An NOAA dataset has been stored in the file data/C2A2_data/BinnedCsvs_d400/fb441e62df2d58994 This is the dataset to use for this assignment. Note: 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:
- id : station identification code
- date : date in YYYY-MM-DD format (e.g. 2012-01-24 = January 24, 2012)
- element : indicator of element type
- TMAX : Maximum temperature (tenths of degrees C)
- TMIN : Minimum temperature (tenths of degrees C)
- value : data value for element (tenths of degrees C)
For this assignment, you must:
- Read the documentation and familiarize yourself with the dataset, then write some python code which returns a line graph of the record high and record low temperatures by day of the year over the period 2005-2014. The area between the record high and record low temperatures for each day should be shaded.
- Overlay a scatter of the 2015 data for any points (highs and lows) for which the ten year record (2005-2014) record high or record low was broken in 2015.
- Watch out for leap days (i.e. February 29th), it is reasonable to remove these points from the dataset for the purpose of this visualization.
- Make the visual nice! Leverage principles from the first module in this course when developing your solution. Consider issues such as legends, labels, and chart junk.
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.
In [1]: import matplotlib.pyplot as plt
import mplleaflet import pandas as pd
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,’fb441e62df2d58994928907a91895ec62c2c42e6cd075c27
Out[1]: <IPython.core.display.HTML object>
In [2]: import matplotlib.pyplot as plt
#import mplleaflet import pandas as pd from datetime import date import numpy as np
df = pd.read_csv(‘data/C2A2_data/BinnedCsvs_d400/fb441e62df2d58994928907a91 df.head()
Out[2]: ID Date Element Data_Value
- USW00094889 2014-11-12 TMAX 22
- USC00208972 2009-04-29 TMIN 56
- USC00200032 2008-05-26 TMAX 278
- USC00205563 2005-11-11 TMAX 139
- USC00200230 2014-02-27 TMAX -106
In [3]: years=pd.DatetimeIndex(df[‘Date’]).year months=pd.DatetimeIndex(df[‘Date’]).month days=pd.DatetimeIndex(df[‘Date’]).day df[‘Year’]=years df[‘Month’]=months df[‘Day’]=days df[‘Data_Value’]=df[‘Data_Value’]/10 df.drop([‘ID’,’Date’],1, inplace=True) df.head()
Out[3]: Element Data_Value Year Month Day
- TMAX 2 2014 11 12
- TMIN 6 2009 4 29
- TMAX 8 2008 5 26
- TMAX 9 2005 11 11
- TMAX -10.6 2014 2 27
In [4]: df1=df[(df[‘Month’]!=2) & (df[‘Day’]!=29)]
df2=df1[df1[‘Year’]<2015]
df_2015=df[df[‘Year’]>=2015]
df2
#df.drop(leap_year.index)
Out[4]: Element Data_Value Year Month Day
0 TMAX 2.2 2014 11 12
- TMAX 8 2008 5 26
- TMAX 9 2005 11 11
5 TMAX 19.4 2010 10 1
- TMAX 9 2005 10 4
- TMIN -1.6 2007 12 14
- TMAX 2 2011 4 21
- TMAX 1 2013 1 16
- TMIN 7 2008 10 17
- TMAX 3 2006 5 14
- TMAX 2 2006 5 14
- TMAX 7 2014 12 7
- TMAX 0 2008 9 7
- TMIN 7 2006 4 22
20 TMIN -7.8 2011 3 28
24 TMIN 10.0 2012 3 20
- TMAX 3 2006 5 11
- TMAX 1 2012 3 31
- TMAX 3 2010 7 25
- TMIN 7 2014 12 9
- TMIN 4 2012 3 20
- TMIN 1 2007 8 4
- TMIN 2 2010 7 24
- TMAX 7 2013 8 23
- TMAX 6 2008 5 26
- TMIN 0 2005 8 6
- TMIN -2.8 2010 1 19
- TMIN 9 2012 6 26
- TMIN 4 2010 10 26
- TMIN 0 2014 11 12
… … … … … …
- TMIN 8 2010 6 17
- TMIN 7 2007 4 25
- TMAX 1 2012 7 31
| 165050 | TMAX | 1.7 2011 | 12 | 8 |
| 165051 | TMIN | 10.0 2008 | 9 | 18 |
