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Python Pandas calculate value_counts of two columns and use groupby

I have a dataframe :
data = {'label': ['cat','dog','dog','cat','cat'],
'breeds': [ 'bengal','shar pei','pug','maine coon','maine coon'],
'nicknames':[['Loki','Loki' ],['Max'],['Toby','Zeus ','Toby'],['Marty'],['Erin ','Erin']],
'eye color':[['blue','green'],['green'],['brown','brown','brown'],['blue'],['green','brown']]
Output:
label breeds nicknames eye color
0 cat bengal [Loki,Loki] [blue, green]
1 dog shar pei [Max] [green]
2 dog pug [Toby,Zeus,Toby] [brown, brown, brown]
3 cat maine coon [Marty] [blue]
4 cat maine coon [Erin,Erin] [green, brown]
I want to apply the groupby :frame['label', 'breeds'], and calculate value_counts(unique value ) of nicknames and eye color,but output them in different columns: 'nickname_count','eye_count'
This code outputs only in one column, how do I output separately?
frame2=frame.groupby(['label','breeds'])['nicknames','eye color'].apply(lambda x: x.astype('str').value_counts().to_dict())

First, we use a groupby with sum on the lists as sum concatenates the lists together :
>>> df_grouped = df.groupby(['label', 'breeds']).agg({'nicknames': sum, 'eye color': sum}).reset_index()
>>> df_grouped
label breeds nicknames eye color
0 cat bengal [Loki, Loki] [blue, green]
1 cat maine coon [Marty, Erin , Erin] [blue, green, brown]
2 dog pug [Toby, Zeus , Toby] [brown, brown, brown]
3 dog shar pei [Max] [green]
Then, we can count the number of unique values in list by converting it to set, using len and save the output in two new columns to get the expected result :
>>> df_grouped['nickname_count'] = df_grouped['nicknames'].apply(lambda x: list(set(x))).str.len()
>>> df_grouped['eye_count'] = df_grouped['eye color'].apply(lambda x: list(set(x))).str.len()
>>> df_grouped
label breeds nicknames eye color nickname_count eye_count
0 cat bengal [Loki, Loki] [blue, green] 1 2
1 cat maine coon [Marty, Erin , Erin] [blue, green, brown] 3 3
2 dog pug [Toby, Zeus , Toby] [brown, brown, brown] 2 1
3 dog shar pei [Max] [green] 1 1

How to split two strings into different columns in Python with Pandas?

I am new to this, and I need to split a column that contains two strings into 2 columns, like this:
Initial dataframe:
Full String
0 Orange Juice
1 Pink Bird
2 Blue Ball
3 Green Tea
4 Yellow Sun
Final dataframe:
First String Second String
0 Orange Juice
1 Pink Bird
2 Blue Ball
3 Green Tea
4 Yellow Sun
I tried this but doesn't work:
df['First String'] , df['Second String'] = df['Full String'].str.split()
and this:
df['First String', 'Second String'] = df['Full String'].str.split()
How to make it work? Thank you!!!

The key here is to include the parameter expand=True in your str.split() to expand the split strings into separate columns.
Type it like this:
df[['First String','Second String']] = df['Full String'].str.split(expand=True)
Output:
Full String First String Second String
0 Orange Juice Orange Juice
1 Pink Bird Pink Bird
2 Blue Ball Blue Ball
3 Green Tea Green Tea
4 Yellow Sun Yellow Sun

have you tried this solution ?
https://stackoverflow.com/a/14745484/15320403
df = pd.DataFrame(df['Full String'].str.split(' ',1).tolist(), columns = ['First String', 'Second String'])

check if a name in one dataframe exist in other dataframe python

I am a beginner in Python and trying to find a solution for the following problem.
I have a csv file:
name, mark
Anna,24
John,19
Mike,22
Monica,20
Alex, 17
Daniel, 26
And xls file:
name, group
John, red
Anna, blue
Monica, blue
Mike, yellow
Alex, red
I am trying to get the result:
group, mark
Red, 26
Blue, 44
Yellow, 22
The number in result shows the total mark for the whole group.
I was trying to find similar problems but was not successful and I do not have much experience to find out what exactly I have to do and what commands to use.

