Expanding on the question here, I'm wondering how to add aggregation to the following based on conditions:
Index Name Item Quantity
0 John Apple Red 10
1 John Apple Green 5
2 John Orange Cali 12
3 Jane Apple Red 10
4 Jane Apple Green 5
5 Jane Orange Cali 18
6 Jane Orange Spain 2
7 John Banana 3
8 Jane Coconut 5
9 John Lime 10
... And so forth
What I need to do is getting this data converted into a dataframe like the following. Note: I am only interested in getting the total quantity of the apples and oranges both of them in separate columns, i.e. whatever other items appear in a certain group are not to be included in the aggregation done on column "Quantity" (but they are still to appear in the column "All items" as strings):
Index Name All Items Apples Total Oranges Total
0 John Apple Red, Apple Green, Orange Cali, Banana, Lime 15 12
1 Jane Apple Red, Apple Green, Orange Cali, Coconut 15 20
How would do I achieve that? Many thanks in advance!
You can use groupby and pivot_table after extracting Apple and Orange sub strings as below:
import re
s = df['Item'].str.extract("(Apple|Orange)",expand=False,flags=re.I)
# re.I used above is optional and is used for case insensitive matching
a = df.assign(Item_1=s).dropna(subset=['Item_1'])
out = (a.groupby("Name")['Item'].agg(",".join).to_frame().join(
a.pivot_table("Quantity","Name","Item_1",aggfunc=sum).add_suffix("_Total"))
.reset_index())
print(out)
Name Item Apple_Total \
0 Jane Apple Red,Apple Green,Orange Cali,Orange Spain 15
1 John Apple Red,Apple Green,Orange Cali 15
Orange_Total
0 20
1 12
EDIT:
For edited question, you can use the same code only except groupby on the original dataframe df instead of the subset a and then join:
out = (df.groupby("Name")['Item'].agg(",".join).to_frame().join(
a.pivot_table("Quantity","Name","Item_1",aggfunc=sum).add_suffix("_Total"))
.reset_index())
print(out)
Name Item Apple_Total \
0 Jane Apple Red,Apple Green,Orange Cali,Orange Spain... 15
1 John Apple Red,Apple Green,Orange Cali,Banana,Lime 15
Orange_Total
0 20
1 12
First Filter only the required rows using str.contains on the column Item
from io import StringIO
import pandas as pd
s = StringIO("""Name;Item;Quantity
John;Apple Red;10
John;Apple Green;5
John;Orange Cali;12
Jane;Apple Red;10
Jane;Apple Green;5
Jane;Orange Cali;18
Jane;Orange Spain;2
John;Banana;3
Jane;Coconut;5
John;Lime;10
""")
df = pd.read_csv(s,sep=';')
req_items_idx = df[df.Item.str.contains('Apple|Orange')].index
df_filtered = df.loc[req_items_idx,:]
Once you have them you can further pivot the data to get the required values based on Name
pivot_df = pd.pivot_table(df_filtered,index=['Name'],columns=['Item'],aggfunc='sum')
pivot_df.columns = pivot_df.columns.droplevel()
pivot_df.columns.name = None
pivot_df = pivot_df.reset_index()
Generate the Totals for Apples and Oranges
orange_columns = pivot_df.columns[pivot_df.columns.str.contains('Orange')].tolist()
apple_columns = pivot_df.columns[pivot_df.columns.str.contains('Apple')].tolist()
pivot_df['Apples Total'] = pivot_df.loc[:,apple_columns].sum(axis=1)
pivot_df['Orange Total'] = pivot_df.loc[:,orange_columns].sum(axis=1)
A wrapper function to combine the Items together
def combine_items(inp,columns):
res = []
for val,col in zip(inp.values,columns):
if not pd.isnull(val):
res += [col]
return ','.join(res)
req_columns = apple_columns+orange_columns
pivot_df['Items'] = pivot_df[apple_columns+orange_columns].apply(combine_items,args=([req_columns]),axis=1)
Finally you can get the required columns in a single place and print the values
total_columns = pivot_df.columns[pivot_df.columns.str.contains('Total')].tolist()
name_item_columns = pivot_df.columns[pivot_df.columns.str.contains('Name|Items')].tolist()
>>> pivot_df[name_item_columns+total_columns]
Name Items Apples Total Orange Total
0 Jane Apple Green,Apple Red,Orange Cali,Orange Spain 15.0 20.0
1 John Apple Green,Apple Red,Orange Cali 15.0 12.0
The answer is intended to outline the individual steps and approach one can take to solve something similar to this
Edits: fixed a bug.
