How to aggregate numbers in a dataframe into a new column gradual sum of column number into a new column:
Index
numbers
new column
0
1
1
1
2
3
2
3
6
3
4
10
4
5
15
The solusion for getting the result and new column as described in the table:
df.cumsum()
Related
I have a Pandas dataframe contains some columns. Each columns have some differents values. See the image.
In col1 I have that the value 1 is more frequent than others, so, I need to transform this column to have values 1 and more then 1.
How can I do that?
My goals here is transforme this column in a categorical column but I have no idea how can I do that.
The output expected is something like the next image:
Try clip function on column:
df["col1"].clip(upper=2)
0 1
1 2
2 2
3 2
4 1
5 2
6 2
7 1
8 1
9 1
10 1
11 2
12 1
Hi I am new on Pyspark dataframe. I am doing concat Multiple column to single column with array of list.column can be duplicate, but pair with first two and then next 2. like that. duplicate column can be rename with append column index at the end of column like a_0,b_1,a_2,b_3
sample Dataframe:
a b a b a b
0 1 4 7 1 4 7
1
2 3 6 9 3 6 9
Expected Output:
combined
0 [{a:1,b:4}, {a:7,b:1}, {a:4,b:7}]
1 []
2 [{a:3, b:6}, {a:9,b:3}, {a:6,b:9}]
How to achieve above output either using pandas or spark dataframe using python
I have a dataframe where there are duplicate values in column A that have different values in column B.
I want to delete rows if one of column A duplicated values has values higher than 15 in column B.
Original Datafram
A Column
B Column
1
10
1
14
2
10
2
20
3
5
3
10
Desired dataframe
A Column
B Column
1
10
1
14
3
5
3
10
This works:
dfnew = df.groupby('A Column').filter(lambda x: x['B Column'].max()<=15 )
dfnew.reset_index(drop=True, inplace=True)
dfnew = dfnew[['A Column','B Column']]
print(dfnew)
output:
A Column B Column
0 1 10
1 1 14
2 3 5
3 3 10
Here is another way using groupby() and transform()
df.loc[~df['B Column'].gt(15).groupby(df['A Column']).transform('any')]
I have a pandas dataframe defined as:
A B SUM_C
1 1 10
1 2 20
I would like to do a cumulative sum of SUM_C and add it as a new column to the same dataframe. In other words, my end goal is to have a dataframe that looks like below:
A B SUM_C CUMSUM_C
1 1 10 10
1 2 20 30
Using cumsum in pandas on group() shows the possibility of generating a new dataframe where column name SUM_C is replaced with cumulative sum. However, my ask is to add the cumulative sum as a new column to the existing dataframe.
Thank you
Just apply cumsum on the pandas.Series df['SUM_C'] and assign it to a new column:
df['CUMSUM_C'] = df['SUM_C'].cumsum()
Result:
df
Out[34]:
A B SUM_C CUMSUM_C
0 1 1 10 10
1 1 2 20 30
how could i use group_by function by sequential rows, for example,
how could I calculate the sum for each seven rows, such as the sum of 1-7 rows and the sum of 8-14 row?
values
1 4
2 2
3 1
4 5
6 1
7 8
...
Use integer division by helper array created by np.arange be length of DataFrame and pass to groupby for aggregate sum:
df = df.groupby(np.arange(len(df)) // 7).sum()
print (df)
values
0 21