I have two DataFrames as follows:
df_discount = pd.DataFrame(data={'Graduation' : np.arange(0,1000,100), 'Discount %' : np.arange(0,50,5)})
df_values = pd.DataFrame(data={'Sum' : [20,801,972,1061,1251]})
Now my goal is to get a new column df_values['New Sum'] for my df_values that applies the corresponding discount to df_values['Sum'] based on the value of df_discount['Graduation']. If the Sum is >= the Graduation the corresponding discount is applied.
Examples: Sum 801 should get a discount of 40% resulting in 480.6, Sum 1061 gets 45% resulting in 583.55.
I know I could write a funtion with if else conditions and the returning values. However, is there a better way to do this if you have very many different conditions?
You could try if pd.merge_asof()
works for you:
df_discount = pd.DataFrame({
'Graduation': np.arange(0, 1000, 100), 'Discount %': np.arange(0, 50, 5)
})
df_values = pd.DataFrame({'Sum': [20, 100, 101, 350, 801, 972, 1061, 1251]})
df_values = (
pd.merge_asof(
df_values, df_discount,
left_on="Sum", right_on="Graduation",
direction="backward"
)
.assign(New_Sum=lambda df: df["Sum"] * (1 - df["Discount %"] / 100))
.drop(columns=["Graduation", "Discount %"])
)
Result (without the last .drop(columns=...)
to see what's happening):
Sum Graduation Discount % New_Sum
0 20 0 0 20.00
1 100 100 5 95.00
2 101 100 5 95.95
3 350 300 15 297.50
4 801 800 40 480.60
5 972 900 45 534.60
6 1061 900 45 583.55
7 1251 900 45 688.05
pandas.cut()
is made for problems like this where you need to segment your data into bins (ie discount % based on value range).
First define the column, the ranges, and the corresponding bins.
# The column we need to segment
col = df_values['Sum']
# The ranges: [0, 100, 200,... ,900, np.inf] means (0,100), (100,200), ... (900,inf)
graduation = np.append(df_discount['Graduation'], np.inf)
# For each range what is the corresponding bin (i.e. discount)
discount = df_discount['Discount %']
Now call pandas.cut()
and do the discount calculation.
df_values['Discount %'] = pd.cut(col,
graduation,
labels=discount)
# Convert the string label to an int for calculation
df_values['Discount %'] = df_values['Discount %'].astype(int)
df_values['New Sum'] = df_values['Sum'] * (1-df_values['Discount %']/100)
Sum Discount % New Sum
0 20 0 20.00
1 801 40 480.60
2 972 45 534.60
3 1061 45 583.55
4 1251 45 688.05
You can use pandas.DataFrame.mask. Basically if your condition is true it replaces the value. But for that your sum column has to be inside first dataframe.
https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.mask.html
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