Lecture 20 exercise - section 1

Lecture 20 exercise - section 1#

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

Clean#

electricity.fillna(0)

	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	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
267	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
268	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
269	Data from database: World Development Indicators	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
270	Last Updated: 10/24/2024	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0

271 rows × 16 columns

electricity[electricity["Country Code"] == "ASM"]

	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]
3	Access to electricity (% of population)	EG.ELC.ACCS.ZS	American Samoa	ASM	..	..	..	..	..	..	..	..	..	..	..	..

electricity["1990 [YR1990]"].value_counts(dropna=False)

[YR1990]
..                  159
                78
NaN                   5
8443069458008      2
1548004150391      1
3                  1
9943911362469      1
546800179424       1
8425200685198      1
9                  1
7                  1
8                  1
3068161010742      1
9834289550781      1
19                 1
6253509521484      1
8532638549805      1
9990844726563      1
9413986206055      1
0415649414063      1
7630462646484      1
8828430175781      1
8                  1
334586             1
9                  1
257427             1
846923828125       1
606559753418       1
475116             1
9842529296875      1
8492813110352      1
Name: count, dtype: int64

electricity["1990 [YR1990]"].mean()

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[5], line 1
----> 1 electricity["1990 [YR1990]"].mean()

File /opt/homebrew/Caskroom/mambaforge/base/envs/computing-in-context/lib/python3.12/site-packages/pandas/core/series.py:6549, in Series.mean(self, axis, skipna, numeric_only, **kwargs)
   6541 @doc(make_doc("mean", ndim=1))
   6542 def mean(
   6543     self,
   (...)
   6547     **kwargs,
   6548 ):
-> 6549     return NDFrame.mean(self, axis, skipna, numeric_only, **kwargs)

File /opt/homebrew/Caskroom/mambaforge/base/envs/computing-in-context/lib/python3.12/site-packages/pandas/core/generic.py:12420, in NDFrame.mean(self, axis, skipna, numeric_only, **kwargs)
  12413 def mean(
  12414     self,
  12415     axis: Axis | None = 0,
   (...)
  12418     **kwargs,
  12419 ) -> Series | float:
> 12420     return self._stat_function(
  12421         "mean", nanops.nanmean, axis, skipna, numeric_only, **kwargs
  12422     )

File /opt/homebrew/Caskroom/mambaforge/base/envs/computing-in-context/lib/python3.12/site-packages/pandas/core/generic.py:12377, in NDFrame._stat_function(self, name, func, axis, skipna, numeric_only, **kwargs)
  12373 nv.validate_func(name, (), kwargs)
  12375 validate_bool_kwarg(skipna, "skipna", none_allowed=False)
> 12377 return self._reduce(
  12378     func, name=name, axis=axis, skipna=skipna, numeric_only=numeric_only
  12379 )

File /opt/homebrew/Caskroom/mambaforge/base/envs/computing-in-context/lib/python3.12/site-packages/pandas/core/series.py:6457, in Series._reduce(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)
   6452     # GH#47500 - change to TypeError to match other methods
   6453     raise TypeError(
   6454         f"Series.{name} does not allow {kwd_name}={numeric_only} "
   6455         "with non-numeric dtypes."
   6456     )
-> 6457 return op(delegate, skipna=skipna, **kwds)

File /opt/homebrew/Caskroom/mambaforge/base/envs/computing-in-context/lib/python3.12/site-packages/pandas/core/nanops.py:147, in bottleneck_switch.__call__.<locals>.f(values, axis, skipna, **kwds)
    145         result = alt(values, axis=axis, skipna=skipna, **kwds)
    146 else:
--> 147     result = alt(values, axis=axis, skipna=skipna, **kwds)
    149 return result

File /opt/homebrew/Caskroom/mambaforge/base/envs/computing-in-context/lib/python3.12/site-packages/pandas/core/nanops.py:404, in _datetimelike_compat.<locals>.new_func(values, axis, skipna, mask, **kwargs)
    401 if datetimelike and mask is None:
    402     mask = isna(values)
--> 404 result = func(values, axis=axis, skipna=skipna, mask=mask, **kwargs)
    406 if datetimelike:
    407     result = _wrap_results(result, orig_values.dtype, fill_value=iNaT)