To do this, before doing your groupby you can create your Total columns. These will contain a the number of apples and oranges in that row, depending whether that row's Item is apple or orange.
df['Apples Total'] = df.apply(lambda x: x.Quantity if ('Apple' in x.Item) else 0, axis=1)
df['Oranges Total'] = df.apply(lambda x: x.Quantity if ('Orange' in x.Item) else 0, axis=1)
When this is in place, groupby name and aggregate on each column. Sum on the total columns, and aggregate to list on the item column.
df.groupby('Name').agg({'Apples Total': 'sum',
'Oranges Total': 'sum',
'Item': lambda x: list(x)
})
df = pd.read_csv(StringIO("""
Index,Name,Item,Quantity
0,John,Apple Red,10
1,John,Apple Green,5
2,John,Orange Cali,12
3,Jane,Apple Red,10
4,Jane,Apple Green,5
5,Jane,Orange Cali,18
6,Jane,Orange Spain,2
7,John,Banana,3
8,Jane,Coconut,5
9,John,Lime,10
"""))
Getting list of items
grouping by name to get the list of items
items_list = pd.DataFrame(df.groupby(["Name"])["Item"].apply(list)).rename(columns={"Item": "All Items"})
items_list
All Items
Name
Jane [Apple Red, Apple Green, Orange Cali, Orange Spain, Coconut]
John [Apple Red, Apple Green, Orange Cali, Banana, Lime]
getting count of name item groups
renaming the temp df items column such that all the apples/oranges are treated similarly
temp2 = df.groupby(["Name", "Item"])['Quantity'].apply(sum)
temp2 = pd.DataFrame(temp2).reset_index().set_index("Name")
temp2['Item'] = temp2['Item'].str.replace(r'(?:.*)(apple|orange)(?:.*)', r'\1', case=False,regex=True)
temp2
Item Quantity
Name
Jane Apple 5
Jane Apple 10
Jane Coconut 5
Jane Orange 18
Jane Orange 2
John Apple 5
John Apple 10
John Banana 3
John Lime 10
John Orange 12
getting the required pivot table
pivot table for getting items count as separate column and retaining just apple orange count
pivot_df = pd.pivot_table(temp2, values='Quantity', columns='Item', index=["Name"], aggfunc=np.sum)
pivot_df = pivot_df[['Apple', 'Orange']]
pivot_df
Item Apple Orange
Name
Jane 15.0 20.0
John 15.0 12.0
merging the items list df and the pivot_df
output = items_list.merge(pivot_df, on="Name").rename(columns = {'Apple': 'Apples
Total', 'Orange': 'Oranges Total'})
output
All Items Apples Total Oranges Total
Name
Jane [Apple Red, Apple Green, Orange Cali, Orange Spain, Coconut] 15.0 20.0
John [Apple Red, Apple Green, Orange Cali, Banana, Lime] 15.0 12.0
I have a dataframe in a format such as the following:
Index Name Fruit Quantity
0 John Apple Red 10
1 John Apple Green 5
2 John Orange Cali 12
3 Jane Apple Red 10
4 Jane Apple Green 5
5 Jane Orange Cali 18
6 Jane Orange Spain 2
I need to turn it into a dataframe such as this:
Index Name All Fruits Apples Total Oranges Total
0 John Apple Red, Apple Green, Orange Cali 15 12
1 Jane Apple Red, Apple Green, Orange Cali, Orange Spain 15 20
Question is how do I do this? I have looked at the groupby docs as well as a number of posts on pivot and aggregation but translating that into this use case somehow escapes me. Any help or pointers much appreciated.
Cheers!
Use GroupBy.agg with join, create column F by split and pass to DataFrame.pivot_table, last join together by DataFrame.join:
df1 = df.groupby('Name', sort=False)['Fruit'].agg(', '.join)
df2 = (df.assign(F = df['Fruit'].str.split().str[0])
.pivot_table(index='Name',
columns='F',
values='Quantity',
aggfunc='sum')
.add_suffix(' Total'))
df3 = df1.to_frame('All Fruits').join(df2).reset_index()
print (df3)
Name All Fruits Apple Total \
0 John Apple Red, Apple Green, Orange Cali 15
1 Jane Apple Red, Apple Green, Orange Cali, Orange Spain 15
Orange Total
0 12
1 20
I have the following df:
stuff
james__America by Estonia : 2
luke__Spain by Italy 3
michael 4
Louis__Portugal by USA 2
I would like that in case in the index the substring "__" exists then I would like to split the index and create 2 new columns next to it to make a second split by ' by ' in order to get the following output:
name1 name2 stuff
james America Estonia 2
luke Spain Italy 3
michael 0 0 4
Louis Portugal USA 2
I thought using :
df.index.str.split('__', expand=True).split(' by ',expand=True).rename(columns={0:'name1',1:'name2'})
However it does not seem to work.
