This question already has answers here:
Remove duplicates by columns A, keeping the row with the highest value in column B
(14 answers)
Get the row(s) which have the max value in groups using groupby
(15 answers)
Closed 1 year ago.
Trying to merge duplicate rows with identical columns besides value but want to keep all data that is not duplicated.
I thought a groupby function and resetting the index would allow me to achieve this goal but that obviously did not work.
Tried to run Microsoft Visual Basic for Applications to achieve my goal but it omitted non duplicate data as well.
Was hoping for some pandas or even excel tips or pandas/excel documentation that could assist me.
My Code:
grouped_df = result1.groupby(['ID','Name','Value'])
maximums = grouped_df.max('Price')
maximums = maximums.reset_index()
Dataset before:
ID
Name
Value
1
Apple
3
2
Banana
4
2
Banana
5
3
Orange
3
4
Pear
7
4
Pear
5
What I am getting with my code:
ID
Name
Value
2
Banana
5
4
Pear
7
What I wish to achieve:
ID
Name
Value
1
Apple
3
2
Banana
5
3
Orange
3
4
Pear
7
Related
This question already has answers here:
What is the most efficient way of counting occurrences in pandas?
(4 answers)
Closed 5 months ago.
Be a DataFrame in pandas of this format:
ID
time
other
0
81219
blue
0
32323
green
1
423
red
1
4232
blue
1
42424
red
2
42422
blue
I simply want to create a DataFrame like the following by counting the number of times each row is output in the previous DataFrame.
ID
number_appears
0
2
1
3
2
1
Try this:
df.groupby('ID').count()
This question already has answers here:
Pandas number rows within group in increasing order
(2 answers)
Closed 11 months ago.
I have a dataframe that is like this
Group | People
--------------
1 Cindy
1 Dylan
2 Kathy
3 Steven
3 Jonathan
3 Tiffany
And I want to add a new column that adds the count number, like this
Group | People | Rank
--------------------------
1 Cindy 1
1 Dylan 2
2 Kathy 1
3 Steven 1
3 Jonathan 2
3 Tiffany 3
Essentially I want it to assign the count of unique individuals in a loop based on grouping by the Group
I know that df.groupby('Group')['People'].nunique() will get me the count, but want that in a loop
Use groupby and cumcount:
df['rank'] = df.groupby('Group')['People'].cumcount()+1
This question already has answers here:
Row-wise average for a subset of columns with missing values
(3 answers)
Closed 2 years ago.
Using Pandas I have a data frame with rows and columns:
id column1 column2 column3
1 4 Banana 2
2 4 Carrot
3 1 Tomato 3
4 7 Melon 5
5 1 Lime 5
I want to iterate through each row and calculate the mean the items in column1 and column2 (e.g. Row 1: 4+2/2=3). Everything will be put in a new column called mean. empty values should be ignored.
The result should be like:
id column1 column2 column3 mean
1 4 Banana 2 3
2 4 Carrot 4
3 1 Tomato 3 2
4 7 Melon 5 6
5 2 Lime 5 3.5
You can use:
df['mean'] = df[['column1', 'column3']].mean(axis=1)
This question already has answers here:
Adding a new pandas column with mapped value from a dictionary [duplicate]
(1 answer)
Pandas create new column with count from groupby
(5 answers)
Closed 2 years ago.
I'm looking to replace values in a Pandas column with their respective frequencies in the column.
I'm aware I can use value_counts to retrieve the frequency distribution for each value in the column. What I'm not sure on is how to replace every occurance of a value with its respective frequency.
An example dataframe:
a b c
0 tiger 2 3
1 tiger 5 6
2 lion 8 9
Example output of df['a'].value_counts():
tiger 2
lion 1
Name: a, dtype: int64
Expected result when applied to column 'a':
a b c
0 2 2 3
1 2 5 6
2 1 8 9
This question already has answers here:
Cumulative Sum Function on Pandas Data Frame
(2 answers)
Closed 4 years ago.
I'm fairly new to working with pandas, and I've been trying to create a new dataframe where the price value at index n is the sum of the values from indices 0 to n.
For example, if I have:
dataframe df:
price
0 1
1 2
2 3
3 2
4 4
5 2
The resulting data frame should look like this:
dataframe df:
price
0 1
1 3
2 6
3 8
4 12
5 14
I can think of a messy way of doing it using nested for loops, but I'm trying to shy away from using costly methods and doing things a more "sophisticated" way. But I can't really seem to think of a better method of doing this, and I know there has to a better way. What is the smart way of getting this "sum" dataframe? Thank you.
I think what you're looking for is the cumulative sum, for which you can use df.cumsum:
df.cumsum()
Which returns:
price
0 1
1 3
2 6
3 8
4 12
5 14