Pandas 遍历 CSV 个文件以按名称查找列

Question

Could someone give me a tip with Pandas on how I could loop over csv files in a directory, find a columns in the CSV files called Temp where then the values of the columns need to be converted from degree C to degrees F, something like degF = degC * 1.8 + 32

I think I am close but the last bit errors out:

import pandas as pd
import os
import glob
  
  
# use glob to get all the csv files 
# in the folder
path = os.getcwd()
csv_files = glob.glob(os.path.join(path, "*.csv"))
  
  
# loop over the list of csv files
for f in csv_files:
      
    # read the csv file
    df = pd.read_csv(f)
      
    # print the location and filename
    file_name = f.split("\\")[-1]
    print('File Name Original:', file_name)

    # print the content
    print('Columns:', df.columns)

    # Find Columns with Temp in the Column Name
    temp_cols = [col for col in df.columns if 'Temp' in col]

    # print the content
    print('temp_cols Columns:', temp_cols)

    for i in range(len(temp_cols)):
        print(df.temp_cols[i].values)

Prints a few lines then errors:

File Name Original: ADMIN FRONT DESK.csv
Columns: Index(['Date', 'Temp', 'RH', 'CO2'], dtype='object')
temp_cols Columns: ['Temp']
Traceback (most recent call last):
  File "C:\OneDrive - \fix_temp.py", line 34, in <module>
    print(df.temp_cols[i].values)
  File "C:\Python39\lib\site-packages\pandas\core\generic.py", line 5465, in __getattr__
    return object.__getattribute__(self, name)
AttributeError: 'DataFrame' object has no attribute 'temp_cols'

This will print the name of the dataframe column, how do I modify?

for i in range(len(temp_cols)):
    #print(df.temp_cols[i].values)
    print(temp_cols[i])

Answer 1

I'm not completely clear on the question, but this should allow you to find if the 'Temp' column is in a DataFrame:

import pandas as pd
import os
import glob
  
  
# use glob to get all the csv files 
# in the folder
path = os.getcwd()
csv_files = glob.glob(os.path.join(path, "*.csv"))
  
  
# loop over the list of csv files
for f in csv_files:
      
    # read the csv file
    df = pd.read_csv(f)
      
    # print the location and filename
    file_name = f.split("\\")[-1]
    print('File Name Original:', file_name)

    # print the content
    print('Columns:', df.columns)

    columns = df.columns
    
    if 'Temp' in columns:
        df['Temp_F'] = (df['Temp'] * 1.8) + 32

Answer 2

If your dataset has multiple target columns to modify with the help of @speeder answer, I still needed this:

# Find Columns with Temp in the Column Name
temp_cols = [col for col in df.columns if 'Temp' in col]

# print the content
print('temp_cols Columns:', temp_cols)

for target in temp_cols:
    print(f'in the loop fixing the {target} column...')
    df[target] = (df[target] * 1.8) + 32

Complete script to loop over multiple CSV files that may have multiple target columns to modify. My target columns is temp or a column that contains the name of temp :

import os
import glob
import pandas as pd
  
# use glob to get all the csv files 
# in the folder
path = os.getcwd()
csv_files = glob.glob(os.path.join(path, "*.csv"))
  
  
# loop over the list of csv files
for f in csv_files:
      
    # read the csv file
    df = pd.read_csv(f)
      
    # print the location and filename
    file_name = f.split("\\")[-1]
    print('File Name Original:', file_name)

    # print the content
    print('Columns:', df.columns)

    # Find Columns with Temp in the Column Name
    temp_cols = [col for col in df.columns if 'Temp' in col]

    # print the content
    print('temp_cols Columns:', temp_cols)

    for target in temp_cols:
        print(f'in the loop fixing the {target} column...')
        df[target] = (df[target] * 1.8) + 32


    print(f"File {file_name} done!")
    df = df.dropna()
    df.to_csv(file_name, index=False)
    print(f"File {file_name} saved success!")