Python开发【模块】：CSV文件 数据可视化

CSV模块

1、CSV文件格式

　　要在文本文件中存储数据，最简单的方式是讲数据作为一系列逗号分隔的值（CSV）写入文件，这样的文件成为CSV文件，如下：

AKDT,Max TemperatureF,Mean TemperatureF,Min TemperatureF,Max Dew PointF,MeanDew PointF,Min DewpointF,Max Humidity, Mean Humidity, Min Humidity, Max Sea Level PressureIn, Mean Sea Level PressureIn, Min Sea Level PressureIn, Max VisibilityMiles, Mean VisibilityMiles, Min VisibilityMiles, Max Wind SpeedMPH, Mean Wind SpeedMPH, Max Gust SpeedMPH,PrecipitationIn, CloudCover, Events, WindDirDegrees
2014-7-1,64,56,50,53,51,48,96,83,58,30.19,30.00,29.79,10,10,10,7,4,,0.00,7,,337
2014-7-2,71,62,55,55,52,46,96,80,51,29.81,29.75,29.66,10,9,2,13,5,,0.14,7,Rain,327
2014-7-3,64,58,53,55,53,51,97,85,72,29.88,29.86,29.81,10,10,8,15,4,,0.01,6,,258
2014-7-4,59,56,52,52,51,50,96,88,75,29.91,29.89,29.87,10,9,2,9,2,,0.07,7,Rain,255
2014-7-5,69,59,50,52,50,46,96,72,49,29.88,29.82,29.79,10,10,10,13,5,,0.00,6,,110
2014-7-6,62,58,55,51,50,46,80,71,58,30.13,30.07,29.89,10,10,10,20,10,29,0.00,6,Rain,213
2014-7-7,61,57,55,56,53,51,96,87,75,30.10,30.07,30.05,10,9,4,16,4,25,0.14,8,Rain,211
2014-7-8,55,54,53,54,53,51,100,94,86,30.10,30.06,30.04,10,6,2,12,5,23,0.84,8,Rain,159
2014-7-9,57,55,53,56,54,52,100,96,83,30.24,30.18,30.11,10,7,2,9,5,,0.13,8,Rain,201
2014-7-10,61,56,53,53,52,51,100,90,75,30.23,30.17,30.03,10,8,2,8,3,,0.03,8,Rain,215
2014-7-11,57,56,54,56,54,51,100,94,84,30.02,30.00,29.98,10,5,2,12,5,,1.28,8,Rain,250
2014-7-12,59,56,55,58,56,55,100,97,93,30.18,30.06,29.99,10,6,2,15,7,26,0.32,8,Rain,275
2014-7-13,57,56,55,58,56,55,100,98,94,30.25,30.22,30.18,10,5,1,8,4,,0.29,8,Rain,291
2014-7-14,61,58,55,58,56,51,100,94,83,30.24,30.23,30.22,10,7,0,16,4,,0.01,8,Fog,307
2014-7-15,64,58,55,53,51,48,93,78,64,30.27,30.25,30.24,10,10,10,17,12,,0.00,6,,318
2014-7-16,61,56,52,51,49,47,89,76,64,30.27,30.23,30.16,10,10,10,15,6,,0.00,6,,294
2014-7-17,59,55,51,52,50,48,93,84,75,30.16,30.04,29.82,10,10,6,9,3,,0.11,7,Rain,232
2014-7-18,63,56,51,54,52,50,100,84,67,29.79,29.69,29.65,10,10,7,10,5,,0.05,6,Rain,299
2014-7-19,60,57,54,55,53,51,97,88,75,29.91,29.82,29.68,10,9,2,9,2,,0.00,8,,292
2014-7-20,57,55,52,54,52,50,94,89,77,29.92,29.87,29.78,10,8,2,13,4,,0.31,8,Rain,155
2014-7-21,69,60,52,53,51,50,97,77,52,29.99,29.88,29.78,10,10,10,13,4,,0.00,5,,297
2014-7-22,63,59,55,56,54,52,90,84,77,30.11,30.04,29.99,10,10,10,9,3,,0.00,6,Rain,240
2014-7-23,62,58,55,54,52,50,87,80,72,30.10,30.03,29.96,10,10,10,8,3,,0.00,7,,230
2014-7-24,59,57,54,54,52,51,94,84,78,29.95,29.91,29.89,10,9,3,17,4,28,0.06,8,Rain,207
2014-7-25,57,55,53,55,53,51,100,92,81,29.91,29.87,29.83,10,8,2,13,3,,0.53,8,Rain,141
2014-7-26,57,55,53,57,55,54,100,96,93,29.96,29.91,29.87,10,8,1,15,5,24,0.57,8,Rain,216
2014-7-27,61,58,55,55,54,53,100,92,78,30.10,30.05,29.97,10,9,2,13,5,,0.30,8,Rain,213
2014-7-28,59,56,53,57,54,51,97,94,90,30.06,30.00,29.96,10,8,2,9,3,,0.61,8,Rain,261
2014-7-29,61,56,51,54,52,49,96,89,75,30.13,30.02,29.95,10,9,3,14,4,,0.25,6,Rain,153
2014-7-30,61,57,54,55,53,52,97,88,78,30.31,30.23,30.14,10,10,8,8,4,,0.08,7,Rain,160
2014-7-31,66,58,50,55,52,49,100,86,65,30.31,30.29,30.26,10,9,3,10,4,,0.00,3,,217

sitka_weather_07-2014.csv

2、取CSV数据绘制气温图表

① 创建highs_lows.py读取数据第一行:

import csv

filename = 'sitka_weather_07-2014.csv'
with open(filename,'r') as f:
    reader = csv.reader(f)      # 生成阅读器，f对象传入
    header_row = next(reader)   # 查看文件第一行，reader是可迭代对象
    print(header_row)           # 列表格式

