This is a short exercise to give you some practice with NumPy and Pandas.
In a Python module named water_temps.py, load the data contained in the file south-atlantic-water-temps.csv into a Pandas DataFrame named water_temps. Use the first column in the data file as the index (row keys) for the data frame.
Add a column to the water_temps DataFrame with the name AVG and whose values are the average temperature for each location.
Add a row to the water_temps DataFrame named Average whose values contain the average temperatures for each month.
Assign the variable coldest_jan_temp the value of the lowest temperature in JAN.
Assign to the variable warmest_nov a Series with the Locations and associated NOV temperatures that are the highest (there will be two). You’ll need to fiddle with this one.
Assign to the variable warm_spots a DataFrame with all the rows from water_temps whose average water emperatures are greater than 70.
After doing these steps you should have something like this (after from water_temps import *):
In [12]: from water_temps import *
In [13]: pd.set_option('display.width', 120) # Display wider DataFrames without wrapping
In [14]: water_temps
Out[14]:
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC AVG
Location
Chesapeake Bay VA 46 42 44 65 72 78 81 81 79 71 56 49 63.666667
Kiptopeke VA 36 39 46 65 72 78 81 81 79 71 54 44 62.166667
... additional rows elided
In [15]: coldest_jan_temp
Out[15]: 36
In [16]: warmest_nov
Out[16]:
Location
Miami Beach FL 76
Virginia Key FL 76
Name: NOV, dtype: int64
In [19]: warm_spots
Out[19]:
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC AVG
Location
Daytona Beach FL 61 59 65 65 72 78 81 81 79 71 71 65 70.666667
Miami Beach FL 71 73 75 65 72 78 81 81 79 71 76 73 74.583333
Stuart Beach FL 67 66 70 65 72 78 81 81 79 71 75 70 72.916667
Virginia Key FL 71 72 74 65 72 78 81 81 79 71 76 73 74.416667