Lecture 20 exercise - section 2#
Live-coded solution.
Electricity data#
import pandas as pd
electricity = pd.read_csv("data/P_Data_Extract_From_World_Development_Indicators/0a6eb0d7-9c99-4d18-bf17-7839ff3ad1ed_Data.csv")
electricity
| Series Name | Series Code | Country Name | Country Code | 1990 [YR1990] | 2000 [YR2000] | 2014 [YR2014] | 2015 [YR2015] | 2016 [YR2016] | 2017 [YR2017] | 2018 [YR2018] | 2019 [YR2019] | 2020 [YR2020] | 2021 [YR2021] | 2022 [YR2022] | 2023 [YR2023] | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Afghanistan | AFG | .. | 4.4 | 89.5 | 71.5 | 97.7 | 97.7 | 93.4 | 97.7 | 97.7 | 97.7 | 85.3 | .. |
| 1 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Albania | ALB | 100 | 99.4 | 100 | 100 | 99.9 | 99.9 | 100 | 100 | 100 | 100 | 100 | .. |
| 2 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Algeria | DZA | .. | 98.6 | 99.3 | 99.4 | 99.4 | 99.5 | 99.6 | 99.5 | 99.7 | 99.8 | 100 | .. |
| 3 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | American Samoa | ASM | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. |
| 4 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Andorra | AND | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | .. |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 266 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 267 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 268 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 269 | Data from database: World Development Indicators | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 270 | Last Updated: 10/24/2024 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
271 rows × 16 columns
pd.melt(electricity, id_vars=["Series Name", "Series Code", "Country Name", "Country Code"])
| Series Name | Series Code | Country Name | Country Code | variable | value | |
|---|---|---|---|---|---|---|
| 0 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Afghanistan | AFG | 1990 [YR1990] | .. |
| 1 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Albania | ALB | 1990 [YR1990] | 100 |
| 2 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Algeria | DZA | 1990 [YR1990] | .. |
| 3 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | American Samoa | ASM | 1990 [YR1990] | .. |
| 4 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Andorra | AND | 1990 [YR1990] | 100 |
| ... | ... | ... | ... | ... | ... | ... |
| 3247 | NaN | NaN | NaN | NaN | 2023 [YR2023] | NaN |
| 3248 | NaN | NaN | NaN | NaN | 2023 [YR2023] | NaN |
| 3249 | NaN | NaN | NaN | NaN | 2023 [YR2023] | NaN |
| 3250 | Data from database: World Development Indicators | NaN | NaN | NaN | 2023 [YR2023] | NaN |
| 3251 | Last Updated: 10/24/2024 | NaN | NaN | NaN | 2023 [YR2023] | NaN |
3252 rows × 6 columns
electricity_by_year = pd.melt(
electricity,
id_vars=["Series Name", "Series Code", "Country Name", "Country Code"],
var_name="Year",
value_name="Access to elec.",
)
electricity_by_year
| Series Name | Series Code | Country Name | Country Code | Year | Access to elec. | |
|---|---|---|---|---|---|---|
| 0 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Afghanistan | AFG | 1990 [YR1990] | .. |
| 1 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Albania | ALB | 1990 [YR1990] | 100 |
| 2 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Algeria | DZA | 1990 [YR1990] | .. |
| 3 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | American Samoa | ASM | 1990 [YR1990] | .. |
| 4 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Andorra | AND | 1990 [YR1990] | 100 |
| ... | ... | ... | ... | ... | ... | ... |
| 3247 | NaN | NaN | NaN | NaN | 2023 [YR2023] | NaN |
| 3248 | NaN | NaN | NaN | NaN | 2023 [YR2023] | NaN |
| 3249 | NaN | NaN | NaN | NaN | 2023 [YR2023] | NaN |
| 3250 | Data from database: World Development Indicators | NaN | NaN | NaN | 2023 [YR2023] | NaN |
| 3251 | Last Updated: 10/24/2024 | NaN | NaN | NaN | 2023 [YR2023] | NaN |
3252 rows × 6 columns
Plot#
import plotly.express as px
px.line(
electricity_by_year,
x="Year",
y="Access to elec.",
color="Country Name",
title="Access to electricity for each country by year",
)
electricity_by_year.dtypes
Series Name object
Series Code object
Country Name object
Country Code object
Year object
Access to elec. object
dtype: object
electricity_by_year["Access to elec."].astype(float)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[6], line 1
----> 1 electricity_by_year["Access to elec."].astype(float)
File /opt/homebrew/Caskroom/mambaforge/base/envs/computing-in-context/lib/python3.12/site-packages/pandas/core/generic.py:6643, in NDFrame.astype(self, dtype, copy, errors)
6637 results = [
6638 ser.astype(dtype, copy=copy, errors=errors) for _, ser in self.items()
6639 ]
6641 else:
6642 # else, only a single dtype is given
-> 6643 new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)
6644 res = self._constructor_from_mgr(new_data, axes=new_data.axes)
6645 return res.__finalize__(self, method="astype")
File /opt/homebrew/Caskroom/mambaforge/base/envs/computing-in-context/lib/python3.12/site-packages/pandas/core/internals/managers.py:430, in BaseBlockManager.astype(self, dtype, copy, errors)
427 elif using_copy_on_write():
428 copy = False
--> 430 return self.apply(
431 "astype",
432 dtype=dtype,
433 copy=copy,
434 errors=errors,
435 using_cow=using_copy_on_write(),
436 )
File /opt/homebrew/Caskroom/mambaforge/base/envs/computing-in-context/lib/python3.12/site-packages/pandas/core/internals/managers.py:363, in BaseBlockManager.apply(self, f, align_keys, **kwargs)
361 applied = b.apply(f, **kwargs)
362 else:
--> 363 applied = getattr(b, f)(**kwargs)
364 result_blocks = extend_blocks(applied, result_blocks)
366 out = type(self).from_blocks(result_blocks, self.axes)
File /opt/homebrew/Caskroom/mambaforge/base/envs/computing-in-context/lib/python3.12/site-packages/pandas/core/internals/blocks.py:758, in Block.astype(self, dtype, copy, errors, using_cow, squeeze)
755 raise ValueError("Can not squeeze with more than one column.")
