量化比較：散佈圖

Matplotlib 資料視覺化入門

Ariel Rokem

Data Scientist

認識散佈圖

fig, ax = plt.subplots()

ax.scatter(climate_change["co2"], climate_change["relative_temp"])

ax.set_xlabel("CO2 (ppm)")
ax.set_ylabel("Relative temperature (Celsius)")
plt.show()

自訂散佈圖

eighties = climate_change["1980-01-01":"1989-12-31"]
nineties = climate_change["1990-01-01":"1999-12-31"]

fig, ax = plt.subplots()

ax.scatter(eighties["co2"], eighties["relative_temp"],
           color="red", label="eighties")

ax.scatter(nineties["co2"], nineties["relative_temp"],
           color="blue", label="nineties")

ax.legend()

ax.set_xlabel("CO2 (ppm)")
ax.set_ylabel("Relative temperature (Celsius)")

plt.show()

用顏色編碼比較

用顏色編碼第三變數

fig, ax = plt.subplots()

ax.scatter(climate_change["co2"], climate_change["relative_temp"],
           c=climate_change.index)

ax.set_xlabel("CO2 (ppm)")
ax.set_ylabel("Relative temperature (Celsius)")
plt.show()

以顏色表達時間

一起來練習吧！

Matplotlib 資料視覺化入門