## Pandas Merge (subtract) Two Rows with same absolute value

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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