756 values = values[0, :] # type: ignore[call-overload]
--> 758 new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)
760 new_values = maybe_coerce_values(new_values)
762 refs = None
File /opt/homebrew/Caskroom/mambaforge/base/envs/computing-in-context/lib/python3.12/site-packages/pandas/core/dtypes/astype.py:237, in astype_array_safe(values, dtype, copy, errors)
234 dtype = dtype.numpy_dtype
236 try:
--> 237 new_values = astype_array(values, dtype, copy=copy)
238 except (ValueError, TypeError):
239 # e.g. _astype_nansafe can fail on object-dtype of strings
240 # trying to convert to float
241 if errors == "ignore":
File /opt/homebrew/Caskroom/mambaforge/base/envs/computing-in-context/lib/python3.12/site-packages/pandas/core/dtypes/astype.py:182, in astype_array(values, dtype, copy)
179 values = values.astype(dtype, copy=copy)
181 else:
--> 182 values = _astype_nansafe(values, dtype, copy=copy)
184 # in pandas we don't store numpy str dtypes, so convert to object
185 if isinstance(dtype, np.dtype) and issubclass(values.dtype.type, str):
File /opt/homebrew/Caskroom/mambaforge/base/envs/computing-in-context/lib/python3.12/site-packages/pandas/core/dtypes/astype.py:133, in _astype_nansafe(arr, dtype, copy, skipna)
129 raise ValueError(msg)
131 if copy or arr.dtype == object or dtype == object:
132 # Explicit copy, or required since NumPy can't view from / to object.
--> 133 return arr.astype(dtype, copy=True)
135 return arr.astype(dtype, copy=copy)
ValueError: could not convert string to float: '..'
list(electricity_by_year["Access to elec."].unique())
['..',
'100',
'95.7630462646484',
'92.1548004150391',
'99.9413986206055',
'99.9842529296875',
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'99.846923828125',
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'89.9',
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'70.334586',
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'32.8',
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nan,
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'81', '18.7', '47', '67.1', '66.5', '44.8', '80.4', '92.3', '45',
'28.4', '90.9', '44.4', '61.2', '46.3', '67', '50.9', '95.3',
'43.6', '85.2', '56.5', '38', '71', '26.1', '65.8', '50.7', '84.7',
'6.3', '55.3', '50', '61.6', '62', '40.2', '45.4',
'43.0171479832647', '51.2110549404709', '89.1692812379426',
'95.804767395244', '99.8729516393806', '92.0349140485874',
'97.6650202588857', '97.4161347769004', '98.0496113702534',
'99.9704727607712', '99.942320081113', '99.940819336018',
'99.9708561296605', '54.5831226685803', '44.0132817192788',
'99.9631057051384', '97.7587635009644', '88.2960951797823',
'73.3946899985265', '55.3160844961813', '61.3394550392999',
'99.2872327792157', '97.7603349993766', '97.5791568486266',
'97.7119543642317', '51.1674138439625', '87.6492919680925',
'40.4843611286372', '86.9830368829659', '96.1467646904579',
'95.5104255149385', '95.4583357981029', '92.9486959090124',
'99.8985605663644', '91.5254892263974', '82.9591610078136',
'99.981741114911', '47.0762627878568', '91.0865817462377',
'94.6179244479771', '46.3373545996798', '46.3326008959214',
'99.152882923018', '89.8908858380532', '45.6', '70.1', '9.8', '84',
'63.2', '14.3', '83.3', '47.7', '64.7', '66.6', '50.3', '77',
'96.1', '90.3', '83.5', '42.2', '30.9', '91.1', '69.7', '89.3',
'23.1', '68.5', '31', '11.2', '47.9', '80.1', '29.7', '53.3',
'55.4', '23.6', '46.6', '75.1', '70.4', '22.7', '85', '6.8',
'57.6', '89.2', '41.3', '43', '46.7', '44.3812594428681',
'51.1680826551065', '90.3581538695401', '96.2215700753372',
'99.9455010885556', '92.834365738276', '97.8779427927934',
'97.6524316332846', '98.2621398882088', '99.9533142481858',
'99.9663713381538', '99.9065322711927', '99.9875256636779',
'55.5016312834383', '45.0522458898094', '99.9505379687558',
'97.8867643059813', '88.6615853993001', '74.1134591811169',
'57.0959550724622', '62.7544694323566', '99.3356536729549',
'98.0015266427987', '97.8532110437475', '97.9559417265741',
'52.9514648159524', '88.0490889564092', '41.836982681727',
'87.6841442253686', '97.1324839563419', '96.6517404120541',
'96.6125775683893', '93.3328680629391', '99.9400367116293',
'92.7013041706379', '84.5931602439996', '99.9929550285056',
'47.5782396795133', '92.2015549079081', '95.3232055481217',
'47.1303990695105', '47.1257966456512', '99.2510521973761',
'90.199100032655', '96.2', '97.2', '15.4', '69.9', '66.7', '85.4',
