【Pandas-Cookbook】05：DataFrame框架案例

# -*-coding:utf-8-*-#  by kevinelstri#  2017.2.17import pandas as pdimport matplotlib.pyplot as pltimport numpy as np# ---------------------# Chapter 5: Combining dataframes and scraping Canadian weather data# ---------------------'''    Summary'''weather_2012_final = pd.read_csv('../data/weather_2012.csv', index_col='Date/Time')print weather_2012_final.head()weather_2012_final['Temp (C)'].plot(figsize=(15, 6))# plt.show()'''    5.1 Downloading one month of weather data'''url_template = "http://climate.weather.gc.ca/climateData/bulkdata_e.html?format=csv&stationID=5415&Year={year}&Month={month}&timeframe=1&submit=Download+Data"url = url_template.format(month=3, year=2012)weather_mar2012 = pd.read_csv(url, skiprows=15, index_col='Date/Time', parse_dates=True, encoding='latin1', header=True)print weather_mar2012weather_mar2012['Temp (C)'].plot(figsize=(15, 5))  # 图形展示温度变化情况plt.show()weather_mar2012.columns = [    u'Year', u'Month', u'Day', u'Time', u'Data Quality', u'Temp (C)',    u'Temp Flag', u'Dew Point Temp (C)', u'Dew Point Temp Flag',    u'Rel Hum (%)', u'Rel Hum Flag', u'Wind Dir (10s deg)', u'Wind Dir Flag',    u'Wind Spd (km/h)', u'Wind Spd Flag', u'Visibility (km)', u'Visibility Flag',    u'Stn Press (kPa)', u'Stn Press Flag', u'Hmdx', u'Hmdx Flag', u'Wind Chill',    u'Wind Chill Flag', u'Weather']weather_mar2012 = weather_mar2012.dropna(axis=1, how='any')  # drop the column if any value is null 删除空列print weather_mar2012[:5]weather_mar2012 = weather_mar2012.drop(['Year', 'Month', 'Day', 'Time', 'Data Quality'], axis=1)print weather_mar2012[:5]'''    5.2 Plotting the temperature by hour of day'''temperatures = weather_mar2012[['Temp (C)']].copy()print temperatures.head()temperatures.loc[:, 'Hour'] = weather_mar2012.index.hourtemperatures.groupby('Hour').aggregate(np.median).plot()plt.show()'''    5.3 Getting the whole year of data'''def download_weather_month(year, month):    if month == 1:        year += 1    url = url_template.format(year=year, month=month)    weather_data = pd.read_csv(url, skiprows=15, index_col='Date/Time', parse_dates=True, header=True)    weather_data = weather_data.dropna(axis=1)    weather_data.columns = [col.replace('\xb0', '') for col in weather_data.columns]    weather_data = weather_data.drop(['Year', 'Day', 'Month', 'Time', 'Data Quality'], axis=1)    return weather_dataprint download_weather_month(2012, 1)[:5]data_by_month = [download_weather_month(2012, i) for i in range(1, 13)]  # 所有月份weather_2012 = pd.concat(data_by_month)print weather_2012'''    5.4 Saving to a CSV'''weather_2012.to_csv('../data/weather_2012.csv')