Use of map function in pandas

Question

I'm trying to replicate this analysis for my city. I'm at the step where I copied and pasted the function to compute daily hours of daylight and add the aggregate count (weekly and daily) to the grouped dataset and then plot.

The thing is that I believe that the map function used here behave strangely (or something has changed along the pandas version). If I give for example weekly.head() the program returns in the hours_of_daylight column this output while I am expecting a number

            Total       East     West         daylight
Date                
2012-10-07  14292.0     7297.0   6995.0       <map object at 0x7f21afcc1ca0>
2012-10-14  16795.0     8679.0   8116.0       <map object at 0x7f21afcc1ca0>
2012-10-21  15509.0     7946.0   7563.0       <map object at 0x7f21afcc1ca0>
2012-10-28  13437.0     6901.0   6536.0       <map object at 0x7f21afcc1ca0>
2012-11-04  12194.0     6408.0   5786.0       <map object at 0x7f21afcc1ca0>

Of course, after this matplotlib says that there is not any numeric data to plot

The code is

daily = data.resample('d').sum()
weekly = data.resample('w').sum()


def hours_of_daylight(date, axis=23.44, latitude=47.61):
    """Compute the hours of daylight for the given date"""
    diff = date - pd.datetime(2000, 12, 21)
    day = diff.total_seconds() / 24. / 3600
    day %= 365.25
    m = 1. - np.tan(np.radians(latitude)) * np.tan(np.radians(axis) * np.cos(day * np.pi / 182.625))
    m = max(0, min(m, 2))
    return 24. * np.degrees(np.arccos(1 - m)) / 180.

# add this to our weekly data
weekly['daylight'] = map(hours_of_daylight, weekly.index)
daily['daylight'] = map(hours_of_daylight, daily.index)

weekly.head()

and, for the plot

weekly['daylight'].plot()
plt.ylabel('hours of daylight (Seattle)');

Can you help me understand what is wrong with these lines of code and get the effective number as result as well as the chart?

map is lazy in Python-3.x, so you do not evaluate the map object (unless forced to do so). — willeM_ Van Onsem
– willeM_ Van Onsem, Commented May 2, 2020 at 10:14

jezrael · Accepted Answer · 2020-05-02 10:09:35Z

2

Use Index.map:

weekly['daylight'] = weekly.index.map(hours_of_daylight)
print (weekly)
              Total    East    West   daylight
Date                                          
2012-10-07  14292.0  7297.0  6995.0  11.045208
2012-10-14  16795.0  8679.0  8116.0  10.644852
2012-10-21  15509.0  7946.0  7563.0  10.255305
2012-10-28  13437.0  6901.0  6536.0   9.881095
2012-11-04  12194.0  6408.0  5786.0   9.527645

daily['daylight'] = daily.index.map(hours_of_daylight)

answered May 2, 2020 at 10:09

jezrael

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3 Comments

Pablohoney Over a year ago

Ok this one worked; can you tell me the difference between your code and mine? is it because of some deprecated pandas syntax?

jezrael Over a year ago

@Ale - No, because map used in your solution is pure python map function and in pandas it is index.map function. It is not same.

jezrael Over a year ago

@Ale - Also is used Series.map for working with Series or columns.

Ahsan Khan · Accepted Answer · 2020-05-02 12:22:22Z

1

You can either try the .filter() function by providing specific details in the bracket or you can use the index.map() to get a structured DF. I personally would recommend to go for .filter()

answered May 2, 2020 at 12:22

Ahsan Khan

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