Use pd.read_csv with df.merge and Groupby.sum:
In [89]: df1 = pd.read_csv('file1.csv')
In [89]: df1
Out[89]:
name mark
0 Anna 24
1 John 19
2 Mike 22
3 Monica 20
4 Alex 17
5 Daniel 26
In [90]: df2 = pd.read_csv('file2.csv')
In [90]: df2
Out[90]:
name group
0 John red
1 Anna blue
2 Monica blue
3 Mike yellow
4 Alex red
In [94]: df = df1.merge(df2).groupby('group').sum().reset_index()
In [95]: df
Out[95]:
group mark
0 blue 44
1 red 36
2 yellow 22
EDIT: If you have other columns, which you don't want to sum, do this:
In [284]: df1.merge(df2).groupby('group').agg({'mark': 'sum'}).reset_index()
Out[284]:
group mark
0 blue 44
1 red 36
2 yellow 22

For loop and if for multiple conditions and change another column in the same row in pandas

I am trying to have a change a column if some strings are in the other column in the same row. I am new to Pandas.
I need to change the price of some oranges to 200 but not the price of 'Red Orange'. I cannot change the name of the "fruits". It is a much longer string and I just made it shorter for convenience here.
fruits price
Green apple from us 10
Orange Apple from US 11
Mango from Canada 15
Blue Orange from Mexico 16
Red Orange from Costa 15
Pink Orange from Brazil 19
Yellow Pear from Guatemala 32
Black Melon from Guatemala 4
Purple orange from Honduras 5
so that the final result would be
fruits price
Green apple from us 10
Orange Apple from US 11
Mango from Canada 15
Blue Orange from Mexico 200
Red Orange from Costa 15
Pink Orange from Brazil 200
Yellow Pear from Guatemala 32
Black Melon from Guatemala 4
Purple orange from Honduras 5
I tried
df.loc[df['fruits'].str.lower().str.contains('orange'), 'price'] = 200
But this produces total of 4 items to change its price instead of only 2 items.
I have used for loop once and that changed the entire column to change its price.

You can use regex:
import re
df.loc[df['fruits'].str.lower().str.contains(r'(?<!red) orange', regex = True), 'price'] = 200
(?<!red) is a negative look behind. So if behind orange is red it wont match it. It also ensure its the second word with the mandatory space before the word orange, so you wont have to worry about it been the color describing something.

df.loc[((df['fruits'].str.contains('orange')) & (~df['fruits'].str.contains('Red'))),'price'] = 200
We check for oranges and ~ to confirm red is not present in the string. If both conditions are true, price change to 200

Assign specific nominal values randomly to rows using pandas

I want to assign some selected nominal values randomly to rows. For example:
I have three nominal values ["apple", "orange", "banana"].
Before assign these values randomly to rows:
**Name Fruit**
Jack
Julie
Juana
Jenny
Christina
Dickens
Robert
Cersei
After assign these values randomly to rows:
**Name Fruit**
Jack Apple
Julie Orange
Juana Apple
Jenny Banana
Christina Orange
Dickens Orange
Robert Apple
Cersei Banana
How can I do this using pandas dataframe?

You can use pd.np.random.choice with your values:
vals = ["apple", "orange", "banana"]
df['Fruit'] = pd.np.random.choice(vals, len(df))
>>> df
Name Fruit
0 Jack apple
1 Julie orange
2 Juana apple
3 Jenny orange
4 Christina apple
5 Dickens banana
6 Robert orange
7 Cersei orange

You can create a DataFrame in pandas and then assign random choices using numpy
ex2 = pd.DataFrame({'Name':['Jack','Julie','Juana','Jenny','Christina','Dickens','Robert','Cersei']})
ex2['Fruits'] = np.random.choice(['Apple','Orange','Banana'],ex2.shape[0])