Create Pandas dataframe with list as values in rows

Question

How to create pandas dataframe in the following format:

      A            B            C             D
0    [1,2,3,4]    [2,3,4,5]     [4,5,5,6]     [6,3,4,5]
1    [2,3,5,6]    [3,4,6,6]     [3,4,5,7]     [2,6,3,4]
2    [8,9,6,7]    [5,7,9,5]     [3,7,9,5]     [5,7,9,8]

Basically each row has a list as elements. I am trying to classify data using machine learning. Each data point has 40 x 6 values. Is there any other format which is suitable to be fed into classifier.

Edit:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plot

from sklearn.neighbors import KNeighborsClassifier

# Read csv data into pandas data frame
data_frame = pd.read_csv('data.csv')

extract_columns = ['LinearAccX', 'LinearAccY', 'LinearAccZ', 'Roll', 'pitch', 'compass']

# Number of sample in one shot
samples_per_shot = 40

# Calculate number of shots in dataframe
count_of_shots = len(data_frame.index)/samples_per_shot

# Initialize Empty data frame
training_index = range(count_of_shots)
training_data_list = []

# flag for backward compatibility
make_old_data_compatible_with_new = 0

if make_old_data_compatible_with_new:
    # Convert 40 shot data to 25 shot data
    # New logic takes 25 samples/shot
    # old logic takes 40 samples/shot
    start_shot_sample_index = 9
    end_shot_sample_index = 34
else:
    # Start index from 1 and continue till lets say 40
    start_shot_sample_index = 1
    end_shot_sample_index = samples_per_shot

# Extract each shot into pandas series
for shot in range(count_of_shots):
    # Extract current shot
    current_shot_data = data_frame[data_frame['shot_no']==(shot+1)]

    # Select only the following column
    selected_columns_from_shot = current_shot_data[extract_columns]

    # Select columns from selected rows
    # Find start and end row indexes
    current_shot_data_start_index = shot * samples_per_shot + start_shot_sample_index
    current_shot_data_end_index = shot * samples_per_shot + end_shot_sample_index
    selected_rows_from_shot = selected_columns_from_shot.ix[current_shot_data_start_index:current_shot_data_end_index]

# Append to list of lists
# Convert selected short into multi-dimensional array
training_data_list.append([selected_columns_from_shot[extract_columns[index]].values.tolist() for index in range(len(extract_c    olumns))])

# Append each sliced shot into training data
training_data = pd.DataFrame(training_data_list, columns=extract_columns)
training_features = [1 for i in range(count_of_shots)]
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(training_data, training_features)

Answer 1

simple

pd.DataFrame(
    [[[1, 2, 3, 4], [2, 3, 4, 5], [4, 5, 5, 6], [6, 3, 4, 5]],
     [[2, 3, 5, 6], [3, 4, 6, 6], [3, 4, 5, 7], [2, 6, 3, 4]],
     [[8, 9, 6, 7], [5, 7, 9, 5], [3, 7, 9, 5], [5, 7, 9, 8]]],
    columns=list('ABCD')
)

Or

build a Series with a MultiIndex and unstack

lst = [
    [1, 2, 3, 4],
    [2, 3, 4, 5],
    [4, 5, 5, 6],
    [6, 3, 4, 5],
    [2, 3, 5, 6],
    [3, 4, 6, 6],
    [3, 4, 5, 7],
    [2, 6, 3, 4],
    [8, 9, 6, 7],
    [5, 7, 9, 5],
    [3, 7, 9, 5],
    [5, 7, 9, 8]]

pd.Series(lst, pd.MultiIndex.from_product([[0, 1, 2], list('ABCD')])).unstack()

              A             B             C             D
0  [1, 2, 3, 4]  [2, 3, 4, 5]  [4, 5, 5, 6]  [6, 3, 4, 5]
1  [2, 3, 5, 6]  [3, 4, 6, 6]  [3, 4, 5, 7]  [2, 6, 3, 4]
2  [8, 9, 6, 7]  [5, 7, 9, 5]  [3, 7, 9, 5]  [5, 7, 9, 8]