# pandas Create a sample DataFrame using Numpy

## Example

Create a `DataFrame` of random numbers:

``````import numpy as np
import pandas as pd

# Set the seed for a reproducible sample
np.random.seed(0)

df = pd.DataFrame(np.random.randn(5, 3), columns=list('ABC'))

print(df)
# Output:
#           A         B         C
# 0  1.764052  0.400157  0.978738
# 1  2.240893  1.867558 -0.977278
# 2  0.950088 -0.151357 -0.103219
# 3  0.410599  0.144044  1.454274
# 4  0.761038  0.121675  0.443863
``````

Create a `DataFrame` with integers:

``````df = pd.DataFrame(np.arange(15).reshape(5,3),columns=list('ABC'))

print(df)
# Output:
#     A   B   C
# 0   0   1   2
# 1   3   4   5
# 2   6   7   8
# 3   9  10  11
# 4  12  13  14
``````

Create a `DataFrame` and include nans (`NaT, NaN, 'nan', None`) across columns and rows:

``````df = pd.DataFrame(np.arange(48).reshape(8,6),columns=list('ABCDEF'))

print(df)
# Output:
#     A   B   C   D   E   F
# 0   0   1   2   3   4   5
# 1   6   7   8   9  10  11
# 2  12  13  14  15  16  17
# 3  18  19  20  21  22  23
# 4  24  25  26  27  28  29
# 5  30  31  32  33  34  35
# 6  36  37  38  39  40  41
# 7  42  43  44  45  46  47

df.ix[::2,0] = np.nan # in column 0, set elements with indices 0,2,4, ... to NaN
df.ix[::4,1] = pd.NaT # in column 1, set elements with indices 0,4, ... to np.NaT
df.ix[:3,2] = 'nan'   # in column 2, set elements with index from 0 to 3 to 'nan'
df.ix[:,5] = None     # in column 5, set all elements to None
df.ix[5,:] = None     # in row 5, set all elements to None
df.ix[7,:] = np.nan   # in row 7, set all elements to NaN

print(df)
# Output:
#     A     B     C   D   E     F
# 0 NaN   NaT   nan   3   4  None
# 1   6     7   nan   9  10  None
# 2 NaN    13   nan  15  16  None
# 3  18    19   nan  21  22  None
# 4 NaN   NaT    26  27  28  None
# 5 NaN  None  None NaN NaN  None
# 6 NaN    37    38  39  40  None
# 7 NaN   NaN   NaN NaN NaN   NaN
``````