 'b'], [3, 'c']]

Each row of the DataFrame will be converted to a list, and all these lists will be stored in another list.

What is the algorithm for converting pandas dataframe to list in Python?

To convert a pandas DataFrame into a list in Python, you can use the values attribute of the DataFrame to get a numpy array representation, and then convert the numpy array to a list.

Here is an example algorithm:

1. Import the pandas library
2. Create a pandas DataFrame
3. Use the values attribute of the DataFrame to get a numpy array representation
4. Convert the numpy array to a list using the tolist() method

Here is a sample code snippet:

import pandas as pd

Create a sample DataFrame

data = {'A': [1, 2, 3], 'B': ['foo', 'bar', 'baz']} df = pd.DataFrame(data)

Convert the DataFrame to a list

df_list = df.values.tolist()

print(df_list)

This will output:

[[1, 'foo'], [2, 'bar'], [3, 'baz']]

Now, df_list is a list representation of the pandas DataFrame df.