Convert Index to Series by Index.to_series, then use Series.str.split by first separator, then split by second column, join original columns and last overwrite index:
df1 = df.index.to_series().str.split('__', expand=True)
df2 = df1[1].str.split(' by ',expand=True).rename(columns={0:'name1',1:'name2'}).fillna('0')
df = df2.join(df)
df.index = df1[0].rename(None)
print (df)
name1 name2 stuff
james America Estonia 2
luke Spain Italy 3
michael 0 0 4
Louis Portugal USA 2
I want to do aggregations on a panda dataframe by word.
Basically there are 3 columns with the click/impression count with the corresponding phrase. I would like to split the phrase into tokens and then sum up their clicks to tokens to decide which token is relatively good/bad.
Expected input: Panda dataframe as below
click_count impression_count text
1 10 100 pizza
2 20 200 pizza italian
3 1 1 italian cheese
Expected output:
click_count impression_count token
1 30 300 pizza // 30 = 20 + 10, 300 = 200+100
2 21 201 italian // 21 = 20 + 1
3 1 1 cheese // cheese only appeared once in italian cheese
tokens = df.text.str.split(expand=True)
token_cols = ['token_{}'.format(i) for i in range(tokens.shape[1])]
tokens.columns = token_cols
df1 = pd.concat([df.drop('text', axis=1), tokens], axis=1)
df1
df2 = pd.lreshape(df1, {'tokens': token_cols})
df2
df2.groupby('tokens').sum()
This creates a new DataFrame like piRSquared's but tokens are stacked and merged with the original:
(df['text'].str.split(expand=True).stack().reset_index(level=1, drop=True)
.to_frame('token').merge(df, left_index=True, right_index=True)
.groupby('token')['click_count', 'impression_count'].sum())
Out:
click_count impression_count
token
cheese 1 1
italian 21 201
pizza 30 300
If you break this down, it merges this:
df['text'].str.split(expand=True).stack().reset_index(level=1, drop=True).to_frame('token')
Out:
token
1 pizza
2 pizza
2 italian
3 italian
3 cheese
with the original DataFrame on their indices. The resulting df is:
(df['text'].str.split(expand=True).stack().reset_index(level=1, drop=True)
.to_frame('token').merge(df, left_index=True, right_index=True))
Out:
token click_count impression_count text
1 pizza 10 100 pizza
2 pizza 20 200 pizza italian
2 italian 20 200 pizza italian
3 italian 1 1 italian cheese
3 cheese 1 1 italian cheese
The rest is grouping by the token column.
You could do
In [3091]: s = df.text.str.split(expand=True).stack().reset_index(drop=True, level=-1)
In [3092]: df.loc[s.index].assign(token=s).groupby('token',sort=False,as_index=False).sum()
Out[3092]:
token click_count impression_count
0 pizza 30 300
1 italian 21 201
2 cheese 1 1
Details
In [3093]: df
Out[3093]:
click_count impression_count text
1 10 100 pizza
2 20 200 pizza italian
3 1 1 italian cheese
In [3094]: s
Out[3094]:
1 pizza
2 pizza
2 italian
3 italian
3 cheese
dtype: object
I've question about how to manipulate specific condtion in some colums.
For example,
from pandas import DataFrame
import pandas as pd
df = DataFrame({'name' : ['apple','pineapple','melon','orange','mango','durian'],
'amt' : [200,300,100,1,3,120]},
index = ['1','2','3','4','5','6'])
print(df)
I can see,
amt name
1 200 apple
2 300 pineapple
3 100 melon
4 1 orange
5 3 mango
6 120 durian
From above result I want to manipulate amt of apple with other items hold.
I just only know...
df.loc[df.name.str.contains('apple'), 'amt'] = df['amt']/100
This syntax manipulates not only 'apple' but 'pineapple'.
I'd like to get only result revising apple's amt like...
amt name
1 2 apple
2 300 pineapple
3 100 melon
4 1 orange
5 3 mango
6 120 durian
Is there anyone help me?
Thanks for reading.