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How to drop rows from a pandas dataframe based on a pre-made list

I have a big dataset. It's about news reading. I'm trying to clean it. I created a checklist of cities that I want to keep (the set has all the cities). How can I drop the rows based on that checklist? For example, I have a checklist (as a list) that contains all the french cities. How can I drop other cities?
To picture the data frame (I have 1.5m rows btw):
City Age
0 Paris 25-34
1 Lyon 45-54
2 Kiev 35-44
3 Berlin 25-34
4 New York 25-34
5 Paris 65+
6 Toulouse 35-44
7 Nice 55-64
8 Hannover 45-54
9 Lille 35-44
10 Edinburgh 65+
11 Moscow 25-34

You can do this using pandas.Dataframe.isin. This will return boolean values checking whether each element is inside the list x. You can then use the boolean values and take out the subset of the df with rows that return True by doing df[df['City'].isin(x)]. Following is my solution:
import pandas as pd
x = ['Paris' , 'Marseille']
df = pd.DataFrame(data={'City':['Paris', 'London', 'New York', 'Marseille'],
'Age':[1, 2, 3, 4]})
print(df)
df = df[df['City'].isin(x)]
print(df)
Output:
>>> City Age
0 Paris 1
1 London 2
2 New York 3
3 Marseille 4
City Age
0 Paris 1
3 Marseille 4

Pandas dataframe sorting string values and by descending aggregated values

I'm working on transforming a dataframe to show the top 3 earners.
The dataframe looks like this
data = {'Name': ['Allistair', 'Bob', 'Carrie', 'Diane', 'Allistair', 'Bob', 'Carrie','Evelyn'], 'Sale': [20, 21, 19, 18, 5, 300, 35, 22]}
df = pd.DataFrame(data)
print(df)
Name Sale
0 Allistair 20
1 Bob 21
2 Carrie 19
3 Diane 18
4 Allistair 5
5 Bob 300
6 Carrie 35
7 Evelyn 22
In my actual dataset, I have several more columns and rows, and I want to print out and get to
something like
Name Sale
0 Bob 321
1 Carrie 35
2 Allistair 25
Every iteration that I've searched through doesn't quite get there because I get
'Name' is both an index level and a column label, which is ambiguous.

Use groupby:
>>> df.groupby('Name').sum().sort_values('Sale', ascending=False)
Sale
Name
Bob 321
Carrie 54
Allistair 25
Evelyn 22
Diane 18

Thanks to #Andrej Kasely above,
df.groupby("Name")["Sale"].sum().nlargest(3)

Combine text using delimiter for duplicate column values

What im trying to achieve is to combine Name into one value using comma delimiter whenever Country column is duplicated, and sum the values in Salary column.
Current input :
pd.DataFrame({'Name': {0: 'John',1: 'Steven',2: 'Ibrahim',3: 'George',4: 'Nancy',5: 'Mo',6: 'Khalil'},
'Country': {0: 'USA',1: 'UK',2: 'UK',3: 'France',4: 'Ireland',5: 'Ireland',6: 'Ireland'},
'Salary': {0: 100, 1: 200, 2: 200, 3: 100, 4: 50, 5: 100, 6: 10}})
Name Country Salary
0 John USA 100
1 Steven UK 200
2 Ibrahim UK 200
3 George France 100
4 Nancy Ireland 50
5 Mo Ireland 100
6 Khalil Ireland 10
Expected output :
Row 1 & 2 (in inputs) got grupped into one since Country column is duplicated & Salary column is summed up.
Tha same goes for Row 4,5 & 6.
Name Country Salary
0 John USA 100
1 Steven, Ibrahim UK 400
2 George France 100
3 Nancy, Mo, Khalil Ireland 160
What i have tried, but im not sure how to combine text in Name column :
df.groupby(['Country'],as_index=False)['Salary'].sum()
[Out:]
Country Salary
0 France 100
1 Ireland 160
2 UK 400
3 USA 100

use groupby() and agg():
out=df.groupby('Country',as_index=False).agg({'Name':', '.join,'Salary':'sum'})
If needed unique values of 'Name' column then use :
out=(df.groupby('Country',as_index=False)
.agg({'Name':lambda x:', '.join(set(x)),'Salary':'sum'}))
Note: use pd.unique() in place of set() if order of unique values is important
output of out:
Country Name Salary
0 France George 100
1 Ireland Nancy, Mo, Khalil 160
2 UK Steven, Ibrahim 400
3 USA John 100

Use agg:
df.groupby(['Country'], as_index=False).agg({'Name': ', '.join, 'Salary':'sum'})
And to get the columns in order you can add [df.columns] to the pipe:
df.groupby(['Country'], as_index=False).agg({'Name': ', '.join, 'Salary':'sum'})[df.columns]
Name Country Salary
0 John USA 100
1 Steven, Ibrahim UK 400
2 George France 100
3 Nancy, Mo, Khalil Ireland 160