| 165052 | TMIN | 5.0 2008 | 11 | 3 |
| 165053 | TMAX | 28.3 2011 | 6 | 27 |
| 165055 | TMAX | 11.1 2009 | 10 | 9 |
| 165057 | TMAX | 10.0 2009 | 11 | 24 |
| 165058 | TMAX | 9.4 2010 | 3 | 22 |
| 165060 | TMAX | 28.3 2010 | 5 | 23 |
| 165061 | TMIN | -3.2 2012 | 12 | 26 |
| 165063 | TMIN | 13.3 2010 | 5 | 23 |
| 165064 | TMIN | 17.2 2008 | 8 | 4 |
| 165065 | TMAX | 1.7 2006 | 3 | 1 |
| 165066 | TMAX | 30.6 2008 | 8 | 4 |
| 165067 | TMAX | 1.7 2005 | 12 | 31 |
| 165068 | TMAX | -3.9 2005 | 12 | 20 |
| 165069 | TMIN | 4.4 2011 | 3 | 18 |
| 165070 | TMIN | 2.8 2011 | 11 | 26 |
| 165071 | TMAX | 29.4 2010 | 6 | 19 |
| 165073 | TMAX | 22.2 2005 | 5 | 13 |
| 165074 | TMAX | 26.1 2009 | 7 | 9 |
| 165075 | TMIN | 10.0 2014 | 10 | 3 |
| 165077 | TMIN | 17.2 2014 | 7 | 14 |
| 165078 | TMIN | 14.4 2011 | 6 | 27 |
| 165079 | TMIN | -6.7 2005 | 3 | 2 |
| 165081 | TMAX | 16.7 2009 | 10 | 6 |
| 165082 | TMAX | 28.3 2014 | 7 | 14 |
| 165084 | TMIN | 11.1 2006 | 9 | 4 |
| [135204 rows x 5 columns] | ||||
| In [5]: df_min=df2[df2[‘Element’]==’TMIN’].groupby([‘Month’,’Day’]).aggregate({‘Dat df_max=df2[df2[‘Element’]==’TMAX’].groupby([‘Month’,’Day’]).aggregate({‘Dat df_min_2015=df_2015[df_2015[‘Element’]==’TMIN’].groupby([‘Month’,’Day’]).ag df_max_2015=df_2015[df_2015[‘Element’]==’TMAX’].groupby([‘Month’,’Day’]).ag df_maxOut[5]: Month Day Data_Value0 1 1 15.61 1 2 13.92 1 3 13.33 1 4 10.64 1 5 12.85 1 6 18.96 1 7 21.77 1 8 19.48 1 9 17.89 1 10 10.010 1 11 15.6 | ||||
- 1 12 1
- 1 13 7
- 1 14 0
- 1 15 7
- 1 16 4
- 1 17 3
- 1 18 2
- 1 19 6
- 1 20 3
- 1 21 3
- 1 22 7
- 1 23 8
- 1 24 7
- 1 25 0
- 1 26 9
- 1 27 8
- 1 28 2
- 1 30 3
- 1 31 4
.. … … …
- 12 1 3
- 12 2 6
- 12 3 3
- 12 4 3
- 12 5 2
- 12 6 8
- 12 7 3
- 12 8 2
- 12 9 3
- 12 10 1
- 12 11 8
- 12 12 3
- 12 13 1
- 12 14 9
- 12 15 0
- 12 16 9
- 12 17 4
- 12 18 6
- 12 19 2
- 12 20 3
- 12 21 6
- 12 22 3
- 12 23 3
- 12 24 9
- 12 25 0
- 12 26 6
- 12 27 9
- 12 28 4
| 324 12 30 11.7325 12 31 13.9[326 rows x 3 columns]In [6]: #df_brokenRecord_min =df_min_2015[df_min_2015[‘Data_Value’] > (df_min_2015[ df3_min=pd.merge(df_min, df_min_2015, how=’inner’, on=[‘Month’,’Day’]) df3_max=pd.merge(df_max, df_max_2015, how=’inner’, on=[‘Month’,’Day’]) df_brokenRecord_min=df3_min[df3_min[‘Data_Value_x’]>df3_min[‘Data_Value_y’ df_brokenRecord_max=df3_max[df3_max[‘Data_Value_x’]<df3_max[‘Data_Value_y’ df3_minOut[6]: Month Day Data_Value_x Data_Value_y | |||
| 0 1 | 1 | -16.0 | -13.3 |
| 1 1 | 2 | -26.7 | -12.2 |
| 2 1 | 3 | -26.7 | -6.7 |
| 3 1 | 4 | -26.1 | -8.8 |
| 4 1 | 5 | -15.0 | -15.5 |
| 5 1 | 6 | -26.6 | -18.2 |
| 6 1 | 7 | -30.6 | -18.2 |
| 7 1 | 8 | -29.4 | -21.1 |
| 8 1 | 9 | -27.8 | -20.6 |
| 9 1 | 10 | -25.6 | -20.6 |
| 10 1 | 11 | -18.3 | -20.0 |
| 11 1 | 12 | -19.3 | -11.7 |
| 12 1 | 13 | -25.0 | -21.6 |
| 13 1 | 14 | -26.6 | -24.4 |
| 14 1 | 15 | -27.2 | -20.0 |
| 15 1 | 16 | -29.4 | -16.7 |
| 16 1 | 17 | -29.4 | -11.7 |
| 17 1 | 18 | -28.9 | -10.0 |
| 18 1 | 19 | -30.0 | -1.7 |
| 19 1 | 20 | -23.9 | -3.3 |
| 20 1 | 21 | -26.0 | -6.1 |
| 21 1 | 22 | -27.7 | -6.7 |
| 22 1 | 23 | -25.0 | -10.0 |
| 23 1 | 24 | -26.7 | -6.1 |
| 24 1 | 25 | -24.3 | -8.8 |
| 25 1 | 26 | -23.8 | -15.0 |
| 26 1 | 27 | -23.9 | -16.1 |
| 27 1 | 28 | -29.4 | -17.2 |
| 28 1 | 30 | -23.3 | -14.3 |
| 29 1 | 31 | -19.4 | -15.6 |
| .. … … | … | … | |
