Get Last "Column" After .Str.Split() Operation on Column in Pandas Dataframe

Get last column after .str.split() operation on column in pandas DataFrame

Do this:

In [43]: temp2.str[-1]
Out[43]: 
0    p500
1    p600
2    p700
Name: ticker

So all together it would be:

>>> temp = pd.DataFrame({'ticker' : ['spx 5/25/2001 p500', 'spx 5/25/2001 p600', 'spx 5/25/2001 p700']})
>>> temp['ticker'].str.split(' ').str[-1]
0    p500
1    p600
2    p700
Name: ticker, dtype: object

Split pandas column and add last element to a new column

You need another str to access the last splits for every row, what you did was essentially try to index the series using a non-existent label:

In [31]:

df['lastname'] = df['fullname'].str.split().str[-1]
df
Out[31]:
         fullname lastname
0   martin master   master
1    andreas test     test

Python - Get Last Element after str.split()

Use a list comprehension to take the last element of each of the split strings:

ids = [val[-1] for val in your_string.split()]

splitting at underscore in python and storing the first value

Just use the vectorised str method split and use integer indexing on the list to get the first element:

In [228]:

df['first'] = df['construct_name'].str.split('_').str[0]
df
Out[228]:
  construct_name first
0      aaaa_t1_2  aaaa
1     cccc_t4_10  cccc
2      bbbb_g3_3  bbbb

Creating a new column with last 2 values after a str.split operation

I believe you need str.split, select last to lists and last str.join:

df['my_col'] = df.category.str.split(',').str[-2:].str.join(',')
print (df)
                       category       product_name                    my_col
0  sweet beverage, cola,sugared          coca-cola              cola,sugared
1          healthy,salty snacks  salted pistachios      healthy,salty snacks
2          juice,beverage,sweet        fruit juice            beverage,sweet
3          fruit juice,beverage          lemon tea      fruit juice,beverage
4      appetizer,salty crackers    roasted peanuts  appetizer,salty crackers

EDIT:

In my opinion pandas str text functions are more recommended as apply with puru python string functions, because also working with NaNs and None.

raw_data = {
    'category': [np.nan, 'healthy,salty snacks'],
    'product_name': ['coca-cola', 'salted pistachios']}
df = pd.DataFrame(raw_data)

df['my_col'] = df.category.str.split(',').str[-2:].str.join(',')
print (df)
               category       product_name                my_col
0                   NaN          coca-cola                   NaN
1  healthy,salty snacks  salted pistachios  healthy,salty snacks

df['my_col'] = df.category.apply(lambda s: ','.join(s.split(',')[-2:]))

AttributeError: 'float' object has no attribute 'split'

split the last string after delimiter without knowing the number of delimiters available in a new column in Pandas

You can do a rsplit, then extract the last element:

df['Column X'].str.rsplit('.', 1).str[-1]

Equivalently, you can apply the python function(s):

df['Column X'].apply(lambda x: x.rsplit('.',1)[-1])

Alternatively, you can extract a regex pattern:

df['Column X'].str.extract('([^.]+)$', expand=False)

Output:

0    price
1    stock
2    price
3    stock
4    price
Name: Column X, dtype: object

Pandas: Split string on last occurrence

I think need indexing by str working with iterables:

#select last lists 
df_client["Subject"].str.rsplit("-", 1).str[-1]
#select second lists
df_client["Subject"].str.rsplit("-", 1).str[1]

If performance is important use list comprehension:

df_client['last_col'] = [x.rsplit("-", 1)[-1] for x in df_client["Subject"]]
print (df_client)
                      Subject  last_col
0  Activity-Location-UserCode  UserCode
1  Activity-Location-UserCode  UserCode

How to select the last column of dataframe

Use iloc and select all rows (:) against the last column (-1):

df.iloc[:,-1:]

Splitting a column in dataframe using str.split function

Another way around to achieve this as follows..

Example DatatSet:

>>> df = pd.DataFrame({'Name': ['Karn,Kumar', 'John,Jimlory']})
>>> df
           Name
0    Karn,Kumar
1  John,Jimlory

Result:

You can assign the column name while splitting the values as below.

>>> df[['First Name','Last Name']] = df['Name'].str.split(",", expand=True)
>>> df
           Name First Name Last Name
0    Karn,Kumar       Karn     Kumar
1  John,Jimlory       John   Jimlory

Or, as another answer stated..

>>> df['Name'].str.split(",", expand=True).rename({0: 'First_Name', 1: 'Second_Name'}, axis=1)
  First_Name Second_Name
0       Karn       Kumar
1       John     Jimlory

OR

>>> df['Name'].str.rsplit(",", expand=True).rename(columns={0:'Fist_Name', 1:'Last_Name'})
  Fist_Name Last_Name
0      Karn     Kumar
1      John   Jimlory

Note: you can use axis = columns or axis =1 both are same.

Just another way using Series.str.partition with little altercation, However, we have to use drop as partition preserves the comma "," as well as a column.

>>> df['Name'].str.partition(",", True).rename(columns={0:'Fist_Name', 2:'Last_Name'}).drop(columns =[1])
  Fist_Name Last_Name
0      Karn     Kumar
1      John   Jimlory

Just make it slim, we can define dict values for the rename.

1 - using str.partition ..

dict = {0: 'First_Name', 2: 'Second_Name'}

df = df['Name'].str.partition(",", True).rename(dict2,axis=1).drop(columns =[1])
print(df)

  First_Name Second_Name
0       Karn       Kumar
1       John     Jimlory

2 - using str.split() ..

dict = {0: 'First_Name', 1: 'Second_Name'}

df = df['Name'].str.split(",", expand=True).rename(dict, axis=1)
 print(df)
  First_Name Second_Name
0       Karn       Kumar
1       John     Jimlory

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