初探 Data Analysis - Matplotlib

前言

本篇聚焦 Matplotlib 核心 APIplt.plot()之應用：涵蓋 折線圖（Line Plot）的時間序列趨勢解析，以及散佈圖（Scatter Plot）**的資料分佈與樣式調校實戰。從 StackExchange 取得每種程式語言的貼文總數作為實作資料來源。

matplotlib.pyplot.plot()語法與參數說明

plt.plot(x, y, fmt, data=None, **kwargs)

測試資料來源

透過在 StackExchange 上執行 SQL 查詢來取得每種程式語言的貼文總數資料

點擊此連結從 [StackExchange](https://data.stackexchange.com/stackoverflow/query/675441/popular-programming-languages-per-over-time-eversql-com) 執行查詢以取得您自己的 .csv 文件

畫單條線

xpoints = np.array([1, 8]) ypoints = np.array([3, 10]) # 繪製兩個座標點,而不是一條線,可以使用 o 參數,表示一個實心圈的標記 plt.plot(xpoints, ypoints, 'o') plt.show()

執行結果​​​​​​

# 繪製一條不規則線 xpoints = np.array([1, 2, 6, 8]) ypoints = np.array([3, 8, 1, 10]) # marker 可以定義的符號 plt.plot(xpoints, ypoints,marker = '*') plt.show()

執行結果

範例 programming_posts.csv

Date,Language,Posts
"2008-07-01 00:00:00","c#","3"
"2008-08-01 00:00:00","assembly","8"
"2008-08-01 00:00:00","c","81"
"2008-08-01 00:00:00","c++","164"
"2008-08-01 00:00:00","java","220"
...
...
"2026-07-01 00:00:00","go","4"
"2026-07-01 00:00:00","java","70"
"2026-07-01 00:00:00","javascript","73"
"2026-07-01 00:00:00","perl","6"
"2026-07-01 00:00:00","php","27"
"2026-07-01 00:00:00","python","142"
"2026-07-01 00:00:00","r","54"
"2026-07-01 00:00:00","ruby","2"
"2026-07-01 00:00:00","swift","17"

實作

讀取CSV檔案

import pandas as pd import matplotlib.pyplot as plt df = pd.read_csv(csv_path, names=['DATE', 'LANG', 'POSTS'], header=0) print(f"df.shape: {df.shape}, df.head()顯示前 5 列資料 :\n{df.head()}")

% python3 programming_posts.py
df.shape: (2999, 3), df.head()顯示前 5 列資料 :
DATE LANG POSTS
0 2008-07-01 00:00:00 c# 3
1 2008-08-01 00:00:00 assembly 8
2 2008-08-01 00:00:00 c 81
3 2008-08-01 00:00:00 c# 503
4 2008-08-01 00:00:00 c++ 164

# 日期轉換 df["DATE"] = pd.to_datetime( df["DATE"] ) # 把 Language 變成 Columns pivot_df = df.pivot( index="DATE", columns="LANG", values="POSTS" ) # fillna() 用常數 0 填入所有缺失值.傳回一個新的 DataFrame(如果 inplace=False),或 None(如果 inplace=True) pivot_df.fillna( 0, inplace=True ) # 繪製折線圖 plt.plot(pivot_df.index, pivot_df.java, label="java") plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.xlabel('DATE', fontsize=14) plt.ylabel('Number of Posts', fontsize=14) plt.ylim(0, 35000) plt.legend(fontsize=14) plt.show()

執行結果​​​​​​

# 繪製各語言貼文折線圖 plt.figure(figsize=(15,8)) for column in pivot_df.columns: plt.plot( pivot_df.index, pivot_df[column], label=column ) plt.legend() plt.title("Programming Language Trend") plt.xlabel("Date") plt.ylabel("Posts") plt.show()

執行結果​​​​​​

# Rolling Mean 三個月平均 rolling_df = pivot_df.rolling( window=3 ).mean() plt.figure(figsize=(18,9)) for column in rolling_df.columns: plt.plot( rolling_df.index, rolling_df[column], linewidth=3, label=column ) plt.legend() plt.grid() plt.xlabel("Year") plt.ylabel("Average Posts") plt.title("3-Month Rolling Average") plt.tight_layout() plt.show()

執行結果​​​​​​

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