# ['AKDT', 'Max TemperatureF', 'Mean TemperatureF', 'Min TemperatureF',
# 'Max Dew PointF', 'MeanDew PointF', 'Min DewpointF', 'Max Humidity',
# ' Mean Humidity', ' Min Humidity', ' Max Sea Level PressureIn',
# ' Mean Sea Level PressureIn', ' Min Sea Level PressureIn',
# ' Max VisibilityMiles', ' Mean VisibilityMiles', ' Min VisibilityMiles',
#  ' Max Wind SpeedMPH', ' Mean Wind SpeedMPH', ' Max Gust SpeedMPH',
# 'PrecipitationIn', ' CloudCover', ' Events', ' WindDirDegrees']

② 修改highs_lows.py文件获取每日最高温度

import csv

filename = 'sitka_weather_07-2014.csv'
with open(filename,'r') as f:
    reader = csv.reader(f)      # 生成阅读器，f对象传入
    header_row = next(reader)   # 查看文件第一行，reader是可迭代对象

    highs = []
    for row in reader:
        high = int(row[1])
        highs.append(high)
    print(highs)

# [64, 71, 64, 59, 69, 62, 61, 55, 57, 61, 57, 59, 57, 61,
# 64, 61, 59, 63, 60, 57, 69, 63, 62, 59, 57, 57, 61, 59, 61,61, 66]

③ 根据数据绘制气温图表

import csv
import matplotlib.pyplot as plt

filename = 'sitka_weather_07-2014.csv'
with open(filename,'r') as f:
    reader = csv.reader(f)      # 生成阅读器，f对象传入
    header_row = next(reader)   # 查看文件第一行，reader是可迭代对象

    highs = []
    for row in reader:
        high = int(row[1])
        highs.append(high)

    # 设置图片大小
    fig = plt.figure(dpi=128,figsize=(10,6))
    plt.plot(highs, c='red',linewidth=1)        # 设置颜色、线条粗细

    # 设置图片格式
    plt.title('Daily high temperatures,July 2014', fontsize=24)  # 标题
    plt.xlabel('', fontsize=14)
    plt.ylabel('Temperature(F)', fontsize=14)

    plt.show()  # 输出图像

绘图：

④ X轴改为时间日期

import csv
import matplotlib.pyplot as plt
from datetime import datetime

filename = 'sitka_weather_07-2014.csv'
with open(filename,'r') as f:
    reader = csv.reader(f)      # 生成阅读器，f对象传入
    header_row = next(reader)   # 查看文件第一行，reader是可迭代对象

    dates,highs = [],[]
    for row in reader:
        current_date = datetime.strptime(row[0],'%Y-%m-%d')
        dates.append(current_date)
        high = int(row[1])
        highs.append(high)

    # 设置图片大小
    fig = plt.figure(dpi=128,figsize=(10,6))
    plt.plot(dates,highs, c='red',linewidth=1)        # linewidth决定绘制线条的粗细

    # 设置图片格式
    plt.title('Daily high temperatures,July 2014', fontsize=20)  # 标题
    plt.xlabel('', fontsize=14)
    fig.autofmt_xdate()             # 日期标签转为斜体
    plt.ylabel('Temperature(F)', fontsize=14)
    plt.tick_params(axis='both',which='major')
    plt.show()  # 输出图像

绘图：

⑤ 添加低温数据，填充折线区域

import csv
import matplotlib.pyplot as plt
from datetime import datetime

filename = 'sitka_weather_2014.csv'
with open(filename,'r') as f:
    reader = csv.reader(f)      # 生成阅读器，f对象传入
    header_row = next(reader)   # 查看文件第一行，reader是可迭代对象

    # 获取日期，最高温度，最低温度
    dates,highs,lows = [],[],[]
    for row in reader:
        try:
            current_date = datetime.strptime(row[0],'%Y-%m-%d')
            high = int(row[1])
            low = int(row[3])
        except ValueError:
            print(current_date,'missing data')
        else:
            dates.append(current_date)
            highs.append(high)
            lows.append(low)

    # 设置图片大小
    fig = plt.figure(dpi=128,figsize=(10,6))
    plt.plot(dates,highs, c='red',alpha=0.5)         # 最高温度 alpha透明度0完全透明，1表示完全不透明
    plt.plot(dates,lows, c='blue',alpha=0.5)        # 最低温度
    plt.fill_between(dates,highs,lows,facecolor='blue',alpha=0.1) #填充色

    # 设置图片格式
    plt.title('Daily high temperatures - 2014', fontsize=20)  # 标题
    plt.xlabel('', fontsize=14)
    fig.autofmt_xdate()             # 日期标签转为斜体
    plt.ylabel('Temperature(F)', fontsize=14)
    plt.tick_params(axis='both',which='major')
    plt.show()  # 输出图像

绘图：