如何按年将映射与带（和不带）动画的聚合结合起来，并仍然用缺失值对土地进行着色？
How to combine a mapping with aggregates with (and without) animation by years and still colorize land with missing values?
- 我正在尝试向我的等值线添加聚合函数。
在后者上，多亏了@RobRaymond，我在显示带有缺失值的国家及其名称的同时按年获得动画。
在 Plotly 网站 [https://plotly.com/python/aggregations/] 上，我看到我们可以获得聚合映射。
我试图将它添加到我的代码中，但我无法获得预期的结果。
我错过了什么？
我的代码：
import plotly.io as pio
import plotly.graph_objects as go
import plotly.express as px
import geopandas as gpd
import pandas as pd
import numpy as np
# need to know countries that make up natural earth...
world = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres"))
data = {"Country helped":
['Afghanistan', 'Algérie', 'Angola', 'Bangladesh', 'Bénin',
'Bhoutan', 'Bolivie (État plurinational de)', 'Burkina Faso',
'Burundi', 'Cambodge', 'Cameroun', 'Colombie', 'Comores', 'Congo',
"Côte d'Ivoire", 'Cuba', 'Djibouti', 'Égypte', 'El Salvador',
'Équateur', 'Éthiopie', 'Gambie', 'Géorgie', 'Ghana', 'Guatemala',
'Guinée', 'Guinée-Bissau', 'Haïti', 'Honduras',
"Iran (République islamique d')", 'Iraq', 'Jordanie', 'Kenya',
'Kirghizistan', 'Lesotho', 'Liban', 'Libéria', 'Libye',
'Madagascar', 'Malawi', 'Mali', 'Mauritanie', 'Mozambique',
'Myanmar', 'Népal', 'Nicaragua', 'Niger', 'Ouganda', 'Pakistan',
'Palestine', 'Philippines', 'République arabe syrienne',
'République centrafricaine', 'République démocratique du Congo',
'République démocratique populaire lao', 'République dominicaine',
'République populaire démocratique de Corée',
'République-Unie de Tanzanie', 'Rwanda', 'Sao Tomé-et-Principe',
'Sénégal', 'Sierra Leone', 'Somalie', 'Soudan', 'Soudan du Sud',
'Sri Lanka', 'Tadjikistan', 'Tchad', 'Timor-Leste', 'Togo',
'Vanuatu', 'Yémen', 'Zambie', 'Zimbabwe'],
"Code zone (ISO3)":
['AFG', 'DZA', 'AGO', 'BGD', 'BEN', 'BTN', 'BOL', 'BFA', 'BDI',
'KHM', 'CMR', 'COL', 'COM', 'COG', 'CIV', 'CUB', 'DJI', 'EGY',
'SLV', 'ECU', 'ETH', 'GMB', 'GEO', 'GHA', 'GTM', 'GIN', 'GNB',
'HTI', 'HND', 'IRN', 'IRQ', 'JOR', 'KEN', 'KGZ', 'LSO', 'LBN',
'LBR', 'LBY', 'MDG', 'MWI', 'MLI', 'MRT', 'MOZ', 'MMR', 'NPL',
'NIC', 'NER', 'UGA', 'PAK', 'PSE', 'PHL', 'SYR', 'CAF', 'COD',
'LAO', 'DOM', 'PRK', 'TZA', 'RWA', 'STP', 'SEN', 'SLE', 'SOM',
'SDN', 'SSD', 'LKA', 'TJK', 'TCD', 'TLS', 'TGO', 'VUT', 'YEM',
'ZMB', 'ZWE'],
"Product":
['Autres non-céréales', 'Blé et Farin', 'Céréales',
'Fruits secs, total', 'Huiles végétales',
'Légumineuses Sèches,Tot.', 'Non-céréales', 'Riz, total',
'Sucre, total', 'Céréales Secondaires', 'Lait, total',
'Mélanges et préparations', 'Poiss&produi', 'BulgurFarEnt',
'Viande Total', 'Graisses Com'],
"Year":
['2013', '2014', '2015', '2016'],
"Quantity of donated products": np.random.random_integers(90, 40000, 65)
}
df = pd.DataFrame(data, columns = ["Country helped", "Code zone (ISO3)",
"Product", "Année", "Quantity of donated products"])
aggs = ["count","sum","avg","median","mode","rms","stddev","min","max","first","last"]
agg = []
