python matplotlib 繪圖

參考資料寫在前面：

Python應用matplotlib繪圖簡介

Python matplotlib高級繪圖詳解

matplotlib主頁

需求簡述：從mongodb取出目標數據，按照日期將成都市房屋成交量以折線圖的形式展示；有坐標名稱，有圖例；日期為x軸，成交量為y軸，如果日期過多，則部分均勻展示；新房和二手房分兩個圖展示。

1、日期作為x軸

參考官方示例api example code: date_demo.py

#按照月份展示坐標軸坐標x值，按照日期繪圖，months = mdates.MonthLocator() # every monthdays = mdates.DayLocator() #every daydaysFmt = mdates.DateFormatter(%Y%m%d)# format the ticksax.xaxis.set_major_locator(days) #坐標x軸實際的點ax.xaxis.set_major_formatter(daysFmt)ax.xaxis.set_minor_locator(days) #坐標x軸精簡後的點#x軸字元串旋轉for label in ax.get_xticklabels(): label.set_rotation(30) label.set_horizontalalignment(right)#set min and max x，防止x軸過長，數據展示抱團timemin = x_gaoxin_new[0]timemax = x_gaoxin_new[len(x_gaoxin_new)-1]ax.set_xlim(timemin, timemax)

2、設置坐標軸名稱

#set title of axisax.set_ylabel(u成交量(套)) ax.set_xlabel(u交易日)

這裡遇到一個中文亂碼問題，解決方案：matplotlib畫圖中文亂碼的解決辦法

a.修改matplotlibrc文件。進入Python安裝目錄下的Libsite-packagesmatplotlibmpl-data目錄，打開matplotlibrc文件，刪除font.family和font.sans-serif兩行前的#，並在font.sans-serif後添加微軟雅黑字體（Microsoft YaHei）

b.代碼中使用unicode編碼。ax.set_xlabel(u交易日)

3、設置圖例

最開始直接使用legend，不生效，報錯： UserWarning: Legend does not support [<matplotlib.lines.Line2D object at 0x000000001074E7F0>]Use proxy artist instead.

line1 = ax1.plot(x_gaoxin_new, y_gaoxin_new,rs-)line2 = ax1.plot(x_all_new,y_all_new,go-)...fig.legend((line1, line2), (gaoxin, chengdu), upper left)

參考官方文檔legend_demo更改一種寫法，能正常顯示：

line1 = ax1.plot(x_gaoxin_new, y_gaoxin_new,rs-,label=u高新區)line2 = ax1.plot(x_all_new,y_all_new,go-,label=u成都市)...legend = pl.legend(loc=center, shadow=True, fontsize=x-large)# Put a nicer background color on the legend.legend.get_frame().set_facecolor(#00FFCC)

但是當後面再加一張圖後，這個圖例顯示就會在最後一張圖上顯示，存在一定瑕疵，後面再解決。

4、設置圖名

#設置圖標題ax1.set_title(u商品房今日成交,{},loc=center)ax2.set_title(u二手房今日成交,{},loc=center)

5、一個畫布上畫多張圖

首先創建一個figure對象，向後使用figure.add_axes()方法向figure添加axes即可。

fig = pl.figure()#按照[left,bottom,width,height]新建一個axes到figureax1 = fig.add_axes([0.1, 0.65, 0.85, 0.3])ax2 = fig.add_axes([0.1, 0.15, 0.85, 0.3])line1 = ax1.plot(x_gaoxin_new, y_gaoxin_new,rs-,label=u高新區)line2 = ax1.plot(x_all_new,y_all_new,go-,label=u成都市)line3 = ax2.plot(x_gaoxin_old, y_gaoxin_old,rs-,label=u高新區)line4 = ax2.plot(x_all_old,y_all_old,go-,label=u成都市)