'33.4', '93.1', '96.5', '47.5', '27.6', '11.5', '50.6', '98.3',
'30.6', '94.5', '20.5', '96.4', '45.2', '26.3', '72.9', '49.9',
'7.3', '59.7', '39.9', '54.1', '73.9', '52.7', '46.2648745577187',
'51.7308990685072', '90.6384870529171', '97.0414890291467',
'99.9796417892388', '93.4926075631317', '97.8013659805564',
'97.5687165889393', '98.1511649991063', '99.9760236823625',
'99.9777773685799', '99.9520178888209', '99.9953534951789',
'56.346408400069', '46.3640414093557', '99.9686544956864',
'98.0667929887089', '89.0003692130539', '74.6422206071065',
'58.6069711033586', '63.9296623394879', '99.3926777630817',
'98.152759448219', '98.0060017525721', '98.1092788328358',
'54.562507330301', '88.4120504553677', '43.217947106415',
'88.4522403939564', '97.2315412836112', '96.7733482603745',
'96.7352691074454', '93.673300524618', '99.9332673681265',
'93.6071297305702', '86.4225510641771', '99.9971273501603',
'48.3855447902348', '93.2090070960632', '96.197039231936',
'48.478195544667', '48.4737889046716', '99.1899480241711',
'90.4854035957629', '48.2', '73.7', '19', '10.2', '65.4', '11.3',
'87.9', '20.8', '71.1', '66.8', '82.9', '63.7', '35.8', '47.2',
'52.6', '50.4', '29.8', '70.2', '14.2', '53.4', '83.6', '31.5',
'55.2', '18.6', '59.5', '20.9', '78.5', '68', '27.5', '76.3',
'49.3', '61.8', '42.7', '55.7', '74.9', '49', '48.100862134193',
'54.224724307904', '90.8493820461977', '97.3939355970029',
'99.9864361919036', '95.2851631707137', '98.1555450173615',
'97.9617970162443', '98.5234655577502', '99.9881579022417',
'99.9616380974146', '99.9763188018388', '99.9969239356276',
'57.8615299350207', '48.0509504079887', '99.9798902530907',
'99.1169517100785', '90.1059784759654', '76.6323166518695',
'60.1327212117', '65.6017359512866', '99.416690000063',
'98.2743453702088', '98.1365448192491', '98.2324938466415',
'56.2251693973526', '89.570092140798', '44.8163768347735',
'90.6238225668445', '97.2914835513583', '96.8500869295379',
'96.8125528853954', '94.8751847446865', '94.5025759531236',
'85.849707571326', '99.9987681484921', '50.5238061632824',
'93.8179009205047', '98.786423758525', '50.5800665756201',
'50.5759132657492', '99.4083794765038', '91.4203322426229', '48.5',
'19.5', '10.3', '11.7', '82.3', '55', '93.5', '37.4', '76', '36.1',
'14', '33.2', '78', '67.9', '48.9', '57.2', '47.1', '47.8', '50.1',
'48.7119945935567', '55.4375772541101', '91.0264045161148',
'97.5932932895718', '95.1851634429426', '98.2132410752903',
'98.026194403413', '98.563293539577', '99.9977236468108',
'99.9914644530224', '99.9954069607762', '57.8181418204066',
'48.6553933920956', '99.9798069474748', '98.9771817674784',
'90.0236427088539', '77.2202249370555', '60.6234216234335',
'66.1197318922923', '99.4825175455422', '98.5791344954591',
'98.4762455431909', '98.5451250779113', '56.8264012842674',
'89.5002576329068', '44.9483258927663', '90.6193347387435',
'97.3733015431867', '96.9464771200397', '96.9097460346813',
'94.8855561460311', '94.6027752416708', '85.8227472405466',
'51.1203418281979', '93.9107043828779', '98.2879959489889',
'51.4347389951457', '51.4299317538115', '99.4851190889072',
'91.3525709654628'], dtype=object)
electricity_by_year["Access to elec."] = electricity_by_year["Access to elec."].replace({"..": None}).astype(float)
px.line(
electricity_by_year,
x="Year",
y="Access to elec.",
color="Country Name",
title="Access to electricity for each country by year",
)
Filter#
electricity_by_year.head()
| Series Name | Series Code | Country Name | Country Code | Year | Access to elec. | |
|---|---|---|---|---|---|---|
| 0 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Afghanistan | AFG | 1990 [YR1990] | NaN |
| 1 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Albania | ALB | 1990 [YR1990] | 100.0 |
| 2 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Algeria | DZA | 1990 [YR1990] | NaN |
| 3 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | American Samoa | ASM | 1990 [YR1990] | NaN |
| 4 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Andorra | AND | 1990 [YR1990] | 100.0 |
first_country_names = electricity_by_year["Country Name"].head()
first_country_names
0 Afghanistan
1 Albania
2 Algeria
3 American Samoa
4 Andorra
Name: Country Name, dtype: object
is_first_country = electricity_by_year["Country Name"].isin(first_country_names)
is_first_country
0 True
1 True
2 True
3 True
4 True
...