agg_func = []
for i in range(0, len(aggs)):
agg = dict(
args=['transforms[0].aggregations[0].func', aggs[i]],
label=aggs[i],
method='restyle'
)
agg_func.append(agg)
# Utilisation de plotly.express pour l'animation
data = [dict(df,
type = 'choropleth',
locations = df["Code zone (ISO3)"],
z = df["Quantity of donated products"],
color="z",
color_continuous_scale="earth_r",
animation_frame=df["Year"],
hover_name="Country helped",
hover_data=["Product"],
transforms = [dict(
type = 'aggregate',
groups = df["Code zone (ISO3)"],
aggregations = [dict(
target = 'z', func = 'sum', enabled = True)
]
)]
)
]
layout = dict(
autosize=True,
title = "<b>Evolution of food aid in the world from 2013 to 2016</b><br>use dropdown to change aggregation",
yaxis = dict(title = 'Score', range = [0,22]),
geo=dict(
landcolor="lightgray",
showland=True,
showcountries=True,
countrycolor="gray",
countrywidth=0.5,
showframe=False,
showcoastlines=False,
projection_type="natural earth",
showocean=True, oceancolor="lightBlue",
showlakes=True, lakecolor="lightblue",
showrivers=True, rivercolor="lightblue"
),
annotations=[
dict(
x=0.55,
y=0.15,
xref="paper",
yref="paper",
text='Source: <a href="https://www.fao.org/faostat/fr/#data">\
FAO</a>',
showarrow=False
)
],
updatemenus = [dict(
x = 0.95,
y = 1.10,
xref = 'paper',
yref = 'paper',
yanchor = 'top',
active = 1,
showactive = False,
buttons = agg_func
)],
coloraxis2={"colorscale": [[0, "lightgray"], [1, "lightgray"]], "showscale": False}
)
fig.layout.updatemenus[0].buttons[0].args[1]['frame']['duration'] = 2000
fig.layout.updatemenus[0].buttons[0].args[1]['transition']['duration'] = 1000
# update each of the animation frames with missing countries
for fr in fig.frames:
tr_missing = (
px.choropleth(
world.loc[~world["iso_a3"].isin(fr.data[0]["locations"]), "iso_a3"]
.to_frame()
.assign(color=1),
color="color",
locations="iso_a3"
)
.update_traces(hovertemplate="%{location} : Datas manquantes",
coloraxis="coloraxis2"
)
.data[0]
)
fr.update(data=[fr.data[0], tr_missing])
# re-construct the figure...
fig_dict = go.Figure(data=fig.frames[0].data, layout=fig.layout, frames=fig.frames)
pio.show(fig_dict, validate=True)
- 如果我想删除
animation_frame=df["Year"] ，我没有找到如何将额外丢失的跟踪追加到数据跟踪中
编辑 2：
- 以文本形式提供数据样本
- 将专栏翻译成英文
- 努力让我的代码更容易测试
- 添加问题
编辑 1：
The plot i get
Here is a sample of df_world_aide_alim
A second sample: df_world_aide_alim.head(25)
The dropdown to change aggregation i tried to combine
- 提供的用于创建数据框的代码失败。提供 dict 到
pd.DataFrame() 所有列表的长度必须相同。已经从这个 dict 合成了一个数据框，创建了所有相同长度的列表（加上这个的副作用，确保国家名称和代码一致）
- 已经使用 pandas named aggregations 创建了您想要的所有分析。请注意，有一些不映射 1:1 已弃用的 plotly 名称和 pandas[= 所需的方法55=]
build_fig() 现在有数据框 dfa（有聚合列表，没有动画）
Code zone (ISO3)
count
sum
avg
median
mode
rms
stddev
min
max
first
last
Country helped
Product
AFG
3
46647
15549
12358
5086
32115.5
12371.1
5086
29203
12358
29203
Afghanistan
['Huiles végétales', 'Céréales', 'Sucre, total']
