Pandas: Create DataFrames that contains random values, contains missing values, contains datetime values and contains mixed values

Pandas: DataFrame Exercise-73 with Solution

Write a Pandas program to create DataFrames that contains random values, contains missing values, contains datetime values and contains mixed values.

Sample Solution :

Python Code :

import pandas as pd
print("DataFrame: Contains random values:")
df1 = pd.util.testing.makeDataFrame() # contains random values
print(df1)
print("\nDataFrame: Contains missing values:")
df2 = pd.util.testing.makeMissingDataframe() # contains missing values
print(df2)
print("\nDataFrame: Contains datetime values:")
df3 = pd.util.testing.makeTimeDataFrame() # contains datetime values
print(df3)
print("\nDataFrame: Contains mixed values:")
df4 = pd.util.testing.makeMixedDataFrame() # contains mixed values
print(df4)

Sample Output:

DataFrame: Contains random values:
                   A         B         C         D
Dog2w4Dv4l  0.591058  1.883454 -1.608613 -0.502516
kV7mfdFcF9  0.629642 -0.474377  0.567357  1.658445
Il2etcpmi6 -0.650443 -1.135115 -0.125597  1.786536
JdSXf3MEyq -1.679493  0.628239 -0.749637  0.852839
2H7lGkxiwL  1.186363 -0.615328  0.080556 -1.955239
jR009ZtfA4 -0.620729  0.844086 -0.143764  0.472620
baAWDkptTk  0.159193 -0.506624 -0.940083 -1.139910
8z1f7y6yzu  0.043180 -0.267833  0.431444  0.874783
P9ZUxqpuJA -0.939453 -1.922785 -0.527641 -0.308326
T4N91lVewM -0.013433 -0.252278  0.774136 -1.824968
7McfxCARW0  1.015361 -0.597383 -1.017453 -1.020482
8I59Iy2tV7  2.429052  0.441168 -0.215161 -0.333973
jHxyr4Htsh -0.344973  0.070246  1.134062 -0.016310
lyMSJjL3fE -0.383133  1.142060 -0.437973  0.372100
iAksZz4YPH -0.189774  1.399061  1.294249  1.220887
jcILDH1uSb  1.208005  0.031609  1.058339 -1.490341
uLXp1wu84s  0.289758  0.428422  0.356415  0.643879
Ie8ubHzNbh  1.699736 -0.018321 -0.670926  1.145490
n4TmM5kPCA  0.122721 -0.890217 -0.980098 -0.338159
CtdL5x1ofR  1.375652 -1.148859 -0.198355 -2.045092
WqggnU8U1w  0.171769  1.276065  0.474320  0.126961
UOCLGy3MJI -0.508391 -0.755753  0.239499  0.484506
wZYF0HwbEY -1.061641 -0.923209  0.394357 -0.843273
JP6QFva9u9 -0.022757  1.238850 -0.607959  1.645612
r02ts3PRSV  0.050639 -1.016244  0.330882 -1.161764
I8lMHDtdEa -0.848674  0.207307 -0.021109  1.421939
rg1rThlQ4o -0.670269  0.853271 -0.384838  0.350151
4P5Xq4rxcL  1.041481 -2.341787 -1.157728  0.497949
Oy6e83TXcQ -1.259630  0.433061  0.893792 -1.427895
C7Zz3C0Jq5 -0.802454  1.001237 -2.233028  0.061644

