Python มีไลบรารี Data Visualization ที่ใช้ง่าย รองรับข้อมูลน้อยหรือมาก:
- Matplotlib: พื้นฐาน ควบคุมรายละเอียดได้หมด
- Pandas: Plot ตรงจาก DataFrame
- Plotly: Interactive, web-based
- Seaborn: Statistical visualization สวยงาม
ข้อมูลตัวอย่าง: ประชากรอินเดีย 2019 แยกตาม Variant
| Country | Year | Variant | Value |
|---|---|---|---|
| India | 2019 | Medium | 1,368,737.513 |
| India | 2019 | High | 1,378,419.072 |
| India | 2019 | Low | 1,359,043.965 |
| India | 2019 | Constant fertility | 1,373,707.838 |
| India | 2019 | Instant replacement | 1,366,687.871 |
| India | 2019 | Zero migration | 1,370,868.782 |
| India | 2019 | Constant mortality | 1,366,282.778 |
| India | 2019 | No change | 1,371,221.64 |
| India | 2019 | Momentum | 1,367,400.614 |
กราฟพื้นฐาน
Line Plot
import matplotlib.pyplot as plt
Year = [2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019]
India_Population = [1173108018,1189172906,1205073612,1220800359,1266344631,1309053980,1324171354,1339180127,1354051854,1368737513]
plt.plot(Year, India_Population)
plt.show()
Scatter Plot
plt.plot(Year, India_Population, 'o') # เพิ่ม 'o' เพื่อทำ scatter
plt.show()
Histogram
import pandas as pd
import matplotlib.pyplot as plt
data = [
['India', 2019, 'Medium', 1368737.513],
['India', 2019, 'High', 1378419.072],
['India', 2019, 'Low', 1359043.965],
['India', 2019, 'Constant fertility', 1373707.838],
['India', 2019, 'Instant replacement', 1366687.871],
['India', 2019, 'Zero migration', 1370868.782],
['India', 2019, 'Constant mortality', 1366282.778],
['India', 2019, 'No change', 1371221.64],
['India', 2019, 'Momentum', 1367400.614],
]
df = pd.DataFrame(data, columns=['Country or Area', 'Year(s)', 'Variant', 'Value'])
df.hist()
plt.show()
กราฟพิเศษ
Pie Chart
import numpy as np
import matplotlib.pyplot as plt
n = 25
Z = np.ones(n)
Z[-1] *= 2.5
plt.axes([0.05, 0.05, 0.95, 0.95])
plt.pie(Z, explode=Z*.05, colors=['%f' % (i/float(n)) for i in range(n)],
wedgeprops={"linewidth": 1, "edgecolor": "green"})
plt.gca().set_aspect('equal')
plt.xticks([]), plt.yticks([])
plt.show()
Polar Plot
import numpy as np
import matplotlib.pyplot as plt
ax = plt.axes([0.5,0.05,0.95,0.95], polar=True)
N = 25
theta = np.arange(0.0, 2.5*np.pi, 2.5*np.pi/N)
radii = 10*np.random.rand(N)
width = np.pi/4*np.random.rand(N)
bars = plt.bar(theta, radii, width=width, bottom=0.0)
for r,bar in zip(radii, bars):
bar.set_facecolor(plt.cm.jet(r/10.))
bar.set_alpha(0.5)
ax.set_xticklabels([])
ax.set_yticklabels([])
plt.show()
คำแนะนำเลือกไลบรารี
- เริ่มต้น: Matplotlib + Pandas built-in plot
- Interactive Dashboard: Plotly / Bokeh
- Statistical plots: Seaborn
- Geospatial: Folium / GeoPandas
- 3D/Complex: Mayavi / PyVista
