Yosuke Tsuchiya - Fab Futures - Data Science

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Jupyter Tutorial

This is a tutorial book of Jupyter and Python.

Python Sample

Here is the first code of Python

print("Hello, World")

Hello, World

a = 1
b = 10

c = a + b

ほら、こんなに簡単にmarkdownが書けるよ

CSVを読み込むやり方

もっとも手っ取り早く、データ分析の用途の場合はPandaを使うのが速いし楽

import pandas as pd

# df: DataFrameの略
df = pd.read_csv('./datasets/sdg_index_2000-2022.csv')

df

	country_code	country	year	sdg_index_score	goal_1_score	goal_2_score	goal_3_score	goal_4_score	goal_5_score	goal_6_score	...	goal_8_score	goal_9_score	goal_10_score	goal_11_score	goal_12_score	goal_13_score	goal_14_score	goal_15_score	goal_16_score	goal_17_score
0	AFG	Afghanistan	2000	36.0	28.8	27.3	19.2	1.6	20.8	32.4	...	38.5	5.2	0.0	25.8	94.7	99.4	0.0	51.9	39.2	34.2
1	AFG	Afghanistan	2001	36.3	28.8	30.6	19.4	1.6	20.8	32.4	...	38.5	5.2	0.0	25.8	94.5	99.4	0.0	51.9	39.2	34.2
2	AFG	Afghanistan	2002	36.3	28.8	30.7	19.7	1.6	20.8	32.7	...	38.4	5.2	0.0	26.1	94.1	99.4	0.0	51.8	39.2	34.2
3	AFG	Afghanistan	2003	36.7	28.8	32.5	19.9	1.6	20.8	33.0	...	38.4	5.2	0.0	26.5	94.4	99.4	0.0	51.8	39.2	34.2
4	AFG	Afghanistan	2004	37.1	28.8	32.1	21.1	1.6	20.8	33.3	...	38.5	5.2	0.0	26.8	94.8	99.4	0.0	51.8	39.2	34.2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4135	ZWE	Zimbabwe	2018	55.9	26.4	46.6	39.8	57.9	76.7	51.6	...	63.8	23.5	32.4	79.2	94.6	94.1	0.0	76.4	45.5	45.6
4136	ZWE	Zimbabwe	2019	53.7	21.4	46.0	40.6	60.6	77.0	51.3	...	62.8	23.8	24.0	58.6	94.6	94.2	0.0	76.4	44.0	44.5
4137	ZWE	Zimbabwe	2020	54.3	20.1	47.2	41.5	62.0	77.0	51.1	...	62.9	25.4	24.0	60.0	94.6	94.3	0.0	76.4	44.6	46.0
4138	ZWE	Zimbabwe	2021	54.8	20.8	48.4	41.9	62.5	77.1	51.1	...	63.1	27.0	24.0	61.2	94.5	94.3	0.0	77.9	43.1	48.1
4139	ZWE	Zimbabwe	2022	55.6	21.5	48.5	41.9	62.5	77.2	51.1	...	63.2	27.0	24.0	65.7	94.5	94.3	0.0	77.9	40.5	47.8

4140 rows × 21 columns

古典的というかスタンダードなやり方

地道ファイルを開いて読み込む方法ですが、めんどい。

import csv

with open('./datasets/sdg_index_2000-2022.csv') as f:
    reader = csv.reader(f)
    rows = [row for row in reader]

for item in rows:
    print(item[1])
    