DataFrame: Contains missing values:
                   A         B         C         D
i6i6Xn9l9c -0.299335  0.410871 -0.431840 -0.302177
OGo5KNNYNJ -0.174594 -1.366146  0.435063 -2.779446
u0mG9q1L7C  1.019094 -0.061077 -1.138138 -0.218460
RNJGqpci4o -0.380815  0.189970 -2.148521 -1.163589
vXIcxItZ1D       NaN -0.079448  0.604777  0.065290
arou6zhX6q       NaN       NaN -0.827082 -0.377132
BkcUNAyKII  0.196885  0.164628 -0.872416  0.578590
Nar3sV5Z01 -0.269490       NaN -1.914949 -2.492530
Sa6BpjQpms -0.035106 -0.531400  0.328387  0.463325
eLlmKur2R2       NaN  1.252522  0.384160 -0.292494
4ZGLI9N5GI  1.103449  0.140680  0.101512 -0.117461
8JpVrcZRCz       NaN -1.228597 -0.889428  1.019362
3ww3qKh37f  1.678527  0.011843  0.405760  1.158411
QlGQoxSVT6  0.763349  1.743806 -1.564245 -1.198915
wrvoGhUQAd  1.045789  0.432039  0.593760  0.635557
oKApKm6NcZ       NaN  0.561950 -1.064052 -2.380983
Ka87bUAT3j -1.243862  0.681610 -0.018944 -1.127184
O7zz89V5e0  0.132516  0.506075 -1.001728 -1.369704
EE4Z8p7SzC  0.274650 -0.552164 -0.478510 -1.182832
wWxAn2q4RD -0.829835       NaN  0.496359       NaN
vzFsnyObuX -1.602297 -2.086616  1.329253  1.463064
QtVb9b3gDQ  0.153850  0.799016  1.701532 -0.141876
Vf6t2LO2Io  0.936485 -0.835217       NaN -0.560338
ZEXVM5SUdU  1.733719  0.086513  0.562900  0.352225
5AvgYYFP05 -0.904654  0.401132 -0.478490  1.390773
EngKTbWqSQ -2.172282 -0.749352 -1.243691  0.217420
rgsi1atINq -1.548443  0.676526 -1.315938  1.314064
zL9042RbHi       NaN       NaN -0.085687  0.303308
uz3laJaCIw -1.390233 -0.822796 -0.132600 -1.138293
f7myQshpvh  0.027210 -0.173178 -0.108948  0.738018

DataFrame: Contains datetime values:
                   A         B         C         D
2000-01-03  0.665402  0.860808 -0.180986 -0.970889
2000-01-04 -1.511533  0.487539 -0.710355 -0.807816
2000-01-05 -0.773294  0.197918 -1.214035  1.049529
2000-01-06 -1.074894  1.774147 -0.620025  0.740779
2000-01-07 -0.714355  0.330679  0.497667 -0.375923
2000-01-10 -0.060936  0.677847  0.686886  1.351782
2000-01-11 -1.692036 -0.470830 -0.249142  0.541105
2000-01-12 -0.077213 -0.592206 -0.132603 -0.656798
2000-01-13 -2.407360 -0.709951 -0.620317 -0.593090
2000-01-14 -0.243385 -1.654542  0.487391  0.595058
2000-01-17  0.139514  0.583979  0.211791 -1.809909
2000-01-18 -1.185097  2.688730  1.105632  0.322994
2000-01-19 -0.647685 -0.380803  0.056086 -1.299670
2000-01-20  0.781133  1.074446 -1.145552 -0.648223
2000-01-21 -0.428875  0.402555  1.735354 -1.230331
2000-01-24  1.282698  1.506384 -2.726718  0.480689
2000-01-25 -0.059287 -0.952011  0.066330  0.897042
2000-01-26 -1.503653 -1.689130 -0.488598 -0.890888
2000-01-27 -0.464802  0.250585 -1.462912  1.789611
2000-01-28 -1.213504  0.304826 -0.190335 -0.693164
2000-01-31 -0.565728 -1.317228 -1.707892 -0.404228
2000-02-01  0.160620  1.689041  0.171084 -0.004823
2000-02-02 -1.251242  2.242914 -0.430506 -0.042091
2000-02-03 -1.721439 -0.159966  1.523550 -0.742485
2000-02-04  0.002191  0.708701  0.029411  0.319738
2000-02-07  0.541060  0.905438  0.452724 -0.849368
2000-02-08  0.335644  1.776628  0.173110 -0.847064
2000-02-09  1.139137 -0.850207  0.718282  0.903825
2000-02-10  0.079852 -1.303238  1.400994 -0.560761
2000-02-11  1.496111  0.143146  0.509362  1.206039

DataFrame: Contains mixed values:
     A    B     C          D
0  0.0  0.0  foo1 2009-01-01
1  1.0  1.0  foo2 2009-01-02
2  2.0  0.0  foo3 2009-01-05
3  3.0  1.0  foo4 2009-01-06
4  4.0  0.0  foo5 2009-01-07

Python-Pandas Code Editor:

Have another way to solve this solution? Contribute your code (and comments) through Disqus.

Previous: Write a Pandas program to combine many given series to create a DataFrame.
Next: Write a Pandas program to fill missing values in time series data.