pandasDuplicated data

Select duplicated

If need set value 0 to column B, where in column A are duplicated data first create mask by Series.duplicated and then use DataFrame.ix or Series.mask:

In [224]: df = pd.DataFrame({'A':[1,2,3,3,2],
     ...:                    'B':[1,7,3,0,8]})

In [225]: mask = df.A.duplicated(keep=False)

In [226]: mask
Out[226]: 
0    False
1     True
2     True
3     True
4     True
Name: A, dtype: bool

In [227]: df.ix[mask, 'B'] = 0

In [228]: df['C'] = df.A.mask(mask, 0)

In [229]: df
Out[229]: 
   A  B  C
0  1  1  1
1  2  0  0
2  3  0  0
3  3  0  0
4  2  0  0

If need invert mask use ~:

In [230]: df['C'] = df.A.mask(~mask, 0)

In [231]: df
Out[231]: 
   A  B  C
0  1  1  0
1  2  0  2
2  3  0  3
3  3  0  3
4  2  0  2

Drop duplicated

Use drop_duplicates:

In [216]: df = pd.DataFrame({'A':[1,2,3,3,2],
     ...:                    'B':[1,7,3,0,8]})

In [217]: df
Out[217]: 
   A  B
0  1  1
1  2  7
2  3  3
3  3  0
4  2  8

# keep only the last value
In [218]: df.drop_duplicates(subset=['A'], keep='last')
Out[218]: 
   A  B
0  1  1
3  3  0
4  2  8

# keep only the first value, default value
In [219]: df.drop_duplicates(subset=['A'], keep='first')
Out[219]: 
   A  B
0  1  1
1  2  7
2  3  3

# drop all duplicated values
In [220]: df.drop_duplicates(subset=['A'], keep=False)
Out[220]: 
   A  B
0  1  1

When you don't want to get a copy of a data frame, but to modify the existing one:

In [221]: df = pd.DataFrame({'A':[1,2,3,3,2],
     ...:                    'B':[1,7,3,0,8]})

In [222]: df.drop_duplicates(subset=['A'], inplace=True)

In [223]: df
Out[223]: 
   A  B
0  1  1
1  2  7
2  3  3

Counting and getting unique elements

Number of unique elements in a series:

In [1]: id_numbers = pd.Series([111, 112, 112, 114, 115, 118, 114, 118, 112])
In [2]: id_numbers.nunique()
Out[2]: 5

Get unique elements in a series:

In [3]: id_numbers.unique()
Out[3]: array([111, 112, 114, 115, 118], dtype=int64)

In [4]: df = pd.DataFrame({'Group': list('ABAABABAAB'), 
                           'ID': [1, 1, 2, 3, 3, 2, 1, 2, 1, 3]})

In [5]: df
Out[5]: 
  Group  ID
0     A   1
1     B   1
2     A   2
3     A   3
4     B   3
5     A   2
6     B   1
7     A   2
8     A   1
9     B   3

Number of unique elements in each group:

In [6]: df.groupby('Group')['ID'].nunique()
Out[6]: 
Group
A    3
B    2
Name: ID, dtype: int64

Get of unique elements in each group:

In [7]: df.groupby('Group')['ID'].unique()
Out[7]: 
Group
A    [1, 2, 3]
B       [1, 3]
Name: ID, dtype: object

Get unique values from a column.

In [15]: df = pd.DataFrame({"A":[1,1,2,3,1,1],"B":[5,4,3,4,6,7]})

In [21]: df
Out[21]: 
   A  B
0  1  5
1  1  4
2  2  3
3  3  4
4  1  6
5  1  7

To get unique values in column A and B.

In [22]: df["A"].unique()
Out[22]: array([1, 2, 3])

In [23]: df["B"].unique()
Out[23]: array([5, 4, 3, 6, 7])

To get the unique values in column A as a list (note that unique() can be used in two slightly different ways)

In [24]: pd.unique(df['A']).tolist()
Out[24]: [1, 2, 3]

Here is a more complex example. Say we want to find the unique values from column 'B' where 'A' is equal to 1.

First, let's introduce a duplicate so you can see how it works. Let's replace the 6 in row '4', column 'B' with a 4:

In [24]: df.loc['4', 'B'] = 4    
Out[24]:    
   A  B
0  1  5
1  1  4
2  2  3
3  3  4
4  1  4
5  1  7

Now select the data:

In [25]: pd.unique(df[df['A'] == 1 ]['B']).tolist()
Out[25]: [5, 4, 7]

This can be broken down by thinking of the inner DataFrame first:

df['A'] == 1 

This finds values in column A that are equal to 1, and applies True or False to them. We can then use this to select values from column 'B' of the DataFrame (the outer DataFrame selection)

For comparison, here is the list if we don't use unique. It retrieves every value in column 'B' where column 'A' is 1

In [26]: df[df['A'] == 1]['B'].tolist()
Out[26]: [5, 4, 4, 7]