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.
What is the difficulty level of this exercise?
Test your Programming skills with w3resource's quiz.
It will be nice if you may share this link in any developer community or anywhere else, from where other developers may find this content. Thanks.
https://198.211.115.131/python-exercises/pandas/python-pandas-data-frame-exercise-73.php
- Weekly Trends and Language Statistics
- Weekly Trends and Language Statistics