Merge two pandas dataframe two create a new dataframe with a specific operation

I have two dataframes as shown below.
Company Name BOD Position Ethnicity DOB Age Gender Degree ( Specialazation) Remark
0 Big Lots Inc. David J. Campisi Director, President and Chief Executive Offic... American 1956 61 Male Graduate NaN
1 Big Lots Inc. Philip E. Mallott Chairman of the Board American 1958 59 Male MBA, Finace NaN
2 Big Lots Inc. James R. Chambers Independent Director American 1958 59 Male MBA NaN
3 Momentive Performance Materials Inc Mahesh Balakrishnan director Asian 1983 34 Male BA Economics NaN
Company Name Net Sale Gross Profit Remark
0 Big Lots Inc. 5.2B 2.1B NaN
1 Momentive Performance Materials Inc 544M 146m NaN
2 Markel Corporation 5.61B 2.06B NaN
3 Noble Energy, Inc. 3.49B 2.41B NaN
4 Leidos Holding, Inc. 7.04B 852M NaN
I want to create a new dataframe with these two, so that in 2nd dataframe, I have new columns with count of ethinicities from each companies, such as American -2 Mexican -5 and so on, so that later on, i can calculate diversity score.
the variables in the output dataframe is like,
Company Name Net Sale Gross Profit Remark American Mexican German .....
Big Lots Inc. 5.2B 2.1B NaN 2 0 5 ....

First get counts per groups by groupby with size and unstack, last join to second DataFrame:
df1 = pd.DataFrame({'Company Name':list('aabcac'),
'Ethnicity':['American'] * 3 + ['Mexican'] * 3})
df1 = df1.groupby(['Company Name', 'Ethnicity']).size().unstack(fill_value=0)
#slowier alternative
#df1 = pd.crosstab(df1['Company Name'], df1['Ethnicity'])
print (df1)
Ethnicity American Mexican
Company Name
a 2 1
b 1 0
c 0 2
df2 = pd.DataFrame({'Company Name':list('abc')})
print (df2)
Company Name
0 a
1 b
2 c
df3 = df2.join(df1, on=['Company Name'])
print (df3)
Company Name American Mexican
0 a 2 1
1 b 1 0
2 c 0 2
EDIT: You need replace unit by 0 and convert to floats:
print (df)
Name sale
0 A 100M
1 B 200M
2 C 5M
3 D 40M
4 E 10B
5 F 2B
d = {'M': '0'*6, 'B': '0'*9}
df['a'] = df['sale'].replace(d, regex=True).astype(float).sort_values(ascending=False)
print (df)
Name sale a
0 A 100M 1.000000e+08
1 B 200M 2.000000e+08
2 C 5M 5.000000e+06
3 D 40M 4.000000e+07
4 E 10B 1.000000e+10
5 F 2B 2.000000e+09

Generate columns of top ranked values in Pandas

I have a dataframe topic_data that contains the output of an LDA topic model:
topic_data.head(15)
topic word score
0 0 Automobile 0.063986
1 0 Vehicle 0.017457
2 0 Horsepower 0.015675
3 0 Engine 0.014857
4 0 Bicycle 0.013919
5 1 Sport 0.032938
6 1 Association_football 0.025324
7 1 Basketball 0.020949
8 1 Baseball 0.016935
9 1 National_Football_League 0.016597
10 2 Japan 0.051454
11 2 Beer 0.032839
12 2 Alcohol 0.027909
13 2 Drink 0.019494
14 2 Vodka 0.017908
This shows the top 5 terms for each topic, and the score (weight) for each. What I'm trying to do is reformat so that the index is the rank of the term, the columns are the topic IDs, and the values are formatted strings generated from the word and score columns (something along the lines of "%s (%.02f)" % (word,score)). That means the new dataframe should look something like this:
Topic 0 1 ...
Rank
0 Automobile (0.06) Sport (0.03) ...
1 Vehicle (0.017) Association_football (0.03) ...
... ... ... ...
What's the right way of going about this? I assume it involves a combination of index-setting, unstacking, and ranking, but I'm not sure of the right approach.

It would be something like this, note that Rank has to be generated first:
In [140]:
df['Rank'] = (-1*df).groupby('topic').score.transform(np.argsort)
df['New_str'] = df.word + df.score.apply(' ({0:.2f})'.format)
df2 = df.sort(['Rank', 'score'])[['New_str', 'topic','Rank']]
print df2.pivot(index='Rank', values='New_str', columns='topic')
topic 0 1 2
Rank
0 Automobile (0.06) Sport (0.03) Japan (0.05)
1 Vehicle (0.02) Association_football (0.03) Beer (0.03)
2 Horsepower (0.02) Basketball (0.02) Alcohol (0.03)
3 Engine (0.01) Baseball (0.02) Drink (0.02)
4 Bicycle (0.01) National_Football_League (0.02) Vodka (0.02)