File /opt/homebrew/Caskroom/mambaforge/base/envs/computing-in-context/lib/python3.12/site-packages/pandas/core/nanops.py:719, in nanmean(values, axis, skipna, mask)
    716     dtype_count = dtype
    718 count = _get_counts(values.shape, mask, axis, dtype=dtype_count)
--> 719 the_sum = values.sum(axis, dtype=dtype_sum)
    720 the_sum = _ensure_numeric(the_sum)
    722 if axis is not None and getattr(the_sum, "ndim", False):

File /opt/homebrew/Caskroom/mambaforge/base/envs/computing-in-context/lib/python3.12/site-packages/numpy/_core/_methods.py:53, in _sum(a, axis, dtype, out, keepdims, initial, where)
     51 def _sum(a, axis=None, dtype=None, out=None, keepdims=False,
     52          initial=_NoValue, where=True):
---> 53     return umr_sum(a, axis, dtype, out, keepdims, initial, where)

TypeError: can only concatenate str (not "int") to str

electricity["1990 [YR1990]"].unique()

array(['..', '100', '95.7630462646484', '92.1548004150391',
       '99.9413986206055', '99.9842529296875', '87.475116',
       '94.606559753418', '99.846923828125', '92.257427', '89.9',
       '99.8443069458008', '70.334586', '96.8', '99.8828430175781',
       '99.8492813110352', '99.0415649414063', '98.9990844726563', '27.3',
       '99.8532638549805', '96.6253509521484', '70.19',
       '99.9834289550781', '91.3068161010742', '32.8', '88.7', '13.9',
       '99.8425200685198', '99.546800179424', '99.9943911362469', nan],
      dtype=object)

electricity["1990 [YR1990]"].astype(int)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[7], line 1
----> 1 electricity["1990 [YR1990]"].astype(int)

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: invalid literal for int() with base 10: '..'

import numpy as np

electricity["1990 [YR1990]"].replace({"..": np.nan}).value_counts()

[YR1990]
               78
8443069458008     2
0415649414063     1
546800179424      1
8425200685198     1
9                 1
7                 1
8                 1
3068161010742     1
9834289550781     1
19                1
6253509521484     1
8532638549805     1
3                 1
9990844726563     1
8492813110352     1
7630462646484     1
8828430175781     1
8                 1
334586            1
9                 1
257427            1
846923828125      1
606559753418      1
475116            1
9842529296875     1
9413986206055     1
1548004150391     1
9943911362469     1
Name: count, dtype: int64

electricity["1990 [YR1990]"].replace({"..": np.nan}).astype(float)

      NaN
    100.0
      NaN
      NaN
    100.0
       ...  
    NaN
    NaN
    NaN
    NaN
    NaN
Name: 1990 [YR1990], Length: 271, dtype: float64

electricity_clean = electricity.replace({"..": np.nan})
electricity_clean

/var/folders/kr/nx0m1j811kz5vy8c87ffchzr0000gn/T/ipykernel_70664/1911216467.py:1: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
  electricity_clean = electricity.replace({

	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	NaN	4.4	89.5	71.5	97.7	97.7	93.4	97.7	97.7	97.7	85.3	NaN
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	NaN
2	Access to electricity (% of population)	EG.ELC.ACCS.ZS	Algeria	DZA	NaN	98.6	99.3	99.4	99.4	99.5	99.6	99.5	99.7	99.8	100	NaN
3	Access to electricity (% of population)	EG.ELC.ACCS.ZS	American Samoa	ASM	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	Access to electricity (% of population)	EG.ELC.ACCS.ZS	Andorra	AND	100	100	100	100	100	100	100	100	100	100	100	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
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

Reshape #

electricity_per_year = electricity_clean.melt(
    id_vars=["Series Name", "Series Code", "Country Name", "Country Code"],
    var_name="Year",
    value_name="Access to electricity (% pop)",
)
electricity_per_year

	Series Name	Series Code	Country Name	Country Code	Year	Access to electricity (% pop)
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
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
...	...	...	...	...	...	...
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#

electricity_per_year["Access to electricity (% pop)"] = electricity_per_year["Access to electricity (% pop)"].astype(float)

import plotly.express as px

px.line(
    electricity_per_year,
    x="Year",
    y="Access to electricity (% pop)",
    color="Country Name",
    title="Access to electricity for each country by year",
)