3247 False
3248 False
3249 False
3250 False
3251 False
Name: Country Name, Length: 3252, dtype: bool
first_countries = electricity_by_year[is_first_country]
first_countries
| Series Name | Series Code | Country Name | Country Code | Year | Access to elec. | |
|---|---|---|---|---|---|---|
| 0 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Afghanistan | AFG | 1990 [YR1990] | NaN |
| 1 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Albania | ALB | 1990 [YR1990] | 100.0 |
| 2 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Algeria | DZA | 1990 [YR1990] | NaN |
| 3 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | American Samoa | ASM | 1990 [YR1990] | NaN |
| 4 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Andorra | AND | 1990 [YR1990] | 100.0 |
| 271 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Afghanistan | AFG | 2000 [YR2000] | 4.4 |
| 272 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Albania | ALB | 2000 [YR2000] | 99.4 |
| 273 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Algeria | DZA | 2000 [YR2000] | 98.6 |
| 274 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | American Samoa | ASM | 2000 [YR2000] | NaN |
| 275 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Andorra | AND | 2000 [YR2000] | 100.0 |
| 542 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Afghanistan | AFG | 2014 [YR2014] | 89.5 |
| 543 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Albania | ALB | 2014 [YR2014] | 100.0 |
| 544 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Algeria | DZA | 2014 [YR2014] | 99.3 |
| 545 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | American Samoa | ASM | 2014 [YR2014] | NaN |
| 546 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Andorra | AND | 2014 [YR2014] | 100.0 |
| 813 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Afghanistan | AFG | 2015 [YR2015] | 71.5 |
| 814 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Albania | ALB | 2015 [YR2015] | 100.0 |
| 815 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Algeria | DZA | 2015 [YR2015] | 99.4 |
| 816 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | American Samoa | ASM | 2015 [YR2015] | NaN |
| 817 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Andorra | AND | 2015 [YR2015] | 100.0 |
| 1084 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Afghanistan | AFG | 2016 [YR2016] | 97.7 |
| 1085 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Albania | ALB | 2016 [YR2016] | 99.9 |
| 1086 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Algeria | DZA | 2016 [YR2016] | 99.4 |
| 1087 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | American Samoa | ASM | 2016 [YR2016] | NaN |
| 1088 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Andorra | AND | 2016 [YR2016] | 100.0 |
| 1355 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Afghanistan | AFG | 2017 [YR2017] | 97.7 |
| 1356 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Albania | ALB | 2017 [YR2017] | 99.9 |
| 1357 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Algeria | DZA | 2017 [YR2017] | 99.5 |
| 1358 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | American Samoa | ASM | 2017 [YR2017] | NaN |
| 1359 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Andorra | AND | 2017 [YR2017] | 100.0 |
| 1626 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Afghanistan | AFG | 2018 [YR2018] | 93.4 |
| 1627 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Albania | ALB | 2018 [YR2018] | 100.0 |
| 1628 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Algeria | DZA | 2018 [YR2018] | 99.6 |
| 1629 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | American Samoa | ASM | 2018 [YR2018] | NaN |
| 1630 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Andorra | AND | 2018 [YR2018] | 100.0 |
| 1897 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Afghanistan | AFG | 2019 [YR2019] | 97.7 |
| 1898 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Albania | ALB | 2019 [YR2019] | 100.0 |
| 1899 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Algeria | DZA | 2019 [YR2019] | 99.5 |
| 1900 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | American Samoa | ASM | 2019 [YR2019] | NaN |
| 1901 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Andorra | AND | 2019 [YR2019] | 100.0 |
| 2168 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Afghanistan | AFG | 2020 [YR2020] | 97.7 |
| 2169 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Albania | ALB | 2020 [YR2020] | 100.0 |
| 2170 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Algeria | DZA | 2020 [YR2020] | 99.7 |
| 2171 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | American Samoa | ASM | 2020 [YR2020] | NaN |
