data-graph-explorer/Data Graph Explorer.md

import matplotlib.pyplot as plt
import numpy as np
from ipywidgets import FileUpload
import pandas as pd
import io
import json

Upload File

uploaded = FileUpload(accept=".csv")
display(uploaded)

FileUpload(value=(), accept='.csv', description='Upload')

for f in uploaded.value:
    content = f["content"]
    df = pd.read_csv(io.BytesIO(content))
    break
df

	Year	CSIRO Adjusted Sea Level	Lower Error Bound	Upper Error Bound	NOAA Adjusted Sea Level
0	1880	0.000000	-0.952756	0.952756	NaN
1	1881	0.220472	-0.732283	1.173228	NaN
2	1882	-0.440945	-1.346457	0.464567	NaN
3	1883	-0.232283	-1.129921	0.665354	NaN
4	1884	0.590551	-0.283465	1.464567	NaN
...	...	...	...	...	...
129	2009	8.586614	8.311024	8.862205	8.046354
130	2010	8.901575	8.618110	9.185039	8.122973
131	2011	8.964567	8.661417	9.267717	8.053065
132	2012	9.326772	8.992126	9.661417	8.457058
133	2013	8.980315	8.622047	9.338583	8.546648

134 rows × 5 columns

Provide CSV file URL as interactive input

url = input("Provide direct url of CSV file: ")
df = pd.read_csv(url)
df
# https://github.com/jasperdebie/VisInfo/raw/master/us-state-capitals.csv

Provide direct url of CSV file:  https://github.com/jasperdebie/VisInfo/raw/master/us-state-capitals.csv

	name	description	latitude	longitude
0	Alabama	Montgomery	32.377716	-86.300568
1	Alaska	Juneau	58.301598	-134.420212
2	Arizona	Phoenix	33.448143	-112.096962
3	Arkansas	Little Rock	34.746613	-92.288986
4	California	Sacramento	38.576668	-121.493629
5	Colorado	Denver	39.739227	-104.984856
6	Connecticut	Hartford<br>	41.764046	-72.682198
7	Delaware	Dover	39.157307	-75.519722
8	Hawaii	Honolulu	21.307442	-157.857376
9	Florida	Tallahassee	30.438118	-84.281296
10	Georgia	Atlanta<br>	33.749027	-84.388229
11	Idaho	Boise	43.617775	-116.199722
12	Illinois	Springfield	39.798363	-89.654961
13	Indiana	Indianapolis	39.768623	-86.162643
14	Iowa	Des Moines	41.591087	-93.603729
15	Kansas	Topeka	39.048191	-95.677956
16	Kentucky	Frankfort	38.186722	-84.875374
17	Louisiana	Baton Rouge	30.457069	-91.187393
18	Maine	Augusta	44.307167	-69.781693
19	Maryland	Annapolis	38.978764	-76.490936
20	Massachusetts	Boston	42.358162	-71.063698
21	Michigan	Lansing	42.733635	-84.555328
22	Minnesota	St. Paul	44.955097	-93.102211
23	Mississippi	Jackson	32.303848	-90.182106
24	Missouri	Jefferson City	38.579201	-92.172935
25	Montana	Helena	46.585709	-112.018417
26	Nebraska	Lincoln	40.808075	-96.699654
27	Nevada	Carson City	39.163914	-119.766121
28	New Hampshire	Concord	43.206898	-71.537994
29	New Jersey	Trenton	40.220596	-74.769913
30	New Mexico	Santa Fe	35.682240	-105.939728
31	North Carolina	Raleigh	35.780430	-78.639099
32	North Dakota	Bismarck	46.820850	-100.783318
33	New York	Albany	42.652843	-73.757874
34	Ohio	Columbus	39.961346	-82.999069
35	Oklahoma	Oklahoma City	35.492207	-97.503342
36	Oregon	Salem	44.938461	-123.030403
37	Pennsylvania	Harrisburg	40.264378	-76.883598
38	Rhode Island	Providence	41.830914	-71.414963
39	South Carolina	Columbia	34.000343	-81.033211
40	South Dakota	Pierre	44.367031	-100.346405
41	Tennessee	Nashville	36.165810	-86.784241
42	Texas	Austin	30.274670	-97.740349
43	Utah	Salt Lake City	40.777477	-111.888237
44	Vermont	Montpelier	44.262436	-72.580536
45	Virginia	Richmond	37.538857	-77.433640
46	Washington	Olympia	47.035805	-122.905014
47	West Virginia	Charleston	38.336246	-81.612328
48	Wisconsin	Madison	43.074684	-89.384445
49	Wyoming	Cheyenne	41.140259	-104.820236