AGO
5
75067
15013.4
14357
2187
38317.9
9236.46
2187
26697
14357
26697
Angola
['Riz, total', 'Fruits secs, total', 'Céréales Secondaires', 'Poiss&produi']
- 从这个结构中可以清楚地看出，可以从每一列中创建一个 trace。默认情况下使第一个可见 (count)
- 添加缺失的国家trace
- 创建 updatemenus 以根据选择 missing 始终可见trace 可见性
build_fig(df)
- 动画类似。在不使用 dash 的情况下，我也看不到从下拉列表中选择每帧多个分析的选项。因此，动画示例和一个分析示例。很明显，对于动画，您需要另一个数据维度，为此使用了年份。
build_fig(df, aggs="rms", group=["Code zone (ISO3)", "Year"])
内嵌列表更复杂的聚合函数
build_fig(
df,
agg_cols={
"Country helped": ("Country helped", "first"),
"Product": (
"Product",
lambda s: df.loc[s.index]
.sort_values("Quantity of donated products", ascending=False)
.loc[:, "Product"]
.head(3),
),
},
)
完整代码
import plotly.io as pio
import plotly.graph_objects as go
import plotly.express as px
import geopandas as gpd
import pandas as pd
import numpy as np
import math
# fmt: off
data = {"Country helped":
['Afghanistan', 'Algérie', 'Angola', 'Bangladesh', 'Bénin',
'Bhoutan', 'Bolivie (État plurinational de)', 'Burkina Faso',
'Burundi', 'Cambodge', 'Cameroun', 'Colombie', 'Comores', 'Congo',
"Côte d'Ivoire", 'Cuba', 'Djibouti', 'Égypte', 'El Salvador',
'Équateur', 'Éthiopie', 'Gambie', 'Géorgie', 'Ghana', 'Guatemala',
'Guinée', 'Guinée-Bissau', 'Haïti', 'Honduras',
"Iran (République islamique d')", 'Iraq', 'Jordanie', 'Kenya',
'Kirghizistan', 'Lesotho', 'Liban', 'Libéria', 'Libye',
'Madagascar', 'Malawi', 'Mali', 'Mauritanie', 'Mozambique',
'Myanmar', 'Népal', 'Nicaragua', 'Niger', 'Ouganda', 'Pakistan',
'Palestine', 'Philippines', 'République arabe syrienne',
'République centrafricaine', 'République démocratique du Congo',
'République démocratique populaire lao', 'République dominicaine',
'République populaire démocratique de Corée',
'République-Unie de Tanzanie', 'Rwanda', 'Sao Tomé-et-Principe',
'Sénégal', 'Sierra Leone', 'Somalie', 'Soudan', 'Soudan du Sud',
'Sri Lanka', 'Tadjikistan', 'Tchad', 'Timor-Leste', 'Togo',
'Vanuatu', 'Yémen', 'Zambie', 'Zimbabwe'],
"Code zone (ISO3)":
['AFG', 'DZA', 'AGO', 'BGD', 'BEN', 'BTN', 'BOL', 'BFA', 'BDI',
'KHM', 'CMR', 'COL', 'COM', 'COG', 'CIV', 'CUB', 'DJI', 'EGY',
'SLV', 'ECU', 'ETH', 'GMB', 'GEO', 'GHA', 'GTM', 'GIN', 'GNB',
'HTI', 'HND', 'IRN', 'IRQ', 'JOR', 'KEN', 'KGZ', 'LSO', 'LBN',
'LBR', 'LBY', 'MDG', 'MWI', 'MLI', 'MRT', 'MOZ', 'MMR', 'NPL',
'NIC', 'NER', 'UGA', 'PAK', 'PSE', 'PHL', 'SYR', 'CAF', 'COD',
'LAO', 'DOM', 'PRK', 'TZA', 'RWA', 'STP', 'SEN', 'SLE', 'SOM',
'SDN', 'SSD', 'LKA', 'TJK', 'TCD', 'TLS', 'TGO', 'VUT', 'YEM',
'ZMB', 'ZWE'],
"Product":
['Autres non-céréales', 'Blé et Farin', 'Céréales',
'Fruits secs, total', 'Huiles végétales',
'Légumineuses Sèches,Tot.', 'Non-céréales', 'Riz, total',
'Sucre, total', 'Céréales Secondaires', 'Lait, total',
'Mélanges et préparations', 'Poiss&produi', 'BulgurFarEnt',
'Viande Total', 'Graisses Com'],
"Year":
['2013', '2014', '2015', '2016'],
"Quantity of donated products": np.random.randint(90, 40000, 500)