| 2172 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Andorra | AND | 2020 [YR2020] | 100.0 |
| 2439 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Afghanistan | AFG | 2021 [YR2021] | 97.7 |
| 2440 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Albania | ALB | 2021 [YR2021] | 100.0 |
| 2441 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Algeria | DZA | 2021 [YR2021] | 99.8 |
| 2442 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | American Samoa | ASM | 2021 [YR2021] | NaN |
| 2443 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Andorra | AND | 2021 [YR2021] | 100.0 |
| 2710 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Afghanistan | AFG | 2022 [YR2022] | 85.3 |
| 2711 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Albania | ALB | 2022 [YR2022] | 100.0 |
| 2712 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Algeria | DZA | 2022 [YR2022] | 100.0 |
| 2713 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | American Samoa | ASM | 2022 [YR2022] | NaN |
| 2714 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Andorra | AND | 2022 [YR2022] | 100.0 |
| 2981 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Afghanistan | AFG | 2023 [YR2023] | NaN |
| 2982 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Albania | ALB | 2023 [YR2023] | NaN |
| 2983 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Algeria | DZA | 2023 [YR2023] | NaN |
| 2984 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | American Samoa | ASM | 2023 [YR2023] | NaN |
| 2985 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Andorra | AND | 2023 [YR2023] | NaN |
px.line(
first_countries,
x="Year",
y="Access to elec.",
color="Country Name",
title="Access to electricity for each country by year, first five",
)
Happiness data#
happiness = pd.read_excel("data/DataForTable2.1.xls")
happiness
| Country name | year | Life Ladder | Log GDP per capita | Social support | Healthy life expectancy at birth | Freedom to make life choices | Generosity | Perceptions of corruption | Positive affect | Negative affect | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | 2008 | 3.723590 | 7.350416 | 0.450662 | 50.500000 | 0.718114 | 0.164055 | 0.881686 | 0.414297 | 0.258195 |
| 1 | Afghanistan | 2009 | 4.401778 | 7.508646 | 0.552308 | 50.799999 | 0.678896 | 0.187297 | 0.850035 | 0.481421 | 0.237092 |
| 2 | Afghanistan | 2010 | 4.758381 | 7.613900 | 0.539075 | 51.099998 | 0.600127 | 0.117861 | 0.706766 | 0.516907 | 0.275324 |
| 3 | Afghanistan | 2011 | 3.831719 | 7.581259 | 0.521104 | 51.400002 | 0.495901 | 0.160098 | 0.731109 | 0.479835 | 0.267175 |
| 4 | Afghanistan | 2012 | 3.782938 | 7.660506 | 0.520637 | 51.700001 | 0.530935 | 0.234157 | 0.775620 | 0.613513 | 0.267919 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2358 | Zimbabwe | 2019 | 2.693523 | 7.697755 | 0.759162 | 53.099998 | 0.631908 | -0.050874 | 0.830652 | 0.658434 | 0.235354 |
| 2359 | Zimbabwe | 2020 | 3.159802 | 7.596050 | 0.717243 | 53.575001 | 0.643303 | 0.002848 | 0.788523 | 0.660658 | 0.345736 |
| 2360 | Zimbabwe | 2021 | 3.154578 | 7.656878 | 0.685151 | 54.049999 | 0.667636 | -0.079007 | 0.756945 | 0.609917 | 0.241682 |
| 2361 | Zimbabwe | 2022 | 3.296220 | 7.670073 | 0.666172 | 54.525002 | 0.651987 | -0.072935 | 0.752632 | 0.640609 | 0.191350 |
| 2362 | Zimbabwe | 2023 | 3.572386 | 7.678590 | 0.693817 | 55.000000 | 0.734613 | -0.068837 | 0.757494 | 0.609752 | 0.178953 |
2363 rows × 11 columns
px.scatter(
x=electricity_by_year["Access to elec."],
y=happiness["Life Ladder"],
title="Country access to electricity vs. happiness",
)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[17], line 1
----> 1 px.scatter(
2 x=electricity_by_year["Access to elec."],
3 y=happiness["Life Ladder"],
4 title="Country access to electricity vs. happiness"
5 )
File /opt/homebrew/Caskroom/mambaforge/base/envs/computing-in-context/lib/python3.12/site-packages/plotly/express/_chart_types.py:66, in scatter(data_frame, x, y, color, symbol, size, hover_name, hover_data, custom_data, text, facet_row, facet_col, facet_col_wrap, facet_row_spacing, facet_col_spacing, error_x, error_x_minus, error_y, error_y_minus, animation_frame, animation_group, category_orders, labels, orientation, color_discrete_sequence, color_discrete_map, color_continuous_scale, range_color, color_continuous_midpoint, symbol_sequence, symbol_map, opacity, size_max, marginal_x, marginal_y, trendline, trendline_options, trendline_color_override, trendline_scope, log_x, log_y, range_x, range_y, render_mode, title, template, width, height)
12 def scatter(
13 data_frame=None,
14 x=None,
(...)
60 height=None,
61 ) -> go.Figure:
62 """
63 In a scatter plot, each row of `data_frame` is represented by a symbol
64 mark in 2D space.
