如何迭代多列数据框中的每个列值？

Question

I have multiple column data frame with columns ['Country', 'Energy Supply', 'Energy Supply per Capita', '% Renewable'] .

In the energy supply column, I want to convert the unit of the column to Peta from Giga. But in the process energy['Energy Supply']*= energy['Energy Supply'] , when the value is like "...." (missing value is denoted by this), is also getting multiplied or say duplicated. Also, the string value in the column is also getting multiplied. (For eg original: Peta, after operation: PetaPetaPetaPeta...).

To stop this from happening, I am running this:

energy = pd.read_excel("Energy Indicators.xls",skiprows = 16, skip_footer = 38)
energy.drop(['Unnamed: 0','Unnamed: 1'],axis = 1, inplace = True)
energy.columns = ['Country', 'Energy Supply', 'Energy Supply per Capita', '% Renewable']
for i in energy['Energy Supply']:
    if (isinstance(energy[i],int) == True):
        energy['Energy Supply'][i]=energy['Energy Supply'][i]*1000000
return (energy)

But I am not getting the result ie to change the value of integer type variables only, and nothing is changing.

Where I think the problem lies in, the first two rows will give the false condition, as first rows are "String" and based on that, the program is not modifying the values, whereas I want to individually check if the value is of integer type and if it is, Multiplies the number by 1,000,000.

Input:

    Country        Energy Supply    Energy Supply per Capita    % Renewable
0   NaN             Petajoules            Gigajoules                 %
1   Afghanistan        321                   10                  78.6693
2   Albania            102                   35                    100
3   Algeria            1959                  51                  0.55101
4   American Samoa      ...                 ...                  0.641026

Expected Output:

    Country        Energy Supply    Energy Supply per Capita    % Renewable
0   NaN             Petajoules            Gigajoules                 %
1   Afghanistan        3210000                10                     78.6693
2   Albania            1020000                35                      100
3   Algeria            19590000               51                     0.55101
4   American Samoa      ...                 ...                    0.641026

Current Output:

    Country        Energy Supply    Energy Supply per Capita    % Renewable
0   NaN             PetajoulesPeta.         Gigajoules               %
1   Afghanistan        3210000                10                   78.6693
2   Albania            1020000                35                    100
3   Algeria            19590000               51                   0.55101
4   American Samoa      ........                ...                0.641026

Answer 1

This worked for me with a million values:

import pandas as pd
import numpy as np 

data = {"Energy Supply":[1,30,"Petajoules",5,70]*2000000}

energy = pd.DataFrame(data)

input:

Energy Supply
0                   1
1                  30
2          Petajoules
3                   5
4                  70
5                   1
6                  30
7          Petajoules
8                   5
9                  70
10                  1
11                 30
12         Petajoules
13                  5
14                 70
15                  1
16                 30
17         Petajoules
18                  5
19                 70
20                  1
21                 30
22         Petajoules
23                  5
24                 70
25                  1
26                 30
27         Petajoules
28                  5
29                 70
              ...
[10000000 rows x 1 columns]

Then i transform the Series into an array and set the values:

arr = energy["Energy Supply"].values

for i in range(len(arr)):
    if isinstance(arr[i],int):
        arr[i] = arr[i]*1000000
    else:
        pass

The output looks like this:

        Energy Supply
0             1000000
1            30000000
2          Petajoules
3             5000000
4            70000000
5             1000000
6            30000000
7          Petajoules
8             5000000
9            70000000
10            1000000
11           30000000
12         Petajoules
13            5000000
14           70000000
15            1000000
16           30000000
17         Petajoules
18            5000000
19           70000000
20            1000000
21           30000000
22         Petajoules
23            5000000
24           70000000
25            1000000
26           30000000
27         Petajoules
28            5000000
29           70000000
              ...
[10000000 rows x 1 columns]

This solution is about twice as fast as an apply:

Looping through an array:

loop: 100%|██████████| 10000000/10000000 [00:07<00:00, 1376439.75it/s]

Using Apply:

apply: 100%|██████████| 10000000/10000000 [00:14<00:00, 687420.00it/s]

If you convert the series to numeric then the string values become nan values. Using np.where you need about 5 seconds for both converting the series to numeric and multiplying the values:

import pandas as pd
import numpy as np 
import time

data = {"Energy Supply":[1,30,"Petajoules",5,70]*2000000}

energy = pd.DataFrame(data)
t = time.time()

energy["Energy Supply"] = pd.to_numeric(energy["Energy Supply"],errors="coerce")

energy["Energy_Supply"] = np.where((energy["Energy Supply"]%1==0),energy["Energy Supply"]*100,energy["Energy Supply"])
t1 = time.time()
print(t1-t)
5.275099515914917

But you could also simply do this after using pd.to_numeric():

energy["Energy Supply"] = energy["Energy Supply"]*1000000

Answer 2

You can use str.isnumeric to check if a string is numeric and then multiply.

energy['Energy Supply'] = energy['Energy Supply'].apply(lambda x: int(x) * 1000000 if str(x).isnumeric() else x)

print (energy)

    Country         Energy Supply   Energy Supply per Capita    % Renewable
0             NaN    Petajoules           Gigajoules                     %
1     Afghanistan    321000000                10                   78.6693
2         Albania    102000000                35                       100
3         Algeria    1959000000               51                   0.55101 
4  American Samoa        ...                  ..                  0.641026