TypeError：ufunc'add'不包含签名匹配类型为dtype（'的循环

Question

I am trying to normalize energy use data using weather data with Pandas. I need my code to read a csv with weather data, calculate some numbers using that data, and sum up those numbers based on the month of the year. Here is my code so far:

    import pandas as pd
    import statsmodels.api as sm
    import statsmodels.formula.api as smf

    data = pd.read_csv("C:\\Users\\mparlo\\Documents\\Python\\NEWYORK - NEWYORK.csv", header=None)
    data.columns = ["Month", "Day", "Year", "Temperature"]

    ndays = len(data)
    data["hdd"] = ""
    data["cdd"] = ""

    t_bp = 65

    for i in range(0,ndays):
        if data.at[i,"Temperature"] > t_bp:
            data.at[i,"hdd"] = 0
            data.at[i,"cdd"] = data.at[i,"Temperature"]-t_bp
        elif data.at[i,"Temperature"] < t_bp:
            data.at[i,"hdd"] = t_bp - data.at[i,"Temperature"]
            data.at[i,"cdd"] = 0

    data

    hddjan = data.loc[data["Month"] == 1, "hdd"].sum()
    cddjan = data.loc[data["Month"] == 1, "cdd"].sum()

    hddfeb = data.loc[data["Month"] == 2, "hdd"].sum()
    cddfeb = data.loc[data["Month"] == 2, "cdd"].sum()

    hddmar = data.loc[data["Month"] == 3, "hdd"].sum()
    cddmar = data.loc[data["Month"] == 3, "cdd"].sum()

    hddapr = data.loc[data["Month"] == 4, "hdd"].sum()

The data is formatted such that Months are numbered 1-12.

The code works up until the last line here, where I try to sum anything past Month 3/March. I get this error:

> ---------------------------------------------------------------------------
>TypeError                                 Traceback (most recent call last)
>~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\nanops.py in 
>f(values, axis, skipna, **kwds)
>    118                 else:
>--> 119                     result = alt(values, axis=axis, skipna=skipna, >**kwds)
>    120             except Exception:
>
>~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\nanops.py in >nansum(values, axis, skipna)
>    292         dtype_sum = np.float64
>--> 293     the_sum = values.sum(axis, dtype=dtype_sum)
>    294     the_sum = _maybe_null_out(the_sum, axis, mask)
>
>~\AppData\Local\Continuum\anaconda3\lib\site-packages\numpy\core\_methods.py >in _sum(a, axis, dtype, out, keepdims)
>     31 def _sum(a, axis=None, dtype=None, out=None, keepdims=False):
>---> 32     return umr_sum(a, axis, dtype, out, keepdims)
>     33 
>
>TypeError: ufunc 'add' did not contain a loop with signature matching types >dtype('<U32') dtype('<U32') dtype('<U32')
>
>During handling of the above exception, another exception occurred:
>
>TypeError                                 Traceback (most recent call last)
><ipython-input-5-beeced82f47d> in <module>()
>      8 cddmar = data.loc[data["Month"] == 3, "cdd"].sum()
>      9 
>---> 10 hddapr = data.loc[data["Month"] == 4, "hdd"].sum()
>
>~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\generic.py >in stat_func(self, axis, skipna, level, numeric_only, **kwargs)
>   6340                                       skipna=skipna)
>   6341         return self._reduce(f, name, axis=axis, skipna=skipna,
>-> 6342                             numeric_only=numeric_only)
>   6343 
>   6344     return set_function_name(stat_func, name, cls)
>
>~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\series.py in >_reduce(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)
>   2379                                           'numeric_only.'.format(name))
>   2380             with np.errstate(all='ignore'):
>-> 2381                 return op(delegate, skipna=skipna, **kwds)
>   2382 
>  2383         return delegate._reduce(op=op, name=name, axis=axis, >skipna=skipna,
>
>~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\nanops.py in >_f(*args, **kwargs)
>     60             try:
>     61                 with np.errstate(invalid='ignore'):
>---> 62                     return f(*args, **kwargs)
>     63             except ValueError as e:
>     64                 # we want to transform an object array
>
>~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\nanops.py in >f(values, axis, skipna, **kwds)
>    120             except Exception:
>    121                 try:
>--> 122                     result = alt(values, axis=axis, skipna=skipna, >**kwds)
>    123                 except ValueError as e:
>    124                     # we want to transform an object array
>
>~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\nanops.py in >nansum(values, axis, skipna)
>    291     elif is_timedelta64_dtype(dtype):
>    292         dtype_sum = np.float64
>--> 293     the_sum = values.sum(axis, dtype=dtype_sum)
>    294     the_sum = _maybe_null_out(the_sum, axis, mask)
>    295 
>
>~\AppData\Local\Continuum\anaconda3\lib\site-packages\numpy\core\_methods.py >in _sum(a, axis, dtype, out, keepdims)
>     30 
>     31 def _sum(a, axis=None, dtype=None, out=None, keepdims=False):
>---> 32     return umr_sum(a, axis, dtype, out, keepdims)
>     33 
>     34 def _prod(a, axis=None, dtype=None, out=None, keepdims=False):
>
>TypeError: ufunc 'add' did not contain a loop with signature matching types >dtype('<U32') dtype('<U32') dtype('<U32')

If anyone has any idea why the sum is working for only the first three months, any help is appreciated.

[EDIT: here is a link to the data: https://docs.google.com/spreadsheets/d/1nUD1wS_ZEWCyjLFdeL14_HaYq6NBjP1VtHN9gvgPD14/edit?usp=sharing ]

Answer 1

While your issue is unclear without data, consider adjusting your code to pandas data manipulation methods such as conditional .loc , groupby , or pivot_table instead of looping through all rows and assigning values by index and manually creating month sums. In fact with an adjusted approach, you will be able to see the extent of your data, if it cuts off after March or not.

...
import calendar

data = pd.read_csv(r'C:\Users\mparlo\Documents\Python\NEWYORK - NEWYORK.csv', header=None)\
                  .set_axis(["Month", "Day", "Year", "Temperature"], axis=1, inplace=False)

t_bp = 65

data.loc[data['Temperature'] > t_bp, 'hdd'] = 0
data.loc[data['Temperature'] <= t_bp, 'hdd'] = t_bp - data['Temperature']

data.loc[data['Temperature'] > t_bp, 'cdd'] = data['Temperature'] - t_bp
data.loc[data['Temperature'] <= t_bp, 'cdd'] = 0

data['Location'] = 'New York'
data['MonthAbbrev'] = data['Month'].apply(lambda x: calendar.month_abbr[x])

# LONG FORMAT
agg_data = data.group_by(['MonthAbbrev']).agg({'hhd':'sum', 'cdd':'sum'})

# WIDE FORMAT
agg_data = data.pivot_table(index=['Location'], values=['hdd', 'cdd'],
                            columns='MonthAbbrev', aggfunc='sum')