# source: https://public.opendatasoft.com/explore/dataset/us-state-boundaries/table/
with open("us-state-boundaries.csv") as f:
    sb = [line.split(';')[-2] for line in f.readlines()[1:]] # state boundaries
    sb = ['[['+b[b.find("[[[")+3: b.find("]]]")+1].strip('[').strip(']')+']]' for b in sb]

sb2 = []
for i in range(len(sb)):
    try:
        sb2.append(json.loads(sb[i]))
    except BaseException as e:
        print(i)

sb3 = []
for b in sb2:
    x = []
    y = []
    for xy in b:
        x.append(xy[0])
        y.append(xy[1])
    sb3.append([x, y])

print(sb3[0])

3
[[-64.843729999672, -64.8306170004155, -64.8166740000574, -64.8003289998509, -64.7850010003439, -64.7780049999629, -64.756923000007, -64.7438040000044, -64.7376500004207, -64.7298299996841, -64.727025000328, -64.7081970000824, -64.7016040001075, -64.6832930000708, -64.6752489999265, -64.6684810000495, -64.6404370000478, -64.6391440000621, -64.643689000191, -64.6377349997255, -64.6638549997298, -64.6613010004438, -64.6642589999609, -64.6782290000832, -64.6944889999008, -64.7079069996541, -64.720173999712, -64.7487359997476, -64.7577690001039, -64.7734199996511, -64.7828909996753, -64.8012889997506, -64.7992609999961, -64.8027730004303, -64.8068909998636, -64.8185169998639, -64.8333530001747, -64.849551999893, -64.8580350004284, -64.8725339995569, -64.8897519996245, -64.8997750000177, -64.9052399998634, -64.9061510002576, -64.921034000424, -64.9337140000354, -64.9420610001283, -64.9464720003629, -64.9609639998188, -64.9804139998553, -64.9978610002517, -65.0198210000917, -65.0315650000928, -65.0485809995957, -65.0501940001468, -65.0577479998463, -65.0677319998815, -65.0809529999913, -65.10083099965, -65.1172670000931, -65.1312199995987, -65.1426220001913, -65.1477229999889, -65.154117999597, -65.1536439999441, -65.1506989999476, -65.1448089999545, -65.137688999725, -65.1399409997855, -65.1351379999137, -65.1303469997376, -65.1174269999267, -65.1130569997, -65.1137599996818, -65.112214999801, -65.1004130000736, -65.091790999621, -65.0805670002019, -65.0606650002537, -65.0415220003638, -65.0172989996944, -65.007769000119, -64.9995900002555, -64.9823889999064, -64.9691820000403, -64.950455000077, -64.9426920001401, -64.9276419995691, -64.907594999856, -64.8950240000245, -64.8910329999146, -64.8739119999314, -64.8649759996594, -64.8599500002049, -64.843729999672], [18.3937130003038, 18.3952020001978, 18.4029059996947, 18.4075949997621, 18.4038019999373, 18.386209000173, 18.3766859999062, 18.3781310002826, 18.3762170003899, 18.3779449996133, 18.3747250001871, 18.373742000052, 18.3706359999168, 18.3725669997141, 18.3674629998913, 18.3655769999245, 18.363997000316, 18.3551899997673, 18.3441069997742, 18.3197589996555, 18.2853760001861, 18.2673589997501, 18.2612709996959, 18.2523160002306, 18.2487579998992, 18.2487249997681, 18.2506900002989, 18.2630590000803, 18.2608550002774, 18.2605059997401, 18.2526930002856, 18.2451059996837, 18.2293219998289, 18.2133720003843, 18.2051629999707, 18.1920609998845, 18.1834559998114, 18.1796829999945, 18.1795870003758, 18.1834839999737, 18.1942359996752, 18.2065249995874, 18.2185139997731, 18.2241929999785, 18.2288429997134, 18.2379699999478, 18.2480099999376, 18.2572230000658, 18.2560599998827, 18.2569120001884, 18.252492999917, 18.2523549996564, 18.2562770001029, 18.2667770001428, 18.2691289998076, 18.2541629999811, 18.2439080002976, 18.2364819999318, 18.2326290001953, 18.23477199986, 18.2417569995878, 18.2507890004071, 18.2590850003178, 18.2774250002881, 18.2931389997995, 18.302844999769, 18.3128859999509, 18.3205580003919, 18.3362909997264, 18.3508549999627, 18.3615369997632, 18.3802330002905, 18.3957579996876, 18.4068750001337, 18.418827, 18.4395440001859, 18.4471019997825, 18.4528070003069, 18.4597469996518, 18.4591020001661, 18.4493229999302, 18.4407029996101, 18.4455259999609, 18.4507079999187, 18.4509469999009, 18.4462940001965, 18.4541570002839, 18.4619490000909, 18.4649839999656, 18.4633640003554, 18.4621600003593, 18.4062549996746, 18.3959270002777, 18.3942250002094, 18.3937130003038]]

