Pandas selection not filtering rows based on values in columns?-CodePudding

I was practicing data wrangling and I eneded up with this simple dataset. but then I started to filter and selecting some information on it but is not working

here is the data set:

https://drive.google.com/file/d/1d1FMWhh3U1KnfVFYyC5R5USuB2BbcN6S/view?usp=sharing

df.head()

0                            TCS 
1                      Accenture 
2                      Cognizant 
3                     ICICI Bank 
4                      HDFC Bank 
                  ...            
8996              Bitla Software 
8997                Kern Liebers 
8998           ANAAMALAIS TOYOTA 
8999                    Elsevier 
9000    Samsung Heavy Industries 
Name: campany_name, Length: 9001, dtype: object

We see here that Accenture is in the second row but when I try to call it is not working

df['campany_name'] == 'Accenture'

0       False
1       False
2       False
3       False
4       False
        ...  
8996    False
8997    False
8998    False
8999    False
9000    False

I don't really want to get a different way. I just want to understand what is happening under the hood and fully understand what is different in this data set that I can't just do it like I normaly do. which is df['campany_name] == 'Accenture' I should get boolenans, and with those id be able to get the row doing df[df['campany_name] == 'Accenture']

something must be wrong at the index or format level. but I mean i'm new to python.

CodePudding user response：

df['campany_name'] = df['campany_name'].astype(str)

and then you can try:

df.query('campany_name == Accenture')

df[df['campany_name'] == 'Accenture']

and if you know the row and column and you are trying to retrieve just one value you can do:

df.at[1, 'campany_name']

Also, remember that you are just printing information, if you need to save the result, assign it to something e.g:

acc_row = df.query('campany_name == Accenture')

CodePudding user response：

As you are trying to filter the dataframe given only a string, you can use df.Series.str.contains

aaa[aaa['campany_name'].str.contains('Accenture')]
 
                       campany_name  ...    jobs interviews
1                        Accenture   ...  4600.0     2500.0
5814    Accenture Federal Services   ...     NaN       20.0

[2 rows x 10 columns]