Python development [module]: CSV file data visualization,

Python development [module]: CSV file data visualization,
CSV Module

1. CSV file format

To store data in a text file, the simplest way is to write data into a file as a series of comma-separated values (CSV). Such a file becomes a CSV file, as shown below:

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, WindDirDegrees2014-7-1,64,56,50,53,51,48,96,83,58,30.19,30.00,29.79,10,10,10,7,4,,0.00,7,,3372014-7-2,71,62,55,55,52,46,96,80,51,29.81,29.75,29.66,10,9,2,13,5,,0.14,7,Rain,3272014-7-3,64,58,53,55,53,51,97,85,72,29.88,29.86,29.81,10,10,8,15,4,,0.01,6,,2582014-7-4,59,56,52,52,51,50,96,88,75,29.91,29.89,29.87,10,9,2,9,2,,0.07,7,Rain,2552014-7-5,69,59,50,52,50,46,96,72,49,29.88,29.82,29.79,10,10,10,13,5,,0.00,6,,1102014-7-6,62,58,55,51,50,46,80,71,58,30.13,30.07,29.89,10,10,10,20,10,29,0.00,6,Rain,2132014-7-7,61,57,55,56,53,51,96,87,75,30.10,30.07,30.05,10,9,4,16,4,25,0.14,8,Rain,2112014-7-8,55,54,53,54,53,51,100,94,86,30.10,30.06,30.04,10,6,2,12,5,23,0.84,8,Rain,1592014-7-9,57,55,53,56,54,52,100,96,83,30.24,30.18,30.11,10,7,2,9,5,,0.13,8,Rain,2012014-7-10,61,56,53,53,52,51,100,90,75,30.23,30.17,30.03,10,8,2,8,3,,0.03,8,Rain,2152014-7-11,57,56,54,56,54,51,100,94,84,30.02,30.00,29.98,10,5,2,12,5,,1.28,8,Rain,2502014-7-12,59,56,55,58,56,55,100,97,93,30.18,30.06,29.99,10,6,2,15,7,26,0.32,8,Rain,2752014-7-13,57,56,55,58,56,55,100,98,94,30.25,30.22,30.18,10,5,1,8,4,,0.29,8,Rain,2912014-7-14,61,58,55,58,56,51,100,94,83,30.24,30.23,30.22,10,7,0,16,4,,0.01,8,Fog,3072014-7-15,64,58,55,53,51,48,93,78,64,30.27,30.25,30.24,10,10,10,17,12,,0.00,6,,3182014-7-16,61,56,52,51,49,47,89,76,64,30.27,30.23,30.16,10,10,10,15,6,,0.00,6,,2942014-7-17,59,55,51,52,50,48,93,84,75,30.16,30.04,29.82,10,10,6,9,3,,0.11,7,Rain,2322014-7-18,63,56,51,54,52,50,100,84,67,29.79,29.69,29.65,10,10,7,10,5,,0.05,6,Rain,2992014-7-19,60,57,54,55,53,51,97,88,75,29.91,29.82,29.68,10,9,2,9,2,,0.00,8,,2922014-7-20,57,55,52,54,52,50,94,89,77,29.92,29.87,29.78,10,8,2,13,4,,0.31,8,Rain,1552014-7-21,69,60,52,53,51,50,97,77,52,29.99,29.88,29.78,10,10,10,13,4,,0.00,5,,2972014-7-22,63,59,55,56,54,52,90,84,77,30.11,30.04,29.99,10,10,10,9,3,,0.00,6,Rain,2402014-7-23,62,58,55,54,52,50,87,80,72,30.10,30.03,29.96,10,10,10,8,3,,0.00,7,,2302014-7-24,59,57,54,54,52,51,94,84,78,29.95,29.91,29.89,10,9,3,17,4,28,0.06,8,Rain,2072014-7-25,57,55,53,55,53,51,100,92,81,29.91,29.87,29.83,10,8,2,13,3,,0.53,8,Rain,1412014-7-26,57,55,53,57,55,54,100,96,93,29.96,29.91,29.87,10,8,1,15,5,24,0.57,8,Rain,2162014-7-27,61,58,55,55,54,53,100,92,78,30.10,30.05,29.97,10,9,2,13,5,,0.30,8,Rain,2132014-7-28,59,56,53,57,54,51,97,94,90,30.06,30.00,29.96,10,8,2,9,3,,0.61,8,Rain,2612014-7-29,61,56,51,54,52,49,96,89,75,30.13,30.02,29.95,10,9,3,14,4,,0.25,6,Rain,1532014-7-30,61,57,54,55,53,52,97,88,78,30.31,30.23,30.14,10,10,8,8,4,,0.08,7,Rain,1602014-7-31,66,58,50,55,52,49,100,86,65,30.31,30.29,30.26,10,9,3,10,4,,0.00,3,,217

