In [1]:

    

import pandas as pd


    

In [2]:

    

df = pd.read_csv('data/src/sample_pandas_normal.csv').head(3)
print(df)


    

    




      name  age state  point
0    Alice   24    NY     64
1      Bob   42    CA     92
2  Charlie   18    CA     70

In [3]:

    

print(df['name'].str.cat(df['state']))


    

    




0      AliceNY
1        BobCA
2    CharlieCA
Name: name, dtype: object

In [4]:

    

print(df['name'].str.cat(df['state'], sep=' in '))


    

    




0      Alice in NY
1        Bob in CA
2    Charlie in CA
Name: name, dtype: object

In [5]:

    

print(df['name'].str.cat(['X', 'Y', 'Z'], sep=' in '))


    

    




0      Alice in X
1        Bob in Y
2    Charlie in Z
Name: name, dtype: object

In [6]:

    

print(df['name'].str.cat([df['state'], ['X', 'Y', 'Z']], sep='-'))


    

    




0      Alice-NY-X
1        Bob-CA-Y
2    Charlie-CA-Z
Name: name, dtype: object

In [7]:

    

# print(df['name'].str.cat('X', sep='-'))
# ValueError: Did you mean to supply a `sep` keyword?


    

In [8]:

    

print(df['name'] + df['state'])


    

    




0      AliceNY
1        BobCA
2    CharlieCA
dtype: object

In [9]:

    

print(df['name'] + ' in ' + df['state'])


    

    




0      Alice in NY
1        Bob in CA
2    Charlie in CA
dtype: object

In [10]:

    

print(df['name'] + ' in ' + df['state'] + ' - ' + ['X', 'Y', 'Z'])


    

    




0      Alice in NY - X
1        Bob in CA - Y
2    Charlie in CA - Z
dtype: object

In [11]:

    

df['col_NaN'] = ['X', pd.np.nan, 'Z']
print(df)


    

    




      name  age state  point col_NaN
0    Alice   24    NY     64       X
1      Bob   42    CA     92     NaN
2  Charlie   18    CA     70       Z

In [12]:

    

print(df['name'].str.cat(df['col_NaN'], sep='-'))


    

    




0      Alice-X
1          NaN
2    Charlie-Z
Name: name, dtype: object

In [13]:

    

print(df['name'].str.cat(df['col_NaN'], sep='-', na_rep='No Data'))


    

    




0        Alice-X
1    Bob-No Data
2      Charlie-Z
Name: name, dtype: object

In [14]:

    

print(df['name'] + '-' + df['col_NaN'])


    

    




0      Alice-X
1          NaN
2    Charlie-Z
dtype: object

In [15]:

    

print(df['name'] + '-' + df['col_NaN'].fillna('No Data'))


    

    




0        Alice-X
1    Bob-No Data
2      Charlie-Z
dtype: object

In [16]:

    

# print(df['name'].str.cat(df['age'], sep='-'))
# TypeError: sequence item 1: expected str instance, int found


    

In [17]:

    

print(df['name'].str.cat(df['age'].astype(str), sep='-'))


    

    




0      Alice-24
1        Bob-42
2    Charlie-18
Name: name, dtype: object

In [18]:

    

# print(df['name'] + '-' + df['age'])
# TypeError: can only concatenate str (not "int") to str


    

In [19]:

    

print(df['name'] + '-' + df['age'].astype(str))


    

    




0      Alice-24
1        Bob-42
2    Charlie-18
dtype: object

In [20]:

    

df['name_state'] = df['name'].str.cat(df['state'], sep=' in ')
print(df)


    

    




      name  age state  point col_NaN     name_state
0    Alice   24    NY     64       X    Alice in NY
1      Bob   42    CA     92     NaN      Bob in CA
2  Charlie   18    CA     70       Z  Charlie in CA

In [21]:

    

print(df.drop(columns=['name', 'state']))


    

    




   age  point col_NaN     name_state
0   24     64       X    Alice in NY
1   42     92     NaN      Bob in CA
2   18     70       Z  Charlie in CA

In [22]:

    

df = pd.read_csv('data/src/sample_pandas_normal.csv').head(3)
print(df)


    

    




      name  age state  point
0    Alice   24    NY     64
1      Bob   42    CA     92
2  Charlie   18    CA     70

In [23]:

    

print(df.assign(name_state=df['name'] + ' in ' + df['state']))


    

    




      name  age state  point     name_state
0    Alice   24    NY     64    Alice in NY
1      Bob   42    CA     92      Bob in CA
2  Charlie   18    CA     70  Charlie in CA

In [24]:

    

print(df.assign(name_state=df['name'] + ' in ' + df['state']).drop(columns=['name', 'state']))


    

    




   age  point     name_state
0   24     64    Alice in NY
1   42     92      Bob in CA
2   18     70  Charlie in CA