Pandas Merge (subtract) Two Rows with same absolute value

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              Quantity  frequency
0                  200        158
1                 -200        116
2                  500         85
3                 1000         62
4                  300         57
5                 -500         51
6                 -300         50

I am trying to subtract two frequencies having the same abs(Quantity) and updating column['frequency'] and order by frequency.

Output:

              Quantity  frequency
0                 1000         62
1                  200         42
2                  500         34
3                  300          7
...


ONe way of doing it.

a = abs(df.Quantity)
b = df[df.groupby(a)["frequency"].transform('count')>1]
c = df[df.groupby(a)["frequency"].transform('count')==1]
d = b.groupby(a)['frequency'].apply(lambda x: x.values[0]-x.values[-1]).reset_index()
d.append(c)

Output

Quantity    frequency
0   200     42
1   300     7
2   500     34
3   1000    62

Pandas Merge (subtract) Two Rows with same absolute value, Pandas Merge (subtract) Two Rows with same absolute value. 发布于2020-05- 03 03:59:20. Quantity frequency 0 200 158 1 -200 116 2 500 85 3 1000 62 4 300 � In a many-to-one join, one of your datasets will have many rows in the merge column that repeat the same values (such as 1, 1, 3, 5, 5), while the merge column in the other dataset will not have repeat values (such as 1, 3, 5). As you might have guessed, in a many-to-many join, both of your merge columns will have repeat values.


This will yield the results you seek:

query = df.copy()
query["abs_quantity"] = query["Quantity"].abs()
abs_freq = pd.DataFrame(data=query.abs_quantity.value_counts()) \
             .reset_index(level=0) \
             .rename(columns={"index": "abs_quantity",
                              "abs_quantity": "abs_freq"})
results = query.merge(abs_freq, on="abs_quantity") \
               .query("abs_freq == 1")[["Quantity", "frequency"]] \
               .sort_values(by="frequency", ascending=False)

pandas.DataFrame.subtract — pandas 1.1.1 documentation, Get Subtraction of dataframe and other, element-wise (binary operator sub ). Any single or multiple element data structure, or list-like object. axis{0 or 'index', 1 or 'columns'} Broadcast across a level, matching Index values on the passed MultiIndex Add a scalar with operator version which return the same results. Pandas dataframe.subtract() function is used for finding the subtraction of dataframe and other, element-wise. This function is essentially same as doing dataframe - other but with a support to substitute for missing data in one of the inputs. Syntax: DataFrame.subtract(other, axis=’columns’, level=None, fill_value=None) Parameters :


You can try below code snippet:

for index,row in df.iterrows():
if int(row["Quantity"])<0:
    # Make all quantities as positive
    row["Quantity"]=row["Quantity"]*-1
    # Transfer the quantity sign to freq
    row["Freq"]=row["Freq"]*-1

This will change the sign.

df.groupby(['Quantity']).sum()

This will group it by the quantity.

pandas.DataFrame.abs — pandas 1.1.1 documentation, Series([pd.Timedelta('1 days')]) >>> s.abs() 0 1 days dtype: timedelta64[ns]. Select rows with data closest to certain value using argsort (from StackOverflow). >� pandas.DataFrame.combine_first¶ DataFrame.combine_first (other) [source] ¶ Update null elements with value in the same location in other. Combine two DataFrame objects by filling null values in one DataFrame with non-null values from other DataFrame. The row and column indexes of the resulting DataFrame will be the union of the two. Parameters


Python, This function is essentially same as doing dataframe - other but with a support to Syntax: DataFrame.subtract(other, axis='columns', level=None, fill_value=None ) fill_value : Fill existing missing (NaN) values, and any new element Get Day from date in Pandas - Python � Python | Pandas Panel.abs()� I would like to group rows in a dataframe, given one column. Then I would like to receive an edited dataframe for which I can decide which aggregation function makes sense. The default should be just the value of the first entry in the group. (it would be nice if the solution also worked for a combi


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