Matplotlibで可視化

import matplotlib.pyplot as plt

jpn = df.query('country == "Japan"')
jpn

	country_code	country	year	sdg_index_score	goal_1_score	goal_2_score	goal_3_score	goal_4_score	goal_5_score	goal_6_score	...	goal_8_score	goal_9_score	goal_10_score	goal_11_score	goal_12_score	goal_13_score	goal_14_score	goal_15_score	goal_16_score	goal_17_score
1840	JPN	Japan	2000	75.3	99.7	73.2	87.5	95.6	53.1	83.6	...	85.9	78.7	77.8	89.4	58.1	69.5	52.6	58.0	85.3	62.2
1841	JPN	Japan	2001	75.6	99.7	72.7	89.1	95.6	53.2	83.9	...	85.8	80.0	77.8	89.4	58.6	69.8	53.0	58.0	85.3	62.5
1842	JPN	Japan	2002	75.7	99.7	72.6	89.6	95.6	53.3	84.2	...	85.6	81.7	77.8	89.4	58.6	69.4	53.4	57.9	85.3	62.6
1843	JPN	Japan	2003	75.8	99.7	70.9	90.0	95.6	53.3	84.2	...	85.7	82.5	77.8	89.4	58.6	69.3	55.2	57.7	85.3	62.8
1844	JPN	Japan	2004	76.1	99.7	72.5	90.4	95.6	53.4	84.3	...	86.0	84.9	77.8	89.4	58.7	69.4	55.5	57.8	85.5	62.8
1845	JPN	Japan	2005	76.5	99.7	73.2	91.6	95.6	54.6	84.3	...	86.1	87.0	77.8	89.4	58.9	69.3	55.4	58.6	85.7	62.9
1846	JPN	Japan	2006	76.6	99.7	72.5	91.8	95.6	54.9	84.4	...	86.3	87.8	77.8	89.4	59.1	69.6	56.0	58.8	85.9	62.8
1847	JPN	Japan	2007	76.7	99.7	73.3	91.9	95.6	54.9	84.4	...	86.4	88.3	77.8	88.3	59.6	69.2	55.7	58.7	86.9	63.0
1848	JPN	Japan	2008	76.6	99.7	73.7	91.4	95.6	54.9	84.5	...	86.3	88.3	77.8	84.5	59.7	70.1	55.5	58.7	87.0	63.4
1849	JPN	Japan	2009	76.9	99.7	72.8	91.2	95.6	55.9	84.6	...	85.8	88.8	77.8	85.6	60.6	71.0	54.9	58.7	88.2	64.6
1850	JPN	Japan	2010	77.1	99.7	72.4	92.6	95.6	55.9	84.6	...	85.7	89.8	81.6	86.2	60.1	70.4	53.3	59.0	87.2	65.4
1851	JPN	Japan	2011	77.4	99.6	73.3	93.1	95.6	56.2	84.7	...	86.0	92.4	81.6	86.7	59.5	68.1	53.9	59.1	87.5	67.7
1852	JPN	Japan	2012	77.6	99.4	74.4	93.0	95.6	55.1	84.7	...	86.1	92.5	81.6	87.1	59.8	67.5	54.3	60.6	88.8	68.1
1853	JPN	Japan	2013	77.7	99.3	74.4	92.9	95.6	57.2	84.7	...	86.3	94.3	80.5	83.8	60.2	67.3	56.9	63.0	84.8	68.1
1854	JPN	Japan	2014	77.7	99.3	74.8	92.9	95.1	57.7	84.8	...	86.6	94.3	80.5	85.2	60.3	67.3	54.9	63.2	84.7	68.0
1855	JPN	Japan	2015	78.1	99.3	74.9	93.5	94.9	58.4	84.8	...	88.0	93.8	80.5	82.5	60.9	70.5	54.0	63.3	88.8	67.6
1856	JPN	Japan	2016	78.0	99.3	74.6	93.7	94.5	58.8	84.8	...	88.2	92.0	80.5	85.1	60.7	70.5	52.9	63.3	88.6	67.2
1857	JPN	Japan	2017	78.1	99.3	74.5	93.9	94.1	59.7	84.9	...	88.2	93.9	80.5	83.4	60.8	71.3	53.1	63.3	87.3	67.7
1858	JPN	Japan	2018	78.5	99.3	74.6	93.8	94.0	60.3	84.9	...	88.7	94.0	80.5	83.5	60.9	72.6	53.2	63.7	87.6	70.6
1859	JPN	Japan	2019	78.8	99.3	74.8	94.0	94.2	60.9	84.9	...	88.3	94.5	80.5	84.8	60.9	73.1	55.6	63.5	86.9	70.9
1860	JPN	Japan	2020	79.0	99.3	74.6	94.4	93.9	60.9	84.9	...	87.8	94.6	80.5	85.8	60.9	73.9	55.7	63.3	87.1	73.0
1861	JPN	Japan	2021	79.5	99.3	75.2	94.4	93.9	61.2	84.9	...	88.1	94.4	80.5	86.9	66.1	73.5	55.8	63.5	87.4	74.3
1862	JPN	Japan	2022	79.4	99.3	75.2	94.6	93.9	61.7	84.9	...	88.2	94.4	80.5	84.2	66.1	73.5	55.8	63.1	86.3	75.6

23 rows × 21 columns

plt.figure(figsize=(10,6))
chn = df.query('country == "China"')

plt.plot(jpn['year'],jpn['sdg_index_score'],label="SDGs Score in Japan",color='black')
plt.plot(chn['year'],chn['sdg_index_score'],label="SDGs Score in China",color='red')


plt.ylim(bottom=-0.1)
plt.ylim(top=100)
#plt.tight_layout()
plt.show()