65 """
---> 66 return make_figure(args=locals(), constructor=go.Scatter)
File /opt/homebrew/Caskroom/mambaforge/base/envs/computing-in-context/lib/python3.12/site-packages/plotly/express/_core.py:2117, in make_figure(args, constructor, trace_patch, layout_patch)
2114 layout_patch = layout_patch or {}
2115 apply_default_cascade(args)
-> 2117 args = build_dataframe(args, constructor)
2118 if constructor in [go.Treemap, go.Sunburst, go.Icicle] and args["path"] is not None:
2119 args = process_dataframe_hierarchy(args)
File /opt/homebrew/Caskroom/mambaforge/base/envs/computing-in-context/lib/python3.12/site-packages/plotly/express/_core.py:1513, in build_dataframe(args, constructor)
1510 args["color"] = None
1511 # now that things have been prepped, we do the systematic rewriting of `args`
-> 1513 df_output, wide_id_vars = process_args_into_dataframe(
1514 args, wide_mode, var_name, value_name
1515 )
1517 # now that `df_output` exists and `args` contains only references, we complete
1518 # the special-case and wide-mode handling by further rewriting args and/or mutating
1519 # df_output
1521 count_name = _escape_col_name(df_output, "count", [var_name, value_name])
File /opt/homebrew/Caskroom/mambaforge/base/envs/computing-in-context/lib/python3.12/site-packages/plotly/express/_core.py:1274, in process_args_into_dataframe(args, wide_mode, var_name, value_name)
1271 col_name = _check_name_not_reserved(field, reserved_names)
1273 if length and len(argument) != length:
-> 1274 raise ValueError(
1275 "All arguments should have the same length. "
1276 "The length of argument `%s` is %d, whereas the "
1277 "length of previously-processed arguments %s is %d"
1278 % (field, len(argument), str(list(df_output.keys())), length)
1279 )
1280 df_output[str(col_name)] = to_unindexed_series(argument, str(col_name))
1282 # Finally, update argument with column name now that column exists
ValueError: All arguments should have the same length. The length of argument `y` is 2363, whereas the length of previously-processed arguments ['x'] is 3252
len(electricity_by_year["Access to elec."])
3252
len(happiness["Life Ladder"])
2363
electricity_by_year["Year"]
0 1990 [YR1990]
1 1990 [YR1990]
2 1990 [YR1990]
3 1990 [YR1990]
4 1990 [YR1990]
...
3247 2023 [YR2023]
3248 2023 [YR2023]
3249 2023 [YR2023]
3250 2023 [YR2023]
3251 2023 [YR2023]
Name: Year, Length: 3252, dtype: object
happiness["year"]
0 2008
1 2009
2 2010
3 2011
4 2012
...
2358 2019
2359 2020
2360 2021
2361 2022
2362 2023
Name: year, Length: 2363, dtype: int64
We decided to diverge from the original prompt and make a scatter plot.
Merge#
elec_2023 = electricity_by_year[electricity_by_year["Year"] == "2023 [YR2023]"]
elec_2023
| Series Name | Series Code | Country Name | Country Code | Year | Access to elec. | |
|---|---|---|---|---|---|---|
| 2981 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Afghanistan | AFG | 2023 [YR2023] | NaN |
| 2982 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Albania | ALB | 2023 [YR2023] | NaN |
| 2983 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Algeria | DZA | 2023 [YR2023] | NaN |
| 2984 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | American Samoa | ASM | 2023 [YR2023] | NaN |
| 2985 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Andorra | AND | 2023 [YR2023] | NaN |
| ... | ... | ... | ... | ... | ... | ... |
| 3247 | NaN | NaN | NaN | NaN | 2023 [YR2023] | NaN |
| 3248 | NaN | NaN | NaN | NaN | 2023 [YR2023] | NaN |
| 3249 | NaN | NaN | NaN | NaN | 2023 [YR2023] | NaN |
| 3250 | Data from database: World Development Indicators | NaN | NaN | NaN | 2023 [YR2023] | NaN |
| 3251 | Last Updated: 10/24/2024 | NaN | NaN | NaN | 2023 [YR2023] | NaN |
271 rows × 6 columns
hap_2023 = happiness[happiness["year"] == 2023]
hap_2023
| Country name | year | Life Ladder | Log GDP per capita | Social support | Healthy life expectancy at birth | Freedom to make life choices | Generosity | Perceptions of corruption | Positive affect | Negative affect | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 14 | Afghanistan | 2023 | 1.445909 | NaN | 0.368478 | 55.200001 | 0.228301 | NaN | 0.738471 | 0.260513 | 0.460167 |
| 30 | Albania | 2023 | 5.444691 | 9.688706 | 0.690753 | 69.199997 | 0.871545 | 0.067885 | 0.855425 | 0.597349 | 0.314227 |
| 63 | Argentina | 2023 | 6.393229 | 9.993596 | 0.892118 | 67.300003 | 0.831684 | -0.129061 | 0.846094 | 0.720122 | 0.301162 |
| 80 | Armenia | 2023 | 5.679090 | 9.729613 | 0.819338 | 68.199997 | 0.819376 | -0.179444 | 0.680709 | 0.574717 | 0.422631 |
| 97 | Australia | 2023 | 7.024582 | 10.846434 | 0.896460 | 71.199997 | 0.875769 | 0.187309 | 0.481580 | 0.731053 | 0.248163 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2295 | Venezuela | 2023 | 5.765363 | NaN | 0.884668 | 63.700001 | 0.756530 | NaN | 0.825393 | 0.757685 | 0.300038 |
| 2313 | Vietnam | 2023 | 6.325115 | 9.392351 | 0.844905 | 65.699997 | 0.955560 | -0.159114 | 0.655275 | 0.710068 | 0.119510 |
