Subplots in python (kaggle in East-Asia)

World Vs East Asia

---

##python을 이용한 plotly Library로 plot 그리기

subplots 를 이용하여 다중 그래프를 그려 보자.

python Library Import

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pylab as plt

import plotly.io as pio
import plotly.express as px
import plotly.graph_objects as go
import plotly.figure_factory as ff
from plotly.subplots import make_subplots
from plotly.offline import init_notebook_mode, iplot
init_notebook_mode(connected=True)
pio.templates.default = "none"
# import plotly.offline as py
# py.offline.init_notebook_mode()

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

import warnings
warnings.filterwarnings("ignore")

data import

df17= pd.read_csv("/kaggle/input/kaggle-survey-2017/multipleChoiceResponses.csv", encoding="ISO-8859-1")
df18= pd.read_csv("/kaggle/input/kaggle-survey-2018/multipleChoiceResponses.csv", )
df19= pd.read_csv("/kaggle/input/kaggle-survey-2019/multiple_choice_responses.csv", )
df20= pd.read_csv("/kaggle/input/kaggle-survey-2020/kaggle_survey_2020_responses.csv", )
df21= pd.read_csv("/kaggle/input/kaggle-survey-2021/kaggle_survey_2021_responses.csv", )

data frame 전처리

total17 = (
    df17['region']
    .value_counts()
    .to_frame()
    .reset_index()
    .rename(columns={'index':'type', 'region':'respodents'})
    .groupby('type')
    .sum()
    .reset_index() 
)

total18 = (
    df18['region']
    .value_counts()
    .to_frame()
    .reset_index()
    .rename(columns={'index':'type', 'region':'respodents'})
    .groupby('type')
    .sum()
    .reset_index() 
)

total19 = (
    df19['region']
    .value_counts()
    .to_frame()
    .reset_index()
    .rename(columns={'index':'type', 'region':'respodents'})
    .groupby('type')
    .sum()
    .reset_index() 
)

total20 = (
    df20['region']
    .value_counts()
    .to_frame()
    .reset_index()
    .rename(columns={'index':'type', 'region':'respodents'})
    .groupby('type')
    .sum()
    .reset_index() 
)

total21 = (
    df21['region']
    .value_counts()
    .to_frame()
    .reset_index()
    .rename(columns={'index':'type', 'region':'respodents'})
    .groupby('type')
    .sum()
    .reset_index() 
)

plot 그리기

make_subplots 로 여러개의 그래프를 한 plot에 담아 봅시다.

• pie그래프를 연도별로 담아 봅시다.

# Create subplots: use 'domain' type for Pie subplot


##subplots가 담길 행렬을 만들어 줍니다. rows와 cols를 맞춰서 제목도 달아 줍니다. 
fig = make_subplots(rows=1, cols=5, 
                    specs=[[{'type':'domain'}, {'type':'domain'}, {'type':'domain'}, {'type':'domain'}, {'type':'domain'}]],
                   subplot_titles=("2017", "2018", "2019", "2020", "2021"))

## scalegroup = one으로 설정 하게 되면, data의 크기에 따라 pie의 크기가 결정됩니다. 
fig.add_trace(go.Pie(labels=total21['type'],
                     values=total21['respodents'], name="2021", scalegroup='one'),
              1, 1)
fig.add_trace(go.Pie(labels=total20['type'], 
                     values=total20['respodents'], name="2020", scalegroup='one'),
              1, 2)
fig.add_trace(go.Pie(labels=total19['type'], 
                     values=total19['respodents'], name="2019", scalegroup='one'),
              1, 3)
fig.add_trace(go.Pie(labels=total18['type'], 
                     values=total18['respodents'], name="2018", scalegroup='one'),
              1, 4)
fig.add_trace(go.Pie(labels=total17['type'], 
                     values=total17['respodents'], name="2017", scalegroup='one'),
              1, 5)




# Use `hole` to create a donut-like pie chart
fig.update_traces(hole=.2, hoverinfo="label+percent+name")
fig.update_layout(
    title_text="<b>World vs EastAsia</b>",
    # Add annotations in the center of the donut pies.
   )
fig.show()