}
# fmt: on
# this fails !!!
# df = pd.DataFrame(data, columns = ["Country helped", "Code zone (ISO3)",
# "Product", "Année", "Quantity of donated products"])
# provided data is ragged, different lengths for each column, make all arrays same length for data frame
df = (
pd.DataFrame(
{
k: data[k] if len(data[k]) == c else np.random.choice(data[k], c)
for k in data.keys()
if (c := max({k: len(data[k]) for k in data.keys()}.values()))
}
)
.drop(columns=["Country helped"])
.merge(
pd.DataFrame(
{
k: v
for k, v in data.items()
if k in ["Country helped", "Code zone (ISO3)"]
}
),
on="Code zone (ISO3)",
how="inner",
)
)
aggs = [
"count",
"sum",
"avg",
"median",
"mode",
"rms",
"stddev",
"min",
"max",
"first",
"last",
]
def build_fig(df,
aggs=aggs,
analytical_col="Quantity of donated products",
group=["Code zone (ISO3)"],
agg_cols={
"Country helped": ("Country helped", "first"),
"Product": ("Product", lambda s: s.unique().tolist()),
},
):
# make sure argument permutations make sense
assert (len(group) == 1 and isinstance(aggs, list)) or (
len(group) == 2 and isinstance(aggs, str)
)
# need to know countries that make up natural earth...
world = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres"))
# pandas aggregate functions don't map 1:1 to deprecated plotly functions
override = {
"avg": "mean",
"mode": lambda s: s.mode().iloc[0],
"rms": lambda s: math.sqrt((s ** 2).sum()),
"stddev": "std",
}
# create dataframe with all wanted aggregates as columns, plus keep country name and list of products
dfa = df.groupby(group, as_index=False).agg(
**{
**{
agg: (
analytical_col,
agg if agg not in override.keys() else override[agg],
)
for agg in (aggs if isinstance(aggs, list) else [aggs])
},
**agg_cols,
}
)
# generate trace of missing countries to be included where necessary
def missing(values):
return (
px.choropleth(
world.loc[~world["iso_a3"].isin(values), "iso_a3"],
locations="iso_a3",
)
.update_traces(
name="missing",
colorscale=[[0, "red"], [1, "red"]],
showlegend=False,
hovertemplate="missing: %{location}",
)
.data
)
# create a trace for each required aggregate
if len(group) == 1:
fig = go.Figure(
[
t.update(name=agg, visible=(isinstance(aggs, str) or aggs[0] == agg))
for agg in (aggs if isinstance(aggs, list) else [aggs])
for t in px.choropleth(
dfa, locations=group[0], color=agg, hover_data=list(agg_cols.keys())
).data
]
)
# add the missing layer
fig.add_traces(missing(fig.data[0]["locations"]))
# add the drop down menu...
fig.update_layout(
updatemenus=[
{
"buttons": [
{
"label": agg,
"method": "restyle",
"args": [
{
"visible": [
t.name == agg or t.name == "missing"
for t in fig.data
]
}
],
}
for agg in aggs
]
}
]
)
# generate an animation, with only one aggregation
else:
fig = px.choropleth(
dfa,
locations=group[0],
color=aggs,
animation_frame=group[1],
hover_data=list(agg_cols.keys()),
)
# update each of the animation frames with missing countries
for fr in fig.frames:
fr.update(data=[fr.data[0], missing(fr.data[0]["locations"])[0]])
# re-construct the figure...
fig = go.Figure(data=fig.frames[0].data, layout=fig.layout, frames=fig.frames)
return fig
- 我正在尝试向我的等值线添加聚合函数。 在后者上，多亏了@RobRaymond，我在显示带有缺失值的国家及其名称的同时按年获得动画。
在 Plotly 网站 [https://plotly.com/python/aggregations/] 上，我看到我们可以获得聚合映射。
我试图将它添加到我的代码中，但我无法获得预期的结果。 我错过了什么？
我的代码：
import plotly.io as pio
import plotly.graph_objects as go
import plotly.express as px
import geopandas as gpd
import pandas as pd
import numpy as np
# need to know countries that make up natural earth...
world = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres"))
data = {"Country helped":
['Afghanistan', 'Algérie', 'Angola', 'Bangladesh', 'Bénin',
'Bhoutan', 'Bolivie (État plurinational de)', 'Burkina Faso',
'Burundi', 'Cambodge', 'Cameroun', 'Colombie', 'Comores', 'Congo',
"Côte d'Ivoire", 'Cuba', 'Djibouti', 'Égypte', 'El Salvador',
'Équateur', 'Éthiopie', 'Gambie', 'Géorgie', 'Ghana', 'Guatemala',
'Guinée', 'Guinée-Bissau', 'Haïti', 'Honduras',
"Iran (République islamique d')", 'Iraq', 'Jordanie', 'Kenya',
'Kirghizistan', 'Lesotho', 'Liban', 'Libéria', 'Libye',
'Madagascar', 'Malawi', 'Mali', 'Mauritanie', 'Mozambique',
'Myanmar', 'Népal', 'Nicaragua', 'Niger', 'Ouganda', 'Pakistan',
'Palestine', 'Philippines', 'République arabe syrienne',
'République centrafricaine', 'République démocratique du Congo',
'République démocratique populaire lao', 'République dominicaine',
'République populaire démocratique de Corée',
'République-Unie de Tanzanie', 'Rwanda', 'Sao Tomé-et-Principe',
'Sénégal', 'Sierra Leone', 'Somalie', 'Soudan', 'Soudan du Sud',
'Sri Lanka', 'Tadjikistan', 'Tchad', 'Timor-Leste', 'Togo',
'Vanuatu', 'Yémen', 'Zambie', 'Zimbabwe'],
"Code zone (ISO3)":
['AFG', 'DZA', 'AGO', 'BGD', 'BEN', 'BTN', 'BOL', 'BFA', 'BDI',
'KHM', 'CMR', 'COL', 'COM', 'COG', 'CIV', 'CUB', 'DJI', 'EGY',
'SLV', 'ECU', 'ETH', 'GMB', 'GEO', 'GHA', 'GTM', 'GIN', 'GNB',
'HTI', 'HND', 'IRN', 'IRQ', 'JOR', 'KEN', 'KGZ', 'LSO', 'LBN',
'LBR', 'LBY', 'MDG', 'MWI', 'MLI', 'MRT', 'MOZ', 'MMR', 'NPL',
'NIC', 'NER', 'UGA', 'PAK', 'PSE', 'PHL', 'SYR', 'CAF', 'COD',
'LAO', 'DOM', 'PRK', 'TZA', 'RWA', 'STP', 'SEN', 'SLE', 'SOM',
'SDN', 'SSD', 'LKA', 'TJK', 'TCD', 'TLS', 'TGO', 'VUT', 'YEM',
'ZMB', 'ZWE'],
"Product":
['Autres non-céréales', 'Blé et Farin', 'Céréales',
'Fruits secs, total', 'Huiles végétales',
'Légumineuses Sèches,Tot.', 'Non-céréales', 'Riz, total',
'Sucre, total', 'Céréales Secondaires', 'Lait, total',
'Mélanges et préparations', 'Poiss&produi', 'BulgurFarEnt',
'Viande Total', 'Graisses Com'],
"Year":
['2013', '2014', '2015', '2016'],
"Quantity of donated products": np.random.random_integers(90, 40000, 65)
}
df = pd.DataFrame(data, columns = ["Country helped", "Code zone (ISO3)",
"Product", "Année", "Quantity of donated products"])
aggs = ["count","sum","avg","median","mode","rms","stddev","min","max","first","last"]
agg = []
agg_func = []
for i in range(0, len(aggs)):
agg = dict(