| 296 12 1 | -13.2 | -2.8 | |
| 297 12 2 | -13.3 | -6.1 | |
| 298 12 3 | -10.0 | -7.8 | |
| 299 12 4 | -12.2 | -4.3 | |
- 12 5 -15.5 -5.0
- 12 6 -18.3 -5.6
- 12 7 -19.4 -6.7
- 12 8 -20.0 -6.7
- 12 9 -18.9 -3.3
- 12 10 -17.2 -4.4
- 12 11 -16.7 0
- 12 12 -21.0 8
- 12 13 -17.8 7
- 12 14 -16.1 1
- 12 15 -16.6 9
- 12 16 -22.8 6
- 12 17 -22.2 -1.1
- 12 18 -19.4 -5.0
- 12 19 -16.1 -6.7
- 12 20 -16.7 -9.4
- 12 21 -19.4 -8.3
- 12 22 -20.0 6
- 12 23 -20.0 0
- 12 24 -16.7 0
- 12 25 -16.7 -3.2
- 12 26 -15.6 -3.9
- 12 27 -13.8 -0.6
- 12 28 -16.6 -3.9
- 12 30 -14.4 -2.2
- 12 31 -15.0 -5.6
- rows x 4 columns]
In [7]: mins_values=df_min[‘Data_Value’].tolist()
mins_months=df_min[‘Month’].tolist()
mins_days=df_min[‘Day’].tolist()
mins_axis=[]
maxs_values=df_max[‘Data_Value’].tolist()
maxs_months=df_max[‘Month’].tolist()
maxs_days=df_max[‘Day’].tolist()
maxs_axis=[]
for i in range(len(mins_values)):
mins_axis.append((date(2015,mins_months[i],mins_days[i] ) – date(2015,
for i in range(len(maxs_values)):
maxs_axis.append((date(2015,maxs_months[i],maxs_days[i] ) – date(2015,
In [8]: df_brokenRecord_min.drop([‘Data_Value_x’],1, inplace=True)
df_brokenRecord_max.drop([‘Data_Value_x’],1, inplace=True) df_brokenRecord_min.rename(columns={‘Data_Value_y’: ‘Data_Value’}, inplace df_brokenRecord_max.rename(columns={‘Data_Value_y’: ‘Data_Value’}, inplace /opt/conda/lib/python3.6/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarn
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/i if __name__ == ‘__main__’:
/opt/conda/lib/python3.6/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarn
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/i from ipykernel import kernelapp as app
/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py:2834: SettingWithCopyWa
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/i **kwargs)
In [9]: from datetime import date mins_brokenRecord_values=df_brokenRecord_min[‘Data_Value’].tolist() mins_brokenRecord_months=df_brokenRecord_min[‘Month’].tolist() mins_brokenRecord_days=df_brokenRecord_min[‘Day’].tolist() mins_brokenRecord_axis=[]
maxs_brokenRecord_values=df_brokenRecord_max[‘Data_Value’].tolist() maxs_brokenRecord_months=df_brokenRecord_max[‘Month’].tolist() maxs_brokenRecord_days=df_brokenRecord_max[‘Day’].tolist() maxs_brokenRecord_axis=[]
for i in range(len(mins_brokenRecord_values)): mins_brokenRecord_axis.append((date(2015,mins_brokenRecord_months[i],mi
for i in range(len(maxs_brokenRecord_values)):
maxs_brokenRecord_axis.append((date(2015,maxs_brokenRecord_months[i],ma
In [10]: plt.figure(figsize=(10,8)) colors = [‘green’, ‘red’]
plt.plot(mins_axis,mins_values, c=’green’, alpha = 0.3, label = ‘Minimum T plt.plot(maxs_axis,maxs_values, c =’red’, alpha = 0.3, label = ‘Maximum Te plt.scatter(mins_brokenRecord_axis, mins_brokenRecord_values, s = 8, c = plt.scatter(maxs_brokenRecord_axis, maxs_brokenRecord_values, s = 8, c = plt.fill_between(mins_axis, mins_values, maxs_values, facecolor=’grey’, al plt.ylim(-45, 60)
plt.legend(loc =’best’, frameon=False,fontsize=10)
plt.xticks( np.linspace(0, 30*11 , num = 12), (r’Jan’, r’Feb’, r’Mar’, r’A plt.xlabel(‘Months’,fontsize=12) plt.ylabel(‘Temperature (tenths of degrees C’) plt.title(‘2015 temperature broke records vs (2005-2014) temperature recor plt.show()
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