Skip to content

Instantly share code, notes, and snippets.

@ryanorsinger
Forked from zgulde/readme.md
Created October 9, 2020 14:44
Show Gist options
  • Save ryanorsinger/28eb225d4b785539876d1498cbc840f2 to your computer and use it in GitHub Desktop.
Save ryanorsinger/28eb225d4b785539876d1498cbc840f2 to your computer and use it in GitHub Desktop.

Visualization Warmup

  1. Copy the following code into a jupyter notebook named visualization_warmup.ipynb

    import matplotlib as mpl
    import matplotlib.pyplot as plt
    import pandas as pd
    import numpy as np
    
    url = 'https://gist.githubusercontent.com/zgulde/018f5d601efc79cb741a7aba92f51d29/raw/c08f1c09f605b43e9a354890f9dd68f296e76dc4/students.csv'
    df = pd.read_csv(url)
  2. df contains (fake) data on students time at codeup:

    • ml_coffee: coffee consumption in milliliters
    • syntax_errors: number of syntax errors
    • p_days_absent: percentage of days absent
  3. Create a scatter plot of syntax_errors and ml_coffee. Make sure to give your visual an appropriate title and labels.

  4. Create a scatter plot of p_days_absent and ml_coffee. Make sure to give your visual an appropriate title and labels.

  5. Perform any other customizations you think will help to the plots you have created

ml_coffee syntax_errors p_days_absent
16225 50713056027.687126 0.038820992224484055
18230 51998229252.91729 0.022748060988232423
15877 50726302962.03755 0.036627492987395247
8263 49079192187.08071 0.04129343103306338
13935 51451136177.88983 0.08154363460383048
596 48971103821.878105 0.006457799507104988
6164 49086478518.09223 0.0967924513056536
1442 48674645815.74119 0.07235402255176207
611 47990409388.900085 0.07862558202046124
3981 49371383834.28593 0.08971917467138506
18247 50759343908.2741 0.06427514113541638
3394 48871755239.162636 0.01309877782079682
3825 48781397251.132576 0.05954355439835769
15444 51289746838.90351 0.020681115314512544
17976 51091151126.58394 0.08207870356893311
8485 49285982322.17809 0.06357902535017997
18731 51567583126.858696 0.07502691191165392
13239 51014032129.78551 0.037817162724330715
6984 49787082102.16054 0.05898127673298323
16923 51381300689.62511 0.09841704462230759
18230 51289421084.517204 0.015914454676227832
7148 49827052440.54124 0.014557137872752217
18043 51334527306.73874 0.05354579171977928
4695 48695928758.6867 0.0049120963881532104
2928 49699414003.21666 0.03606200506834234
8528 49930121824.28635 0.049126849511167005
3752 49017077224.034546 0.04470232692534938
18266 51293826424.10715 0.040796420556827355
12394 49835213548.77343 0.09750841010168625
2838 49173021686.93753 0.06338466312853337
17472 50358971263.559204 0.06645925283927805
10396 50706297017.98254 0.014064820544340696
12894 50040639938.23156 0.09415257810502815
17721 50871847976.48518 0.05215034220384256
3124 49385427546.08377 0.009266727355454698
1942 48691239075.71138 0.02181738358310648
9518 48960113620.8529 0.06252130505115192
10110 50043396394.18915 0.05752010048195476
14529 51088048874.320625 0.037625098942747175
16131 51138186870.71452 0.011960305093687462
10481 50422770974.08835 0.07158631969235683
18273 51196373516.38276 0.02978038648668023
11918 50034265326.79261 0.07252081964814776
11460 50457079356.16739 0.03661363757597188
8450 50106176081.192116 0.10767659263998891
8947 48453426330.89523 0.06984183934404936
3816 48205398580.03025 0.08549909113414617
17834 51101222260.89855 0.0820338569868742
18037 51829388397.1521 0.1135945075976035
9247 49600175266.8647 0.10958775067350787
3758 49044147571.881714 0.00615058914012113
10073 50321989454.11825 0.04563785797427472
9387 49789811317.38642 0.06766278504702442
7078 48848184255.90325 0.015908848854716762
10696 49018183388.01361 0.06901623431529895
8492 49677081552.71469 0.038708129716526186
1829 47871778084.20879 0.11807130763421582
16640 51365986777.170074 0.0690798982076166
5126 49139719225.093834 0.07556731608564567
10229 50348868946.43616 0.04812147316435777
14887 50271882988.09999 0.08935560758224258
5536 49091536944.09054 0.048142311002859546
9034 49462898647.77529 0.09185701489979878
15351 50094448414.10896 0.11789174827848595
19593 51213336761.047356 0.020174012065427873
3021 48936665921.04527 0.056951362039460975
10205 50231658088.87221 0.06587285810479501
5974 49280288759.46638 0.05233288075907749
6527 49291396177.27339 0.035607755749270754
12023 49946663224.27521 0.047227648818072614
9938 49708802501.39021 0.10056973536693464
17146 51283858691.92124 0.07819710732947464
15101 50749279805.12193 0.11446763198997797
17995 51761741552.02144 0.05724002728804568
9730 48880562608.58952 0.10546722565224338
14810 49454588782.8967 0.09506493393570067
15303 51131509160.92364 0.03184290287290168
1722 48366886004.19529 0.091492592875674
6784 49526488281.99569 0.09872836742713598
938 48700663232.10746 0.04235908444986617
3087 49647764168.59918 0.11690783717913626
11546 49645757809.69184 0.08102800268490216
8069 48904113995.72108 0.08392555775280981
12721 50161383219.12272 0.055151630875256936
18786 51835582558.73082 0.008368190063933415
19125 51100760319.34904 0.11975762123468131
3363 48871121723.43714 0.07653011515681403
19425 52062527261.65255 0.008178309958626184
12804 51218602064.56892 0.01753644447677404
2169 48374549792.4242 0.10066959285421988
9825 50510839233.55437 0.03326338918482584
5706 48823065625.27779 0.06473411654339822
4642 49018582241.27943 0.009651932723287494
8963 49482422799.75619 0.06796160824730002
15132 50861120817.78616 0.04349818935289836
12065 50949167254.998886 0.038185636885575525
10135 49851288828.19464 0.0784227913888481
4497 49140740714.48489 0.04258836019631617
17863 51480341932.29813 0.08583081683644939
12625 50383605519.823204 0.075393778883335
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment