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In [1]:

    
import pandas as pd



In [2]:

    
df = pd.DataFrame({'a': [0, 1, 2], 'b': [3, 4, 5]}, index=['x', 'y', 'z'])
print(df)



In [3]:

    
print(df.loc['x':'y']['a'])









    



x    0
y    1
Name: a, dtype: int64



In [4]:

    
df.loc['x':'y']['a'] = 100









    



/usr/local/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  """Entry point for launching an IPython kernel.



In [5]:

    
print(df)



In [6]:

    
print(df.loc[['x', 'y']]['a'])









    



x    100
y    100
Name: a, dtype: int64



In [7]:

    
df.loc[['x', 'y']]['a'] = 0
print(df)



In [8]:

    
df.loc['x':'y', 'a'] = 10
print(df)



In [9]:

    
df.loc[['x', 'y'], 'a'] = 0
print(df)



In [10]:

    
print(df.iloc[[0, 1]]['a'])









    



x    0
y    0
Name: a, dtype: int64



In [11]:

    
# df.loc[[0, 1], 'a']
# KeyError: "None of [Int64Index([0, 1], dtype='int64')] are in the [index]"



In [12]:

    
# df.iloc[[0, 1], 'a']
# ValueError: Location based indexing can only have [integer, integer slice (START point is INCLUDED, END point is EXCLUDED), listlike of integers, boolean array] types



In [13]:

    
print(df.index[0])



In [14]:

    
print(df.index[1])



In [15]:

    
print(df.columns[0])



In [16]:

    
print(df.columns[1])



In [17]:

    
print(df.loc[[df.index[0], df.index[1]], 'a'])









    



x    0
y    0
Name: a, dtype: int64



In [18]:

    
print(df.iloc[:2]['a'])









    



x    0
y    0
Name: a, dtype: int64



In [19]:

    
print(df.loc[:df.index[2], 'a'])









    



x    0
y    0
z    2
Name: a, dtype: int64



In [20]:

    
print(df.loc[:df.index[2 - 1], 'a'])









    



x    0
y    0
Name: a, dtype: int64



In [21]:

    
df = pd.DataFrame({'a': [0, 1, 2], 'b': [3, 4, 5]}, index=['x', 'y', 'z'])
print(df)



In [22]:

    
df_slice = df.loc['x':'y']
print(df_slice)



In [23]:

    
print(df_slice['a'])









    



x    0
y    1
Name: a, dtype: int64



In [24]:

    
df_slice['a'] = 100









    



/usr/local/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  """Entry point for launching an IPython kernel.



In [25]:

    
print(df_slice)



In [26]:

    
print(df)



In [27]:

    
df_list = df.loc[['x', 'y']]
print(df_list)



In [28]:

    
print(df_list['a'])









    



x    100
y    100
Name: a, dtype: int64



In [29]:

    
df_list['a'] = 0
print(df_list)



In [30]:

    
print(df)



In [31]:

    
df_slice['c'] = 0









    



/usr/local/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  """Entry point for launching an IPython kernel.



In [32]:

    
print(df_slice)









    



     a  b  c
x  100  3  0
y  100  4  0



In [33]:

    
df.at['x', 'a'] = 0
print(df)



In [34]:

    
print(df_slice)









    



     a  b  c
x    0  3  0
y  100  4  0



In [35]:

    
df_slice_copy = df.loc['x':'y'].copy()
print(df_slice_copy)



In [36]:

    
df.at['x', 'a'] = 100
print(df)



In [37]:

    
print(df_slice_copy)