#!/usr/bin/env python
# coding: utf-8

# # Paris Agreement: Entry Into Force

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get_ipython().run_line_magic('matplotlib', 'inline')


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import matplotlib.pyplot as plt
plt.style.use("ggplot")
from pandas_datapackage_reader import read_datapackage
from countrygroups import EUROPEAN_UNION


# Reading in the [Data Package](https://github.com/openclimatedata/paris-agreement-entry-into-force) with ratification status of the Paris Agreement.

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parties = read_datapackage("https://github.com/openclimatedata/paris-agreement-entry-into-force")


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parties.head()


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parties.count()


# The European Union is contained both as a block and as single parties so let's look at EU parties only.

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print(EUROPEAN_UNION)
assert(len(EUROPEAN_UNION) == 28)


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parties.loc["EUU"]


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parties.loc[EUROPEAN_UNION]


# For the emissions shares the last available data submitted to the UNFCCC was used.

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parties.Year.hist(bins=25 )
plt.title("Distribution of submission years");


# And a look at how emissions are distributed, many countries with small shares.

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parties.Percentage.hist(bins=20)
plt.title("Distribution of emissions shares for Entry Into Force");


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not_ratified = parties[parties["Ratification-Acceptance-Approval"].isnull() != False]
ratified = parties[parties["Ratification-Acceptance-Approval"].isnull() == False]


# Sum of emissions of parties that have ratified, subtracting EU as a group to avoid double-counting.

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print(ratified.Percentage.sum() - parties.loc["EUU"].Percentage)


# A list of the ten highest emitters who have not yet ratified.

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not_ratified.sort_values("Emissions", ascending=False).head(10)


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