Extracting Value Based on Another Column

Extract column value based on another column in Pandas

You could use loc to get series which satisfying your condition and then iloc to get first element:

In [2]: df
Out[2]:
    A  B
0  p1  1
1  p1  2
2  p3  3
3  p2  4

In [3]: df.loc[df['B'] == 3, 'A']
Out[3]:
2    p3
Name: A, dtype: object

In [4]: df.loc[df['B'] == 3, 'A'].iloc[0]
Out[4]: 'p3'

extracting value based on another column

As @Justin said, you might be working with a data.frame instead of a matrix. All the better. Using your example above, if your data.frame looks as follows:

df <- data.frame(x=c(FALSE,TRUE), freq=c(40, 6))
> df
      x freq
1 FALSE   40
2  TRUE    6

The following will get you what you want irrespective of whether there are FALSE values or not.

df$freq[df$x==TRUE]
[1] 6

EDIT: As @DWin points out, you can simplify further by using the fact that df$x is logical:

> df$freq[df$x]
[1] 6
> df$freq[!df$x]
[1] 40

For example:

> df2 <- data.frame(x=TRUE, freq=46)
> df2
     x freq
1 TRUE   46

Still works:

> df2$freq[df2$x==TRUE]
[1] 46

extract the top values from one column based on another column

I think this is what you are looking for:

Data:

ID,genre,plays
12345,pop,23
12345,pop,576
12345,dance,18
12345,world,45
12345,dance,23
12345,pop,456

Input:

df = df.groupby(['ID','genre'])['plays'].sum().reset_index()
df.sort_values(by=['plays'], ascending=False)

Output:

    ID      genre   plays
1   12345   pop     1055
2   12345   world   45
0   12345   dance   41

Extract pattern from a column based on another column's value

You can use a regex with str.extract in a groupby+apply:

import re
df['match'] = (df.groupby('root')['word']
                 .apply(lambda g: g.str.extract(f'^(.*{re.escape(g.name)})'))
               )

Or, if you expect few repeated "root" values:

import re
df['match'] = df.apply(lambda r: m.group()
                       if (m:=re.match(f'.*{re.escape(r["root"])}', r['word']))
                       else None, axis=1)

output:

         word   root   match
0      replay   play  replay
1    replayed   play  replay
2    playable   play    play
3     thinker  think   think
4       think  think   think
5  thoughtful  think     NaN

extract a column in dataframe based on condition for another column R

You can use the subset function in base R -

subset(df, g == 'b', select = b)

#   b
#bb 2
#cc 3

How to access another column's value from a given id number in pandas DataFrame?

Like this:

In [21]: df.set_index('id').loc['b', 'label']
Out[21]: 'sal'

Or, use df.query:

In [28]: df.query('id == "b"')['label']
Out[28]: 
1    sal
Name: label, dtype: object

How to search for and extract unique values from one column in another column?

I think this works for you:

mutate(df, Col_C = stringr::str_extract(
  Col_A,
  paste0("\\b(", paste0(unique(Col_B), collapse = "|"), ")\\b")))
#                Col_A  Col_B  Col_C
# 1   blue shovel 1024   blue   blue
# 2    red shovel 1022    red    red
# 3  green bucket 3021  green  green
# 4    green rake 3021   blue  green
# 5 yellow shovel 1023 yellow yellow

Breakdown:

paste0(unique(Col_B), collapse="|") takes the words in Col_B, de-duplicates it, and concatenates them all together with | symbols; that is, c("blue","red","green") --> "blue|red|green". In regex, the | symbol is an "OR" operator.
\\b( and )\\b are word-boundaries, meaning that there isn't a word-like character immediately before (first) or after (second) the patterns; by adding this around the words, we prevent a partial match of blu on blue (in case that ever happens); while it is not apparent that this changes anything here, it's a more defensive/specific pattern. The parens add grouping, more evident in the next bullet.
With all of that, our overall pattern looks something like "\\b(blue|red|green)\\b" (abbreviated). This translates into "find blue or red or green such that there is a word-boundary on both ends of whichever one(s) you find".