args=['transforms[0].aggregations[0].func', aggs[i]],
label=aggs[i],
method='restyle'
)
agg_func.append(agg)
# Utilisation de plotly.express pour l'animation
data = [dict(df,
type = 'choropleth',
locations = df["Code zone (ISO3)"],
z = df["Quantity of donated products"],
color="z",
color_continuous_scale="earth_r",
animation_frame=df["Year"],
hover_name="Country helped",
hover_data=["Product"],
transforms = [dict(
type = 'aggregate',
groups = df["Code zone (ISO3)"],
aggregations = [dict(
target = 'z', func = 'sum', enabled = True)
]
)]
)
]
layout = dict(
autosize=True,
title = "<b>Evolution of food aid in the world from 2013 to 2016</b><br>use dropdown to change aggregation",
yaxis = dict(title = 'Score', range = [0,22]),
geo=dict(
landcolor="lightgray",
showland=True,
showcountries=True,
countrycolor="gray",
countrywidth=0.5,
showframe=False,
showcoastlines=False,
projection_type="natural earth",
showocean=True, oceancolor="lightBlue",
showlakes=True, lakecolor="lightblue",
showrivers=True, rivercolor="lightblue"
),
annotations=[
dict(
x=0.55,
y=0.15,
xref="paper",
yref="paper",
text='Source: <a href="https://www.fao.org/faostat/fr/#data">\
FAO</a>',
showarrow=False
)
],
updatemenus = [dict(
x = 0.95,
y = 1.10,
xref = 'paper',
yref = 'paper',
yanchor = 'top',
active = 1,
showactive = False,
buttons = agg_func
)],
coloraxis2={"colorscale": [[0, "lightgray"], [1, "lightgray"]], "showscale": False}
)
fig.layout.updatemenus[0].buttons[0].args[1]['frame']['duration'] = 2000
fig.layout.updatemenus[0].buttons[0].args[1]['transition']['duration'] = 1000
# update each of the animation frames with missing countries
for fr in fig.frames:
tr_missing = (
px.choropleth(
world.loc[~world["iso_a3"].isin(fr.data[0]["locations"]), "iso_a3"]
.to_frame()
.assign(color=1),
color="color",
locations="iso_a3"
)
.update_traces(hovertemplate="%{location} : Datas manquantes",
coloraxis="coloraxis2"
)
.data[0]
)
fr.update(data=[fr.data[0], tr_missing])
# re-construct the figure...
fig_dict = go.Figure(data=fig.frames[0].data, layout=fig.layout, frames=fig.frames)
pio.show(fig_dict, validate=True)
- 如果我想删除
animation_frame=df["Year"]，我没有找到如何将额外丢失的跟踪追加到数据跟踪中
编辑 2：
- 以文本形式提供数据样本
- 将专栏翻译成英文
- 努力让我的代码更容易测试
- 添加问题
编辑 1：
The plot i get
Here is a sample of df_world_aide_alim
A second sample: df_world_aide_alim.head(25)
The dropdown to change aggregation i tried to combine
- 提供的用于创建数据框的代码失败。提供 dict 到
pd.DataFrame()所有列表的长度必须相同。已经从这个 dict 合成了一个数据框，创建了所有相同长度的列表（加上这个的副作用，确保国家名称和代码一致） - 已经使用 pandas named aggregations 创建了您想要的所有分析。请注意，有一些不映射 1:1 已弃用的 plotly 名称和 pandas[= 所需的方法55=]
build_fig()现在有数据框 dfa（有聚合列表，没有动画）
| Code zone (ISO3) | count | sum | avg | median | mode | rms | stddev | min | max | first | last | Country helped | Product |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AFG | 3 | 46647 | 15549 | 12358 | 5086 | 32115.5 | 12371.1 | 5086 | 29203 | 12358 | 29203 | Afghanistan | ['Huiles végétales', 'Céréales', 'Sucre, total'] |
