In [1]:

    

import pandas as pd
import numpy as np


    

In [2]:

    

print(np.arange(9).reshape(3, 3))


    

    




[[0 1 2]
 [3 4 5]
 [6 7 8]]

In [3]:

    

print(pd.DataFrame(np.arange(9).reshape(3, 3)))


    

    




   0  1  2
0  0  1  2
1  3  4  5
2  6  7  8

In [4]:

    

print(pd.DataFrame(np.arange(9).reshape(3, 3),
                   columns=['col_0', 'col_1', 'col_2'],
                   index=['row_0', 'row_1', 'row_2']))


    

    




       col_0  col_1  col_2
row_0      0      1      2
row_1      3      4      5
row_2      6      7      8

In [5]:

    

print(pd.DataFrame([[0, 1, 2], [3, 4, 5], [6, 7, 8]]))


    

    




   0  1  2
0  0  1  2
1  3  4  5
2  6  7  8

In [6]:

    

print(pd.DataFrame([[0, 1, 2], [3, 4, 5], [6, 7, 8, 9, 10]]))


    

    




   0  1  2    3     4
0  0  1  2  NaN   NaN
1  3  4  5  NaN   NaN
2  6  7  8  9.0  10.0

In [7]:

    

print(pd.DataFrame({'col_0': [0, 1, 2],
                    'col_1': np.arange(3, 6),
                    'col_2': (6, 7, 8)}))


    

    




   col_0  col_1  col_2
0      0      3      6
1      1      4      7
2      2      5      8

In [8]:

    

print(pd.DataFrame({'col_0': [0, 1, 2],
                    'col_1': np.arange(3, 6),
                    'col_2': (6, 7, 8)},
                   index=['row_0', 'row_1', 'row_2']))


    

    




       col_0  col_1  col_2
row_0      0      3      6
row_1      1      4      7
row_2      2      5      8

In [9]:

    

# print(pd.DataFrame({'col_0': [0, 1, 2, 100],
#                     'col_1': np.arange(3, 6),
#                     'col_2': (6, 7, 8)}))
# ValueError: arrays must all be same length


    

In [10]:

    

print(pd.DataFrame({'col_0': [0, 1, 2],
                    'col_1': np.arange(3, 6),
                    'col_2': (6, 7, 8)},
                   index=['row_0', 'row_1', 'row_2']))


    

    




       col_0  col_1  col_2
row_0      0      3      6
row_1      1      4      7
row_2      2      5      8

In [11]:

    

print(pd.DataFrame({'row_0': [0, 1, 2],
                    'row_1': np.arange(3, 6),
                    'row_2': (7, 8, 9)}).T)


    

    




       0  1  2
row_0  0  1  2
row_1  3  4  5
row_2  7  8  9

In [12]:

    

print(pd.DataFrame.from_dict(
    {'row_0': [0, 1, 2],
     'row_1': [3, 4, 5],
     'row_2': [6, 7, 8]},
    orient='index'))


    

    




       0  1  2
row_0  0  1  2
row_1  3  4  5
row_2  6  7  8

In [13]:

    

print(pd.DataFrame.from_dict(
    {'row_0': np.array([0, 1, 2]),
     'row_1': np.array([3, 4, 5]),
     'row_2': np.array([6, 7, 8])},
    orient='index'))


    

    




       0  1  2
row_0  0  1  2
row_1  3  4  5
row_2  6  7  8

In [14]:

    

# print(pd.DataFrame.from_dict(
#     {'row_0': [0, 1, 2],
#      'row_1': np.array([3, 4, 5]),
#      'row_2': [6, 7, 8]},
#     orient='index'))
# TypeError: Expected list, got numpy.ndarray


    

In [15]:

    

print(pd.DataFrame([{'col_0': 0, 'col_1': 1, 'col_2': 2},
                    {'col_0': 3, 'col_1': 4, 'col_2': 5},
                    {'col_0': 6, 'col_1': 7, 'col_2': 8}]))


    

    




   col_0  col_1  col_2
0      0      1      2
1      3      4      5
2      6      7      8

In [16]:

    

print(pd.DataFrame([{'col_0': 0, 'col_1': 1, 'col_2': 2},
                    {'col_0': 3, 'col_1': 4, 'col_2': 5},
                    {'col_0': 6, 'col_1': 7, 'col_2': 8}],
                   index=['row_0', 'row_1', 'row_2']))


    

    




       col_0  col_1  col_2
row_0      0      1      2
row_1      3      4      5
row_2      6      7      8

In [17]:

    

print(pd.DataFrame([{'col_0': 0, 'col_1': 1, 'col_2': 2},
                    {'col_0': 3, 'col_2': 5, 'col_3': 100},
                    {'col_0': 6, 'col_1': 7, 'col_2': 8}]))


    

    




   col_0  col_1  col_2  col_3
0      0    1.0      2    NaN
1      3    NaN      5  100.0
2      6    7.0      8    NaN

In [18]:

    

print(pd.DataFrame({'col_0': {'row_0': 0, 'row_1': 1, 'row_2': 2},
                    'col_1': {'row_0': 3, 'row_2': 4, 'row_3': 5},
                    'col_2': {'row_0': 6, 'row_1': 7, 'row_2': 8}}))


    

    




       col_0  col_1  col_2
row_0    0.0    3.0    6.0
row_1    1.0    NaN    7.0
row_2    2.0    4.0    8.0
row_3    NaN    5.0    NaN