6、效果圖

7、源碼

# --*-- coding: utf-8 --*--__Author__ = "Leng Fuping"__Doc__ = "chart paln sample";import numpy as npimport matplotlib.pyplot as plfrom pymongo import MongoClientimport pymongoimport matplotlib.dates as mdatesimport timeimport datetimeclient = MongoClient("localhost", 27017)db = client.housemarketingcollection = db.housemarketingx_gaoxin_new = []y_gaoxin_new = []x_all_new = []y_all_new = []x_gaoxin_old = []y_gaoxin_old = []x_all_old = []y_all_old = []for i in collection.find({ "type" : "商品房今日成交","dist" : "高新區"}).sort("cdate", pymongo.ASCENDING): x_gaoxin_new.append(datetime.datetime.strptime(i["cdate"], "%Y%m%d").date()) y_gaoxin_new.append(int(i["harea"]))for i in collection.aggregate([{$match :{ "type" : "商品房今日成交"}},{$group:{_id:"$cdate", harea : {$sum:$harea}}}, {$sort : {_id:1}}]): x_all_new.append(datetime.datetime.strptime(i["_id"], "%Y%m%d").date()) y_all_new.append(int(i["harea"]))for i in collection.find({ "type" : "二手房今日成交","dist" : "高新區"}).sort("cdate", pymongo.ASCENDING): x_gaoxin_old.append(datetime.datetime.strptime(i["cdate"], "%Y%m%d").date()) y_gaoxin_old.append(int(i["harea"]))for i in collection.aggregate([{$match :{ "type" : "二手房今日成交"}},{$group:{_id:"$cdate", harea : {$sum:$harea}}}, {$sort : {_id:1}}]): x_all_old.append(datetime.datetime.strptime(i["_id"], "%Y%m%d").date()) y_all_old.append(int(i["harea"])) print(x_gaoxin_new)fig = pl.figure()#按照[left,bottom,width,height]新建一個axes到figureax1 = fig.add_axes([0.1, 0.65, 0.85, 0.3])ax2 = fig.add_axes([0.1, 0.15, 0.85, 0.3])#設置圖標題ax1.set_title(u商品房今日成交,{},loc=center)ax2.set_title(u二手房今日成交,{},loc=center)#使用數據渲染圖line1 = ax1.plot(x_gaoxin_new, y_gaoxin_new,rs-,label=u高新區)line2 = ax1.plot(x_all_new,y_all_new,go-,label=u成都市)line3 = ax2.plot(x_gaoxin_old, y_gaoxin_old,rs-,label=u高新區)line4 = ax2.plot(x_all_old,y_all_old,go-,label=u成都市)#按照月份展示坐標軸坐標x值，按照日期繪圖，months = mdates.MonthLocator() # every monthdays = mdates.DayLocator() #every daydaysFmt = mdates.DateFormatter(%Y%m%d)# format the ticksax1.xaxis.set_major_locator(days)ax1.xaxis.set_major_formatter(daysFmt)ax1.xaxis.set_minor_locator(days)#x軸字元串旋轉for label in ax1.get_xticklabels(): label.set_rotation(30) label.set_horizontalalignment(right)ax2.xaxis.set_major_locator(days)ax2.xaxis.set_major_formatter(daysFmt)ax2.xaxis.set_minor_locator(days)#x軸字元串旋轉for label in ax2.get_xticklabels(): label.set_rotation(30) label.set_horizontalalignment(right)#set min and max xtimemin = x_gaoxin_new[0]timemax = x_gaoxin_new[len(x_gaoxin_new)-1]ax1.set_xlim(timemin, timemax)#set title of axisax1.set_ylabel(u成交量(套)) ax1.set_xlabel(u交易日) ax2.set_ylabel(u成交量(套)) ax2.set_xlabel(u交易日) ax1.format_xdata = mdates.DateFormatter(%Y%m%d)ax1.grid(True)ax2.format_xdata = mdates.DateFormatter(%Y%m%d)ax2.grid(True)#設置和顯示圖例legend = pl.legend(loc=center, shadow=True, fontsize=x-large)# Put a nicer background color on the legend.legend.get_frame().set_facecolor(#00FFCC)pl.show()

8、TODO

a、圖例只能顯示在一個圖中；

b、弄清楚matplotlib的關鍵實體

9、參考資料

matplotlib api

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