How to Use the Apply() Function For a Single Column

How can I use the apply() function for a single column?

Given a sample dataframe df as:

   a  b
0  1  2
1  2  3
2  3  4
3  4  5

what you want is:

df['a'] = df['a'].apply(lambda x: x + 1)

that returns:

   a  b
0  2  2
1  3  3
2  4  4
3  5  5

Apply function to single column of pandas Dataframe

If it is just a single column, you don't need to use apply. Directly divide the column using its max will do.

discrepancies['Volume'] = discrepancies['Volume'] / discrepancies['Volume'].max()

Apply Function Panda Single Column & Groupby

I've user this data in the following way. (I assume that you've meant CAETITE II instead of CAETITE I) in coefficients table.

df = pd.DataFrame(
    data=[
        ["ARIZONA I", 1991, 1, 6.99],
        ["ARIZONA I", 1991, 2, 7.00],
        ["ARIZONA I", 1991, 3, 6.41],
        ["ARIZONA I", 1991, 4, 6.18],
        ["CAETITE II",1991, 1, 6.69],
        ["CAETITE II",1991, 2, 6.21]
    ], 
    columns=['plant_name',  'year',  'month',  'wind_speed_ms'])
coeff_df = pd.DataFrame(
    data=[
        ['ARIZONA I', 1.033109, -0.353939],
        ['CAETITE II', 0.967414, 0.194396],
    ],  
    columns=['plant_name', 'slopex', 'intercept']
)

You just need simple merge:

df = df.merge(coeff_df, on='plant_name')
df['adjusted_wind_speed'] = df.wind_speed_ms * df.slopex + df.intercept
df
    plant_name  year    month   wind_speed_ms   slopex  intercept   adjusted_wind_speed
0   ARIZONA I   1991    1   6.99    1.033109    -0.353939   6.867493
1   ARIZONA I   1991    2   7.00    1.033109    -0.353939   6.877824
2   ARIZONA I   1991    3   6.41    1.033109    -0.353939   6.268290
3   ARIZONA I   1991    4   6.18    1.033109    -0.353939   6.030675
4   CAETITE II  1991    1   6.69    0.967414    0.194396    6.666396
5   CAETITE II  1991    2   6.21    0.967414    0.194396    6.202037

R Apply() function on specific dataframe columns

Using an example data.frame and example function (just +1 to all values)

A <- function(x) x + 1
wifi <- data.frame(replicate(9,1:4))
wifi

#  X1 X2 X3 X4 X5 X6 X7 X8 X9
#1  1  1  1  1  1  1  1  1  1
#2  2  2  2  2  2  2  2  2  2
#3  3  3  3  3  3  3  3  3  3
#4  4  4  4  4  4  4  4  4  4

data.frame(wifi[1:3], apply(wifi[4:9],2, A) )
#or
cbind(wifi[1:3], apply(wifi[4:9],2, A) )

#  X1 X2 X3 X4 X5 X6 X7 X8 X9
#1  1  1  1  2  2  2  2  2  2
#2  2  2  2  3  3  3  3  3  3
#3  3  3  3  4  4  4  4  4  4
#4  4  4  4  5  5  5  5  5  5

Or even:

data.frame(wifi[1:3], lapply(wifi[4:9], A) )
#or
cbind(wifi[1:3], lapply(wifi[4:9], A) )

#  X1 X2 X3 X4 X5 X6 X7 X8 X9
#1  1  1  1  2  2  2  2  2  2
#2  2  2  2  3  3  3  3  3  3
#3  3  3  3  4  4  4  4  4  4
#4  4  4  4  5  5  5  5  5  5

R - Apply custom function to single column row by row

An example data would be helpful but I think you can try sapply :

df$Child_Date_of_Birth <- sapply(df$Child_Date_of_Birth, format_dates)

How to apply a function over rows within a single column of a pandas data frame?

You have to do what you do, but in reverse:
Let's say your dataframe is this:

d = [{'id': '1', 'address': '4998 Stairstep Lane Toronto ON'}, {'id': '2', 'address': '1234 Stairwell Road Toronto ON'}] 
df = pd.DataFrame(d) 
df

    id  address
0   1   4998 Stairstep Lane Toronto ON
1   2   1234 Stairwell Road Toronto ON

Extract these addresses to a list

address_list = df['address'].tolist()

and then process each with pyapp:

for al in address_list:
   addresses = pyap.parse(al, country='CA')
   for address in addresses:
        print(address)
        print(address.as_dict())

Let me know if it works.

pandas DataFrame, how to apply function to a specific column?

The answer is,

df['A'] = df['A'].map(addOne)

and maybe you would be better to know about the difference of map, applymap, apply.

but if you insist to use apply, you could try like below.

def addOne(v):
    v['A'] += 1
    return v

df.apply(addOne, axis=1)

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