| AGO | 5 | 75067 | 15013.4 | 14357 | 2187 | 38317.9 | 9236.46 | 2187 | 26697 | 14357 | 26697 | Angola | ['Riz, total', 'Fruits secs, total', 'Céréales Secondaires', 'Poiss&produi'] |
- 从这个结构中可以清楚地看出，可以从每一列中创建一个 trace。默认情况下使第一个可见 (count)
- 添加缺失的国家trace
- 创建 updatemenus 以根据选择 missing 始终可见trace 可见性
build_fig(df)
- 动画类似。在不使用 dash 的情况下，我也看不到从下拉列表中选择每帧多个分析的选项。因此，动画示例和一个分析示例。很明显，对于动画，您需要另一个数据维度，为此使用了年份。
build_fig(df, aggs="rms", group=["Code zone (ISO3)", "Year"])
内嵌列表更复杂的聚合函数
build_fig(
df,
agg_cols={
"Country helped": ("Country helped", "first"),
"Product": (
"Product",
lambda s: df.loc[s.index]
.sort_values("Quantity of donated products", ascending=False)
.loc[:, "Product"]
.head(3),
),
},
)
完整代码
import plotly.io as pio
import plotly.graph_objects as go
import plotly.express as px
import geopandas as gpd
import pandas as pd
import numpy as np
import math
# fmt: off
data = {"Country helped":
['Afghanistan', 'Algérie', 'Angola', 'Bangladesh', 'Bénin',
'Bhoutan', 'Bolivie (État plurinational de)', 'Burkina Faso',
'Burundi', 'Cambodge', 'Cameroun', 'Colombie', 'Comores', 'Congo',
"Côte d'Ivoire", 'Cuba', 'Djibouti', 'Égypte', 'El Salvador',
'Équateur', 'Éthiopie', 'Gambie', 'Géorgie', 'Ghana', 'Guatemala',
'Guinée', 'Guinée-Bissau', 'Haïti', 'Honduras',
"Iran (République islamique d')", 'Iraq', 'Jordanie', 'Kenya',
'Kirghizistan', 'Lesotho', 'Liban', 'Libéria', 'Libye',
'Madagascar', 'Malawi', 'Mali', 'Mauritanie', 'Mozambique',
'Myanmar', 'Népal', 'Nicaragua', 'Niger', 'Ouganda', 'Pakistan',
'Palestine', 'Philippines', 'République arabe syrienne',
'République centrafricaine', 'République démocratique du Congo',
'République démocratique populaire lao', 'République dominicaine',
'République populaire démocratique de Corée',
'République-Unie de Tanzanie', 'Rwanda', 'Sao Tomé-et-Principe',
'Sénégal', 'Sierra Leone', 'Somalie', 'Soudan', 'Soudan du Sud',
'Sri Lanka', 'Tadjikistan', 'Tchad', 'Timor-Leste', 'Togo',
'Vanuatu', 'Yémen', 'Zambie', 'Zimbabwe'],
"Code zone (ISO3)":
['AFG', 'DZA', 'AGO', 'BGD', 'BEN', 'BTN', 'BOL', 'BFA', 'BDI',
'KHM', 'CMR', 'COL', 'COM', 'COG', 'CIV', 'CUB', 'DJI', 'EGY',
'SLV', 'ECU', 'ETH', 'GMB', 'GEO', 'GHA', 'GTM', 'GIN', 'GNB',
'HTI', 'HND', 'IRN', 'IRQ', 'JOR', 'KEN', 'KGZ', 'LSO', 'LBN',
'LBR', 'LBY', 'MDG', 'MWI', 'MLI', 'MRT', 'MOZ', 'MMR', 'NPL',
'NIC', 'NER', 'UGA', 'PAK', 'PSE', 'PHL', 'SYR', 'CAF', 'COD',
'LAO', 'DOM', 'PRK', 'TZA', 'RWA', 'STP', 'SEN', 'SLE', 'SOM',
'SDN', 'SSD', 'LKA', 'TJK', 'TCD', 'TLS', 'TGO', 'VUT', 'YEM',
'ZMB', 'ZWE'],
"Product":
['Autres non-céréales', 'Blé et Farin', 'Céréales',
'Fruits secs, total', 'Huiles végétales',
'Légumineuses Sèches,Tot.', 'Non-céréales', 'Riz, total',
'Sucre, total', 'Céréales Secondaires', 'Lait, total',
'Mélanges et préparations', 'Poiss&produi', 'BulgurFarEnt',
'Viande Total', 'Graisses Com'],
"Year":
['2013', '2014', '2015', '2016'],
"Quantity of donated products": np.random.randint(90, 40000, 500)
