Plotting Temperatures

I recently completed the 2nd assignment in the Coursera course. I had so much fun and had to post it on my blog! Ha!

We were tasked to analyze an 11-year data set which contains the maximum and minimum temperatures for everyday from 2005 to 2015. 2005 to 2014 data was sorted and 2015 data was set aside. The maximum and minimum temperatures per day were plotted. Feb 29 data was removed in the data to keep it clean. Finally, 2015 data was processed and temperature outliers were highlighted.

The following libraries were used.
import pandas as pd import numpy as np import matplotlib.pyplot as plt import matplotlib.patches as mpatches

The data set came from 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. 

• The original dataframe has 165085 rows and 4 columns and was assigned to the variable df.
• The 'Data_Value' column is in tenths of degrees C.

In [8]: df = pd.read_csv('course2data.csv') In [9]: df.head() Out[9]: ID Date Element Data_Value 0 USW00094889 11/12/2014 TMAX 22 1 USC00208972 4/29/2009 TMIN 56 2 USC00200032 5/26/2008 TMAX 278 3 USC00205563 11/11/2005 TMAX 139 4 USC00200230 2/27/2014 TMAX -106

Additional columns were added to df for further processing.

• February 29 from leap years were also removed at this stage. 
• 'temp_in_C' was used to convert 'Data_Value' to Celcius. Spyder was able to process the /10 operation but Jupyter kept on crashing. One of the mentors advised that the division operation consumes alot of memory thus causing Jupyter notebook to crash. I opted to use *.10 instead.

#convert date column to real date format df["Date"] = pd.to_datetime(df["Date"]) #spread out month, day and year date_index=pd.DatetimeIndex(df['Date']) df['month']=date_index.month df['day']=date_index.day df['year']=date_index.year df['dayofyear']=date_index.dayofyear # remove feb 29 from dataframe df=df[~((df.month==2) & (df.day ==29))] #add column and convert Temp to celcius #used *.1 instead of /10. division utilizes most of the memory. df['temp_in_C']=df['Data_Value']*.1

Created more dataframes! More fun! At this stage, the maximum and minimum temperatures per day were identified. For example, all temps for November 24 of 2005 to 2014 were collected and the highest (or lowest) temperature was selected and placed to the new dataframe.

• I felt comfortable using pivot_table but groupby function may be used as well.
• 2015 was excluded in the dataframe. We needed to identify which 2015 temperature exceeded the 2005 to 2014 data.

#create df without 2015 data df_data=df[~(df.year==2015)] #create df for TMAX Elements and get max value df_tmax = df_data[df_data.Element=='TMAX'] tmax = pd.pivot_table(df_tmax,index='dayofyear',values=['temp_in_C'],aggfunc={'temp_in_C':'max'}) #create df for TMIN Elements and get min value df_tmin = df_data[df_data.Element=='TMIN'] tmin=pd.pivot_table(df_tmin,index='dayofyear',values=['temp_in_C'],aggfunc={'temp_in_C':'min'}) #create 2015 df and respective tmax and tmin df's df_2015=df[(df.year==2015)] df_2015_tmax = df_2015[df_2015.Element=='TMAX'] tmax_2015=pd.pivot_table(df_2015_tmax,index='dayofyear',values=['temp_in_C'],aggfunc={'temp_in_C':'max'}) df_2015_tmin = df_2015[df_2015.Element=='TMIN'] tmin_2015=pd.pivot_table(df_2015_tmin,index='dayofyear',values=['temp_in_C'],aggfunc={'temp_in_C':'min'}) #create df to show data values that exceed the 2005 to 2014 data tmin_exceed=tmin_2015[tmin_2015.temp_in_C < tmin.temp_in_C.loc[:len(tmin_2015)]] tmax_exceed=tmax_2015[tmax_2015.temp_in_C > tmax.temp_in_C.loc[:len(tmax_2015)]]

Plotting! I no matplotlib expert (yet) but thanks to stackoverflow, I survived. Yey! I did try to minimize the noise to put more emphasis on the data requirement.
#create figure and axes fig = plt.figure() fig, ax = plt.subplots() plt.plot(tmin.temp_in_C,color="white") plt.plot(tmax.temp_in_C,color="white") plt.plot(tmin_exceed.temp_in_C,".",color="blue") plt.plot(tmax_exceed.temp_in_C,".",color="red") # set y axis with degrees celcius softened label by turning to grey ax.yaxis.set_major_formatter(plt.FuncFormatter('{:.0f}$^\circ$C'.format)) plt.yticks(alpha=0.8) #set the range of xticks soften labels by turning to grey plt.xticks(range(0, 366, 30), alpha=0.8) plt.xlabel('Day of Year', alpha=0.8) #set figure title plt.title('Daily High and Low temperatures from 2005-2014 and record breaks of 2015 data.\n',alpha=0.8) # fill the area between the tmax annd tmin data plt.fill_between(range(len(tmax)),tmax.temp_in_C,tmin.temp_in_C,facecolor='blue',alpha=0.25) # remove the frame of the chart for spine in plt.gca().spines.values(): spine.set_visible(False) # remove the ticks (both axes) plt.tick_params(top='off', bottom='on', left='on', right='off', labelleft='on', labelbottom='on') #set up legends red_patch = mpatches.Patch(color='red', label='2015 high temp') blue_patch = mpatches.Patch(color='blue', label='2015 low temp') shade_patch = mpatches.Patch(color='blue', label='2005 to 2014 temp',alpha=0.25) plt.legend(handles=[shade_patch,red_patch,blue_patch],loc=4,frameon=False)

Finally, here is the output.
plt.show()

The complete code may be found here.

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