| 2327 | Yemen | 2023 | 3.531574 | NaN | 0.824958 | 56.599998 | 0.582724 | NaN | 0.771464 | 0.446534 | 0.340794 |
| 2344 | Zambia | 2023 | 3.685568 | 8.115053 | 0.664099 | 56.099998 | 0.854004 | 0.092377 | 0.814191 | 0.652999 | 0.359132 |
| 2362 | Zimbabwe | 2023 | 3.572386 | 7.678590 | 0.693817 | 55.000000 | 0.734613 | -0.068837 | 0.757494 | 0.609752 | 0.178953 |
138 rows × 11 columns
merged = pd.merge(elec_2023, hap_2023, left_on="Country Name", right_on="Country name")
merged
| Series Name | Series Code | Country Name | Country Code | Year | Access to elec. | Country name | year | Life Ladder | Log GDP per capita | Social support | Healthy life expectancy at birth | Freedom to make life choices | Generosity | Perceptions of corruption | Positive affect | Negative affect | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Afghanistan | AFG | 2023 [YR2023] | NaN | Afghanistan | 2023 | 1.445909 | NaN | 0.368478 | 55.200001 | 0.228301 | NaN | 0.738471 | 0.260513 | 0.460167 |
| 1 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Albania | ALB | 2023 [YR2023] | NaN | Albania | 2023 | 5.444691 | 9.688706 | 0.690753 | 69.199997 | 0.871545 | 0.067885 | 0.855425 | 0.597349 | 0.314227 |
| 2 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Argentina | ARG | 2023 [YR2023] | NaN | Argentina | 2023 | 6.393229 | 9.993596 | 0.892118 | 67.300003 | 0.831684 | -0.129061 | 0.846094 | 0.720122 | 0.301162 |
| 3 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Armenia | ARM | 2023 [YR2023] | NaN | Armenia | 2023 | 5.679090 | 9.729613 | 0.819338 | 68.199997 | 0.819376 | -0.179444 | 0.680709 | 0.574717 | 0.422631 |
| 4 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Australia | AUS | 2023 [YR2023] | NaN | Australia | 2023 | 7.024582 | 10.846434 | 0.896460 | 71.199997 | 0.875769 | 0.187309 | 0.481580 | 0.731053 | 0.248163 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 116 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | United States | USA | 2023 [YR2023] | NaN | United States | 2023 | 6.520872 | 11.089293 | 0.860503 | 65.599998 | 0.721453 | 0.184887 | 0.721701 | 0.706368 | 0.283993 |
| 117 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Uruguay | URY | 2023 [YR2023] | NaN | Uruguay | 2023 | 6.661722 | 10.122264 | 0.908374 | 67.500000 | 0.904164 | -0.050256 | 0.661658 | 0.753448 | 0.264817 |
| 118 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Uzbekistan | UZB | 2023 [YR2023] | NaN | Uzbekistan | 2023 | 6.385361 | 9.025871 | 0.908859 | 65.900002 | 0.926784 | 0.247137 | 0.649540 | 0.751747 | 0.202207 |
| 119 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Zambia | ZMB | 2023 [YR2023] | NaN | Zambia | 2023 | 3.685568 | 8.115053 | 0.664099 | 56.099998 | 0.854004 | 0.092377 | 0.814191 | 0.652999 | 0.359132 |
| 120 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Zimbabwe | ZWE | 2023 [YR2023] | NaN | Zimbabwe | 2023 | 3.572386 | 7.678590 | 0.693817 | 55.000000 | 0.734613 | -0.068837 | 0.757494 | 0.609752 | 0.178953 |
121 rows × 17 columns
Plot#
px.scatter(
merged,
x="Access to elec.",
y="Life Ladder",
color="Country Name",
title="Country access to electricity vs. happiness",
)
merged.dtypes
Series Name object
Series Code object
Country Name object
Country Code object
Year object
Access to elec. float64
Country name object
year int64
Life Ladder float64
Log GDP per capita float64
Social support float64
Healthy life expectancy at birth float64
Freedom to make life choices float64
Generosity float64
Perceptions of corruption float64
Positive affect float64
Negative affect float64
dtype: object
merged[["Country Name", "Access to elec.", "Life Ladder"]]
| Country Name | Access to elec. | Life Ladder | |
|---|---|---|---|
| 0 | Afghanistan | NaN | 1.445909 |
| 1 | Albania | NaN | 5.444691 |
| 2 | Argentina | NaN | 6.393229 |
| 3 | Armenia | NaN | 5.679090 |
| 4 | Australia | NaN | 7.024582 |
| ... | ... | ... | ... |
| 116 | United States | NaN | 6.520872 |
| 117 | Uruguay | NaN | 6.661722 |
| 118 | Uzbekistan | NaN | 6.385361 |
| 119 | Zambia | NaN | 3.685568 |
| 120 | Zimbabwe | NaN | 3.572386 |
121 rows × 3 columns
merged2 = pd.merge(elec_2023, hap_2023, left_on="Country Name", right_on="Country name", how="inner")
merged2 = merged2[["Country Name", "Access to elec.", "Life Ladder"]]
merged2
| Country Name | Access to elec. | Life Ladder | |
|---|---|---|---|
| 0 | Afghanistan | NaN | 1.445909 |
| 1 | Albania | NaN | 5.444691 |
| 2 | Argentina | NaN | 6.393229 |
| 3 | Armenia | NaN | 5.679090 |
| 4 | Australia | NaN | 7.024582 |
| ... | ... | ... | ... |
| 116 | United States | NaN | 6.520872 |
| 117 | Uruguay | NaN | 6.661722 |
| 118 | Uzbekistan | NaN | 6.385361 |
| 119 | Zambia | NaN | 3.685568 |
| 120 | Zimbabwe | NaN | 3.572386 |
121 rows × 3 columns
electricity["2023 [YR2023]"].unique()
array(['..', nan], dtype=object)
Whoops, 2023 is all blank. We need to do a different year.