}
# fmt: on
# this fails !!!
# df = pd.DataFrame(data, columns = ["Country helped", "Code zone (ISO3)",
# "Product", "Année", "Quantity of donated products"])
# provided data is ragged, different lengths for each column, make all arrays same length for data frame
df = (
pd.DataFrame(
{
k: data[k] if len(data[k]) == c else np.random.choice(data[k], c)
for k in data.keys()
if (c := max({k: len(data[k]) for k in data.keys()}.values()))
}
)
.drop(columns=["Country helped"])
.merge(
pd.DataFrame(
{
k: v
for k, v in data.items()
if k in ["Country helped", "Code zone (ISO3)"]
}
),
on="Code zone (ISO3)",
how="inner",
)
)
aggs = [
"count",
"sum",
"avg",
"median",
"mode",
"rms",
"stddev",
"min",
"max",
"first",
"last",
]
def build_fig(df,
aggs=aggs,
analytical_col="Quantity of donated products",
group=["Code zone (ISO3)"],
agg_cols={
"Country helped": ("Country helped", "first"),
"Product": ("Product", lambda s: s.unique().tolist()),
},
):
# make sure argument permutations make sense
assert (len(group) == 1 and isinstance(aggs, list)) or (
len(group) == 2 and isinstance(aggs, str)
)
# need to know countries that make up natural earth...
world = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres"))
# pandas aggregate functions don't map 1:1 to deprecated plotly functions
override = {
"avg": "mean",
"mode": lambda s: s.mode().iloc[0],
"rms": lambda s: math.sqrt((s ** 2).sum()),
"stddev": "std",
}
# create dataframe with all wanted aggregates as columns, plus keep country name and list of products
dfa = df.groupby(group, as_index=False).agg(
**{
**{
agg: (
analytical_col,
agg if agg not in override.keys() else override[agg],
)
for agg in (aggs if isinstance(aggs, list) else [aggs])
},
**agg_cols,
}
)
# generate trace of missing countries to be included where necessary
def missing(values):
return (
px.choropleth(
world.loc[~world["iso_a3"].isin(values), "iso_a3"],
locations="iso_a3",
)
.update_traces(
name="missing",
colorscale=[[0, "red"], [1, "red"]],
showlegend=False,
hovertemplate="missing: %{location}",
)
.data
)
# create a trace for each required aggregate
if len(group) == 1:
fig = go.Figure(
[
t.update(name=agg, visible=(isinstance(aggs, str) or aggs[0] == agg))
for agg in (aggs if isinstance(aggs, list) else [aggs])
for t in px.choropleth(
dfa, locations=group[0], color=agg, hover_data=list(agg_cols.keys())
).data
]
)
# add the missing layer
fig.add_traces(missing(fig.data[0]["locations"]))
# add the drop down menu...
fig.update_layout(
updatemenus=[
{
"buttons": [
{
"label": agg,
"method": "restyle",
"args": [
{
"visible": [
t.name == agg or t.name == "missing"
for t in fig.data
]
}
],
}
for agg in aggs
]
}
]
)
# generate an animation, with only one aggregation
else:
fig = px.choropleth(
dfa,
locations=group[0],
color=aggs,
animation_frame=group[1],
hover_data=list(agg_cols.keys()),
)
# update each of the animation frames with missing countries
for fr in fig.frames:
fr.update(data=[fr.data[0], missing(fr.data[0]["locations"])[0]])
# re-construct the figure...
fig = go.Figure(data=fig.frames[0].data, layout=fig.layout, frames=fig.frames)
return fig