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An NOAA dataset has been stored in the file data/C2A2_data/BinnedCsvs_d400/fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89.csv. The data for this assignment comes from a subset of The National Centers for Environmental Information (NCEI) Daily Global Historical Climatology Network (GHCN-Daily). The GHCN-Daily is comprised of daily climate records from thousands of land surface stations across the globe.
Each row in the assignment datafile corresponds to a single observation.
The following variables are provided to you:
For this assignment, you must:
The data you have been given is near Ann Arbor, Michigan, United States, and the stations the data comes from are shown on the map below.
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import matplotlib.pyplot as plt
import mplleaflet
import pandas as pd
import numpy as np
def leaflet_plot_stations(binsize, hashid):
df = pd.read_csv('data/C2A2_data/BinSize_d{}.csv'.format(binsize))
station_locations_by_hash = df[df['hash'] == hashid]
lons = station_locations_by_hash['LONGITUDE'].tolist()
lats = station_locations_by_hash['LATITUDE'].tolist()
plt.figure(figsize=(8,8))
plt.scatter(lons, lats, c='r', alpha=0.7, s=200)
return mplleaflet.display()
leaflet_plot_stations(400,'fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89')
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In [2]:
df=pd.read_csv('data/C2A2_data/BinnedCsvs_d400/fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89.csv')
# Take a look at the df
df.info()
df.head()
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In [3]:
# We can see above the 'Date' is an object not a datetime, convert below
df['Date'] = pd.to_datetime(df['Date'], yearfirst = True)
df.info()
In [4]:
dfmax = df.pivot_table(index='Date', columns='Element', aggfunc='max')
dfmax.drop('ID', 1, inplace=True)
dfmax.columns = dfmax.columns.droplevel()
dfmax.drop('TMIN', 1, inplace=True)
dfmax.head()
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In [5]:
dfmin = df.pivot_table(index='Date', columns='Element', aggfunc='min')
dfmin.drop('ID', 1, inplace=True)
dfmin.columns = dfmin.columns.droplevel()
dfmin.drop('TMAX', 1, inplace=True)
dfmin.head()
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In [6]:
# 2015 Record Data
df3 = pd.concat([dfmax, dfmin], axis=1)
df2015 = df3[3652:]
df2015['TMAX'] = df2015['TMAX']/10
df2015['TMIN'] = df2015['TMIN']/10
df2015['TMAX15'] = df2015['TMAX']
df2015['TMIN15'] = df2015['TMIN']
df2015.drop('TMAX', 1, inplace=True)
df2015.drop('TMIN', 1, inplace=True)
df2015 = df2015.reset_index()
df2015.head()
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In [7]:
import datetime
dfhist = df3[:-365]
dfhist = dfhist.reset_index()
dfhist['Date'] = dfhist['Date'].apply(lambda x: x.strftime('%m-%d-00'))
dfhist['Date'] = pd.to_datetime(dfhist['Date'])
# Max
dfhistmax = dfhist.drop('TMIN', 1)
dfhistmax = dfhistmax.pivot_table(values='TMAX', index=['Date'], aggfunc='max')
dfhistmax = dfhistmax.to_frame()
# Min
dfhistmin = dfhist.drop('TMAX', 1)
dfhistmin = dfhistmin.pivot_table(values='TMIN', index=['Date'], aggfunc='min')
dfhistmin = dfhistmin.to_frame()
print(dfhistmin.head())
print(dfhistmax.head())
In [8]:
dfhistc = pd.concat([dfhistmax, dfhistmin], axis=1)
dfhistc = dfhistc.reset_index()
dfhistc['Date'] = dfhistc['Date'].apply(lambda x: x.strftime('%m-%d'))
dfhistc.set_index('Date', inplace=True)
dfhistc.drop(dfhistc.index[59], inplace=True)
dfhistc = dfhistc.reset_index()
dfhistc['TMAX'] = dfhistc['TMAX']/10
dfhistc['TMIN'] = dfhistc['TMIN']/10
dfhistc.info()
In [9]:
# Merge historical and 2015 dfs
dfhistc = pd.concat([dfhistc, df2015], axis=1)
#dfhistc['2015Max'] = "NaN"
dfhistc['2015Max'] = np.where((dfhistc['TMAX15'] > dfhistc['TMAX']), dfhistc['TMAX15'], np.nan)
dfhistc['2015Min'] = np.where((dfhistc['TMIN15'] < dfhistc['TMIN']), dfhistc['TMIN15'], np.nan)
dfhistc.head()
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In [10]:
%matplotlib notebook
plt.figure()
# plot the linear data and the exponential data
plt.plot(dfhistc['TMAX'], '-r', dfhistc['TMIN'], '-b')
# Fill
plt.gca().fill_between(range(len(dfhistc)),
dfhistc['TMAX'], dfhistc['TMIN'],
facecolor='blue',
alpha=0.20)
# plot points where 2015 broke records
plt.plot(dfhistc['2015Max'], 'm^', dfhistc['2015Min'], 'c^', ms=6.0)
# Beautify & Lables
ax = plt.gca()
ax.set_xlabel('Days of the Year', alpha=0.7)
ax.set_ylabel('degrees celsius', alpha=0.7)
ax.set_title('Historical Max. & Min. Temperatures Overlayed with 2015 Records', alpha=0.7)
# Remove ticks
plt.tick_params(top='off', bottom='off', left='off', right='off')
# remove the frame of the chart
plt.gca().spines['right'].set_visible(False)
plt.gca().spines['top'].set_visible(False)