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:
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