2022#
elec_2022 = electricity_by_year[electricity_by_year["Year"] == "2022 [YR2022]"]
elec_2022
| Series Name | Series Code | Country Name | Country Code | Year | Access to elec. | |
|---|---|---|---|---|---|---|
| 2710 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Afghanistan | AFG | 2022 [YR2022] | 85.3 |
| 2711 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Albania | ALB | 2022 [YR2022] | 100.0 |
| 2712 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Algeria | DZA | 2022 [YR2022] | 100.0 |
| 2713 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | American Samoa | ASM | 2022 [YR2022] | NaN |
| 2714 | Access to electricity (% of population) | EG.ELC.ACCS.ZS | Andorra | AND | 2022 [YR2022] | 100.0 |
| ... | ... | ... | ... | ... | ... | ... |
| 2976 | NaN | NaN | NaN | NaN | 2022 [YR2022] | NaN |
| 2977 | NaN | NaN | NaN | NaN | 2022 [YR2022] | NaN |
| 2978 | NaN | NaN | NaN | NaN | 2022 [YR2022] | NaN |
| 2979 | Data from database: World Development Indicators | NaN | NaN | NaN | 2022 [YR2022] | NaN |
| 2980 | Last Updated: 10/24/2024 | NaN | NaN | NaN | 2022 [YR2022] | NaN |
271 rows × 6 columns
hap_2022 = happiness[happiness["year"] == 2022]
hap_2022
| Country name | year | Life Ladder | Log GDP per capita | Social support | Healthy life expectancy at birth | Freedom to make life choices | Generosity | Perceptions of corruption | Positive affect | Negative affect | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 13 | Afghanistan | 2022 | 1.281271 | NaN | 0.228217 | 54.875000 | 0.368377 | NaN | 0.733198 | 0.205868 | 0.575512 |
| 29 | Albania | 2022 | 5.212213 | 9.648703 | 0.724090 | 69.175003 | 0.802250 | -0.069952 | 0.845502 | 0.547126 | 0.254826 |
| 41 | Algeria | 2022 | 5.538172 | 9.322542 | 0.783401 | 66.699997 | 0.439971 | -0.044912 | 0.611083 | 0.583429 | 0.258596 |
| 62 | Argentina | 2022 | 6.260993 | 10.018916 | 0.893330 | 67.250000 | 0.825189 | -0.130209 | 0.810037 | 0.724068 | 0.284279 |
| 79 | Armenia | 2022 | 5.381943 | 9.682955 | 0.811169 | 67.925003 | 0.789599 | -0.157764 | 0.704730 | 0.530931 | 0.549468 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2294 | Venezuela | 2022 | 5.948992 | NaN | 0.899366 | 63.875000 | 0.770417 | NaN | 0.798016 | 0.754337 | 0.292252 |
| 2312 | Vietnam | 2022 | 6.266509 | 9.341064 | 0.878744 | 65.599998 | 0.975405 | -0.182106 | 0.703423 | 0.774236 | 0.108473 |
| 2326 | Yemen | 2022 | 3.590379 | NaN | 0.872113 | 56.825001 | 0.606788 | NaN | 0.787555 | 0.459952 | 0.255151 |
| 2343 | Zambia | 2022 | 3.728098 | 8.101115 | 0.717196 | 55.674999 | 0.888741 | -0.008760 | 0.716396 | 0.659846 | 0.308741 |
| 2361 | Zimbabwe | 2022 | 3.296220 | 7.670073 | 0.666172 | 54.525002 | 0.651987 | -0.072935 | 0.752632 | 0.640609 | 0.191350 |
140 rows × 11 columns
merged_2022 = pd.merge(elec_2022, hap_2022, left_on="Country Name", right_on="Country name", how="inner")
merged_2022 = merged_2022[["Country Name", "Access to elec.", "Life Ladder"]]
merged_2022
| Country Name | Access to elec. | Life Ladder | |
|---|---|---|---|
| 0 | Afghanistan | 85.3 | 1.281271 |
| 1 | Albania | 100.0 | 5.212213 |
| 2 | Algeria | 100.0 | 5.538172 |
| 3 | Argentina | 100.0 | 6.260993 |
| 4 | Armenia | 100.0 | 5.381943 |
| ... | ... | ... | ... |
| 117 | United States | 100.0 | 6.692790 |
| 118 | Uruguay | 100.0 | 6.670853 |
| 119 | Uzbekistan | 100.0 | 6.016239 |
| 120 | Zambia | 47.8 | 3.728098 |
| 121 | Zimbabwe | 50.1 | 3.296220 |
122 rows × 3 columns
Plot#
px.scatter(
merged_2022,
x="Access to elec.",
y="Life Ladder",
color="Country Name",
title="Country access to electricity vs. happiness",
)