Sitka_weather_07-2014.csv

 

2. Use CSV data to draw a temperature chart

① Create highs_lows.py to read the first row of data:

Import csvfilename = 'sitka_weather_07-2014.csv 'with open (filename, 'R') as f: reader = csv. reader (f) # generate a reader. The f object is passed into header_row = next (reader) # view the first row of the file. reader is the iteratable object print (header_row) # List format # ['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', # 'preititationin', 'cloudcover', 'events', 'winddirgrees']

② Modify the highs_lows.py file to obtain the daily maximum temperature.

Import csvfilename = 'sitka_weather_07-2014.csv 'with open (filename, 'R') as f: reader = csv. reader (f) # generate a reader. The f object is passed into header_row = next (reader) # view the first row of the file. reader is the iteratable object 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 Plot the temperature chart based on the data

Import csvimport matplotlib. pyplot as pltfilename = 'sitka_weather_07-2014.csv 'with open (filename, 'R') as f: reader = csv. reader (f) # generate a reader. The f object is passed into header_row = next (reader) # view the first row of the file. reader is the iteratable object highs = [] for row in reader: high = int (row [1]) highs. append (high) # Set the image size fig = plt. figure (dpi = 128, figsize = () plt. plot (highs, c = 'red', linewidth = 1) # Set the color and line width # Set the image format plt. title ('daily high temperatures, July 2014 ', fontsize = 24) # title plt. xlabel ('', fontsize = 14) plt. ylabel ('temperature (F) ', fontsize = 14) plt. show () # output image

Drawing:

④ Change the X axis to the time and date

Import csvimport matplotlib. pyplot as pltfrom datetime import datetimefilename = 'sitka_weather_07-2014.csv 'with open (filename, 'R') as f: reader = csv. reader (f) # generate reader. f object is passed into header_row = next (reader) # view the first row of the file. reader is an iteratable object 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) # Set the image size fig = plt. figure (dpi = 128, figsize = () plt. plot (dates, highs, c = 'red', linewidth = 1) # linewidth determines the width of the drawn line # sets the image format plt. title ('daily high temperatures, July 2014 ', fontsize = 20) # title plt. xlabel ('', fontsize = 14) fig. autofmt_xdate () # convert the date tag to the Italic plt. ylabel ('temperature (F) ', fontsize = 14) plt. tick_params (axis = 'both ', which = 'major') plt. show () # output image

Drawing:

⑤ Add low temperature data and fill the line area

Import csvimport matplotlib. pyplot as pltfrom datetime import datetimefilename = 'sitka_weather_2014.csv 'with open (filename, 'R') as f: reader = csv. reader (f) # generate a reader. The f object is passed into header_row = next (reader) # view the first row of the file. reader is an iteratable object # obtain the date, maximum temperature, and minimum temperature of 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]) Comment t ValueError: print (current_date, 'missing data') else: dates. append (current_date) highs. append (high) lows. append (low) # Set the image size fig = plt. figure (dpi = 128, figsize = () plt. plot (dates, highs, c = 'red', alpha = 0.5) # maximum temperature alpha transparency 0 completely transparent, 1 indicates completely opaque plt. plot (dates, lows, c = 'Blue ', alpha = 0.5) # minimum temperature plt. fill_between (dates, highs, lows, facecolor = 'blue', alpha = 0.1) # fill color # Set the image format plt. title ('daily high temperatures-2014 ', fontsize = 20) # title plt. xlabel ('', fontsize = 14) fig. autofmt_xdate () # convert the date tag to the Italic plt. ylabel ('temperature (F) ', fontsize = 14) plt. tick_params (axis = 'both ', which = 'major') plt. show () # output image

Drawing: