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]:

    

mask = [True, False, True]
df_mask = df[mask]
print(df_mask)


    

    




      name  age state  point
0    Alice   24    NY     64
2  Charlie   18    CA     70

In [4]:

    

print(df['state'] == 'CA')


    

    




0    False
1     True
2     True
Name: state, dtype: bool

In [5]:

    

print(df[df['state'] == 'CA'])


    

    




      name  age state  point
1      Bob   42    CA     92
2  Charlie   18    CA     70

In [6]:

    

print(df['name'].str.contains('li'))


    

    




0     True
1    False
2     True
Name: name, dtype: bool

In [7]:

    

print(df[df['name'].str.contains('li')])


    

    




      name  age state  point
0    Alice   24    NY     64
2  Charlie   18    CA     70

In [8]:

    

df_nan = df.copy()
df_nan.iloc[2, 0] = float('nan')
print(df_nan)


    

    




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

In [9]:

    

print(df_nan['name'].str.contains('li'))


    

    




0     True
1    False
2      NaN
Name: name, dtype: object

In [10]:

    

# print(df_nan[df_nan['name'].str.contains('li')])
# ValueError: cannot index with vector containing NA / NaN values


    

In [11]:

    

print(df_nan['name'].str.contains('li', na=False))


    

    




0     True
1    False
2    False
Name: name, dtype: bool

In [12]:

    

print(df_nan['name'].str.contains('li', na=True))


    

    




0     True
1    False
2     True
Name: name, dtype: bool

In [13]:

    

print(df['name'].str.contains('LI'))


    

    




0    False
1    False
2    False
Name: name, dtype: bool

In [14]:

    

print(df['name'].str.contains('LI', case=False))


    

    




0     True
1    False
2     True
Name: name, dtype: bool

In [15]:

    

print(df['name'].str.contains('i.*e'))


    

    




0     True
1    False
2     True
Name: name, dtype: bool

In [16]:

    

print(df['name'].str.contains('i.*e', regex=False))


    

    




0    False
1    False
2    False
Name: name, dtype: bool

In [17]:

    

df_q = df.copy()
df_q.iloc[2, 0] += '?'
print(df_q)


    

    




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

In [18]:

    

# print(df_q['name'].str.contains('?'))
# error: nothing to repeat at position 0


    

In [19]:

    

print(df_q['name'].str.contains('?', regex=False))


    

    




0    False
1    False
2     True
Name: name, dtype: bool

In [20]:

    

print(df_q['name'].str.contains('\?'))


    

    




0    False
1    False
2     True
Name: name, dtype: bool

In [21]:

    

print(df['name'].str.endswith('e'))


    

    




0     True
1    False
2     True
Name: name, dtype: bool

In [22]:

    

print(df[df['name'].str.endswith('e')])


    

    




      name  age state  point
0    Alice   24    NY     64
2  Charlie   18    CA     70

In [23]:

    

print(df['name'].str.startswith('B'))


    

    




0    False
1     True
2    False
Name: name, dtype: bool

In [24]:

    

print(df[df['name'].str.startswith('B')])


    

    




  name  age state  point
1  Bob   42    CA     92

In [25]:

    

print(df['name'].str.match('.*i.*e'))


    

    




0     True
1    False
2     True
Name: name, dtype: bool

In [26]:

    

print(df[df['name'].str.match('.*i.*e')])


    

    




      name  age state  point
0    Alice   24    NY     64
2  Charlie   18    CA     70

In [27]:

    

print(df['name'].str.match('.*i'))


    

    




0     True
1    False
2     True
Name: name, dtype: bool

In [28]:

    

print(df['name'].str.match('i.*e'))


    

    




0    False
1    False
2    False
Name: name, dtype: bool