Search for string in a dataframe first 3 word-CodePudding

In this data frame, I have the start word "PRE" in the columns containing the note, so I should update yes to the new columns, otherwise no.

For whom I got this code but it is not working.

import pandas as pd
    
df1 = pd.DataFrame({'NOTES': ["PREPAID_HOME_SCREEN_MAMO", "SCREEN_MAMO",
                              "> Unable to connect internet>4G Compatible>Set",
                              "No>Not Barred>Active>No>Available>Others>",
                              "Internet Not Working>>>Unable To Connect To"]})
df1['NOTES'].astype(str)
    
for i in df1['NOTES']:
    if i [:3]=='PRE':
        df1['new']='yes'
    else:
        df1['new']='No'
    
df1

CodePudding user response：

Set df1['new'] to a list using a list comprehension and ternary operator:

df1['new'] = ['yes' if i[:3] == 'PRE' else 'no' for i in df1['NOTES']

When setting dataframe columns, you need to set them to lists, not individual elements.

For case-insensitive:

df1['new'] = ['yes' if i[:3].upper() == 'PRE' else 'no' for i in df1['NOTES']

CodePudding user response：

You can use list to apppend the values and then add value to dataframe.

Code -

import pandas as pd
    
df1 = pd.DataFrame({'NOTES': ["PREPAID_HOME_SCREEN_MAMO", "SCREEN_MAMO",
                              "> Unable to connect internet>4G Compatible>Set",
                              "No>Not Barred>Active>No>Available>Others>",
                              "Internet Not Working>>>Unable To Connect To"]})
df1['NOTES'].astype(str)
    
data = []

for i in df1['NOTES']:
    if i[:3]=='PRE':
        data.append('yes')
    else:
        data.append('no')
    
df1['new'] = data

CodePudding user response：

The code that you posted will update all the 'new' column values with 'yes' or 'no' based on the condition. This happens because you do not already have a column 'new'.

Try the following :

import pandas as pd    
df1 = pd.DataFrame({'NOTES': ...)

df1['NOTES'].astype(str)
new=['*' for i in range(len(df1['NOTES']))]
for i in range(len(df1['NOTES'])):
  if df1['NOTES'][i][0:3]=="PRE":
      new[i]='Yes'
  else:
      new[i]='No'
df1['new']=new