如何根据另一个 dataframe 中的条件计算一个 dataframe 中的值

Question

I have two dataframes. df1 shows annual rainfall over a certain area:

df1:

longitude latitude year           
-13.0     8.0      1979  15.449341
                   1980  21.970507
                   1981  18.114307
                   1982  16.881737
                   1983  24.122467
                   1984  27.108953
                   1985  27.401234
                   1986  18.238272
                   1987  25.421076
                   1988  11.796293
                   1989  17.778618
                   1990  18.095036
                   1991  20.414757
        

and df2 shows the upper limits of each bin:

   bin limits
0   16.655970
1   18.204842
2   19.526524
3   20.852657
4   22.336731
5   24.211905
6   27.143820

I'm trying to add a new column to df2 that shows the frequency of rainfall events from df1 in their corresponding bin. For example, in bin 1 I'd be looking for the values in df1 that fall between 16.65 and 18.2.

I've tried the following:

rain = df1['tp1']
for i in range 7:
    limit = df2.iloc[i]
    out4['count']=rain[rain>limit].count()

However, I get the following message:

ValueError: Can only compare identically-labeled Series objects

Which I think is referring to the fact that I'm comparing two df's that are different sizes? I'm also unsure if that loop is correct or not.

Any help is much appreciated, thanks!

Answer 1

Use pd.cut to assign your rainfall into bins:

# Define the limits for your bins
# Bin 0: (-np.inf  , 16.655970]
# Bin 1: (16.655970, 18.204842]
# Bin 2: (18.204842, 19.526524]
# ...
# note that your bins only go up to 27.14 while max rainfall is 27.4 (row 6).
# You may need to add / adjust your limits.
limits = [-np.inf] + df2["limits"].to_list()

# Assign the rainfall to each bin
bins = pd.cut(df1["rainfall"], limits, labels=df2["bin"])

# Count how many values fall into each bin
bins.value_counts(sort=False).rename_axis("bin")