fig, axis = plt.subplots()
x_col = "longitude"
y_col = "latitude"
xlo = df[x_col].min() - 5
xhi = df[x_col].max() + 5
ylo = df[y_col].min() - 5
yhi = df[y_col].max() + 5

x = df[x_col].to_numpy()
y = df[y_col].to_numpy()

plt.axis([xlo, xhi, ylo, yhi])
plt.plot(x, y, "ro")
for b in sb3:
    plt.plot(b[0], b[1], "b")
plt.title("USA states and their capital location")
plt.show()

Edit CSV file URL variable

url = "https://data.gov.au/data/dataset/7169894e-b518-4933-a900-f79ebc4ec6a3/resource/5d9edabe-b6af-4975-9340-88f55b872a00/download/soe2016antarctic5-year-smoothed-annual-max-min-temperature-by-stationaad.csv"
df = pd.read_csv(url)
df

	Year	Macquarie Island MAX	Macquarie Island MIN	Mawson MAX	Mawson MIN	Casey MAX	Casey MIN	Davis MAX	Davis MIN
0	1948	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	1949	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	1950	6.050	2.625	NaN	NaN	NaN	NaN	NaN	NaN
3	1951	6.100	2.660	NaN	NaN	NaN	NaN	NaN	NaN
4	1952	6.140	2.660	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...
63	2011	6.740	3.200	-8.04	-14.28	-5.86	-12.50	-7.00	-12.98
64	2012	6.840	3.260	-8.24	-14.40	-6.28	-12.96	-7.10	-13.08
65	2013	6.825	3.225	-8.10	-14.30	-6.30	-12.90	-7.05	-13.05
66	2014	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
67	2015	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

68 rows × 9 columns

df.head(2)

	Year	Macquarie Island MAX	Macquarie Island MIN	Mawson MAX	Mawson MIN	Casey MAX	Casey MIN	Davis MAX	Davis MIN
0	1948	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	1949	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

col_names = df.columns.values
print(col_names)

['Year' 'Macquarie Island MAX' 'Macquarie Island MIN' 'Mawson MAX'
 'Mawson MIN' 'Casey MAX' 'Casey MIN' 'Davis MAX' 'Davis MIN']

[print(i, col_names[i]) for i in range(len(col_names))]
x_col = int(input("Input index number of column to be used for x-axis: "))
y_col = int(input("Input index number of column to be used for y-axis: "))
y_col2 = input("Input index number of column 2 (optional) to be used for y-axis: ")
y_col2 = int(y_col2) if y_col2 else None
print(f"x-axis: {col_names[x_col]}, y-axis: {col_names[y_col]}, y-axis (second): {y_col2}")

0 Year
1 Macquarie Island MAX
2 Macquarie Island MIN
3 Mawson MAX
4 Mawson MIN
5 Casey MAX
6 Casey MIN
7 Davis MAX
8 Davis MIN


Input index number of column to be used for x-axis:  0
Input index number of column to be used for y-axis:  2
Input index number of column 2 (optional) to be used for y-axis:  1


x-axis: Year, y-axis: Macquarie Island MIN, y-axis (second): 1

fig, axis = plt.subplots()
xlo = df[col_names[x_col]].min() - 5
xhi = df[col_names[x_col]].max() + 5
ylo = df[col_names[y_col]].min() - 5
yhi = df[col_names[y_col]].max() + 5
if y_col2:
    ylo = min(ylo, df[col_names[y_col2]].min() - 5)
    yhi = max(yhi, df[col_names[y_col]].max() + 5)

x = df[col_names[x_col]].to_numpy()
y = df[col_names[y_col]].to_numpy()
if y_col2:
    y2 = df[col_names[y_col2]].to_numpy()

plt.axis([xlo, xhi, ylo, yhi])
# plt.plot(x, y, "bo")
plt.plot(x, y, "b")
if y_col2:
    # plt.plot(x, y2, "ro")
    plt.plot(x, y2, "r")
plt.show()