Use groupby and merge to create new column in pandas

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So I have a pandas dataframe that looks something like this.

    name  is_something
0   a     0
1   b     1
2   c     0
3   c     1
4   a     1
5   b     0
6   a     1
7   c     0
8   a     1

Is there a way to use groupby and merge to create a new column that gives the number of times a name appears with an is_something value of 1 in the whole dataframe? The updated dataframe would look like this:

    name  is_something no_of_times_is_something_is_1
0   a     0            3
1   b     1            1
2   c     0            1
3   c     1            1
4   a     1            3
5   b     0            1
6   a     1            3
7   c     0            1  
8   a     1            3

I know you can just loop through the dataframe to do this but I'm looking for a more efficient way because the dataset I'm working with is quite large. Thanks in advance!

If there are only 0 and 1 values in is_something column only use sum with GroupBy.transform for new column filled by aggregate values:

df['new'] = df.groupby('name')['is_something'].transform('sum')
print (df)
  name  is_something  new
0    a             0    3
1    b             1    1
2    c             0    1
3    c             1    1
4    a             1    3
5    b             0    1
6    a             1    3
7    c             0    1
8    a             1    3

If possible multiple values first compare by 1, convert to integer and then use transform with sum:

df['new'] = df['is_something'].eq(1).view('i1').groupby(df['name']).transform('sum')

Group By: split-apply-combine, Once you have created the GroupBy object from a DataFrame, you might want to do something different for each of the columns. Thus, using [] similar to getting a  Trying to create a new column from the groupby calculation. In the code below, I get the correct calculated values for each date (see group below) but when I try to create a new column (df['Data4']) with it I get NaN. So I am trying to create a new column in the dataframe with the sum of Data3 for the all dates and apply that to each date row

Or we just map it

df['New']=df.name.map(df.query('is_something ==1').groupby('name')['is_something'].sum())
df
  name  is_something  New
0    a             0    3
1    b             1    1
2    c             0    1
3    c             1    1
4    a             1    3
5    b             0    1
6    a             1    3
7    c             0    1
8    a             1    3

How to use the Split-Apply-Combine strategy in Pandas groupby, Pandas groupby-apply is an invaluable tool in a Python data will drop the columns that make up the MultiIndex and create a new index with  Here we have grouped Column 1.1, Column 1.2 and Column 1.3 into Column 1 and Column 2.1, Column 2.2 into Column 2. Notice that the output in each column is the min value of each row of the columns grouped together. i.e in Column 1, value of first row is the minimum value of Column 1.1 Row 1, Column 1.2 Row 1 and Column 1.3 Row 1.

You could do:

df['new'] = df.groupby('name')['is_something'].transform(lambda xs: xs.eq(1).sum())
print(df)

Output

  name  is_something  new
0    a             0    3
1    b             1    1
2    c             0    1
3    c             1    1
4    a             1    3
5    b             0    1
6    a             1    3
7    c             0    1
8    a             1    3

Pandas' groupby explained in detail, Groupby allows adopting a split-apply-combine approach to a data set. The data set consists, among other columns, of fictitious sales reps, order To demonstrate some advanced grouping functionalities, we will use the  You'll first use a groupby method to split the data into groups, where each group is the set of movies released in a given year. This is the split in split-apply-combine: # Group by year df_by_year = df.groupby('release_year') This creates a groupby object: # Check type of GroupBy object type(df_by_year) pandas.core.groupby.DataFrameGroupBy Step 2.

Combining multiple columns in Pandas groupby with dictionary , Let' see how to combine multiple columns in Pandas using groupby with dictionary with the Creating a dictionary Converting dictionary into a data-​frame. Create a new column in Pandas DataFrame based on the existing columns While working with data in Pandas, we perform a vast array of operations on the data to get the data in the desired form. One of these operations could be that we want to create new columns in the DataFrame based on the result of some operations on the existing columns in the

Pandas GroupBy: Your Guide to Grouping Data in Python – Real , You can add these to a startup file to set them automatically each time You can read the CSV file into a Pandas DataFrame with read_csv() : You call .groupby​() and pass the name of the column you want to group on, which is "state" . You can use read_csv() to combine two columns into a timestamp  To support column-specific aggregation with control over the output column names, pandas accepts the special syntax in GroupBy.agg(), known as “named aggregation”, where. The keywords are the output column names. The values are tuples whose first element is the column to select and the second element is the aggregation to apply to that column.

Groupby, split-apply-combine and pandas, The process of split-apply-combine with groupby objects is a pattern that we Import packages and set visualization style import pandas as pd import DataFrame'> RangeIndex: 1000 entries, 0 to 999 Data columns (total 7  Pandas merge(): Combining Data on Common Columns or Indices. The first technique you’ll learn is merge(). You can use merge() any time you want to do database-like join operations. It’s the most flexible of the three operations you’ll learn.