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

Merge#

pd.merge(
    electricity_per_year,
    happiness,
    left_on=["Country Code", "Year"],
    right_on=["Country name", "year"],
)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[15], line 1
----> 1 pd.merge(
   electricity_per_year,
   happiness,
   left_on=["Country Code", "Year"],
   right_on=["Country name", "year"]
)

File /opt/homebrew/Caskroom/mambaforge/base/envs/computing-in-context/lib/python3.12/site-packages/pandas/core/reshape/merge.py:170, in merge(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)
   return _cross_merge(
       left_df,
       right_df,
   (...)
       copy=copy,
   )
else:
--> 170     op = _MergeOperation(
       left_df,
       right_df,
       how=how,
       on=on,
       left_on=left_on,
       right_on=right_on,
       left_index=left_index,
       right_index=right_index,
       sort=sort,
       suffixes=suffixes,
       indicator=indicator,
       validate=validate,
   )
   return op.get_result(copy=copy)

File /opt/homebrew/Caskroom/mambaforge/base/envs/computing-in-context/lib/python3.12/site-packages/pandas/core/reshape/merge.py:807, in _MergeOperation.__init__(self, left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, indicator, validate)
self._validate_tolerance(self.left_join_keys)
# validate the merge keys dtypes. We may need to coerce
# to avoid incompatible dtypes
--> 807 self._maybe_coerce_merge_keys()
# If argument passed to validate,
# check if columns specified as unique
# are in fact unique.
if validate is not None:

File /opt/homebrew/Caskroom/mambaforge/base/envs/computing-in-context/lib/python3.12/site-packages/pandas/core/reshape/merge.py:1508, in _MergeOperation._maybe_coerce_merge_keys(self)
   # unless we are merging non-string-like with string-like
   elif (
       inferred_left in string_types and inferred_right not in string_types
   ) or (
       inferred_right in string_types and inferred_left not in string_types
   ):
-> 1508         raise ValueError(msg)
# datetimelikes must match exactly
elif needs_i8_conversion(lk.dtype) and not needs_i8_conversion(rk.dtype):

ValueError: You are trying to merge on object and int64 columns for key 'Year'. If you wish to proceed you should use pd.concat

Clean#

electricity_per_year["Year"].unique()

array(['1990 [YR1990]', '2000 [YR2000]', '2014 [YR2014]', '2015 [YR2015]',
       '2016 [YR2016]', '2017 [YR2017]', '2018 [YR2018]', '2019 [YR2019]',
       '2020 [YR2020]', '2021 [YR2021]', '2022 [YR2022]', '2023 [YR2023]'],
      dtype=object)

electricity_per_year["Year"] = electricity_per_year["Year"].str.replace(r" \[YR\d{4}\]", "", regex=True).astype(int)

electricity_per_year

	Series Name	Series Code	Country Name	Country Code	Year	Access to electricity (% pop)
0	Access to electricity (% of population)	EG.ELC.ACCS.ZS	Afghanistan	AFG	1990	NaN
1	Access to electricity (% of population)	EG.ELC.ACCS.ZS	Albania	ALB	1990	100.0
2	Access to electricity (% of population)	EG.ELC.ACCS.ZS	Algeria	DZA	1990	NaN
3	Access to electricity (% of population)	EG.ELC.ACCS.ZS	American Samoa	ASM	1990	NaN
4	Access to electricity (% of population)	EG.ELC.ACCS.ZS	Andorra	AND	1990	100.0
...	...	...	...	...	...	...
3247	NaN	NaN	NaN	NaN	2023	NaN
3248	NaN	NaN	NaN	NaN	2023	NaN
3249	NaN	NaN	NaN	NaN	2023	NaN
3250	Data from database: World Development Indicators	NaN	NaN	NaN	2023	NaN
3251	Last Updated: 10/24/2024	NaN	NaN	NaN	2023	NaN

3252 rows × 6 columns

pd.merge(
    electricity_per_year,
    happiness,
    left_on=["Country Code", "Year"],
    right_on=["Country name", "year"],
)

	Series Name	Series Code	Country Name	Country Code	Year	Access to electricity (% pop)	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

Jump over to the end of official solution.

Lecture 20 exercise - section 1

Contents

Lecture 20 exercise - section 1#

Electricity data#

Clean#

Reshape#

Plot#

Happiness data#

Merge#

Clean#

Reshape #