How to count duplicate rows in pandas dataframe?

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I am trying to count the duplicates of each type of row in my dataframe. For example, say that I have a dataframe in pandas as follows:

df = pd.DataFrame({'one': pd.Series([1., 1, 1]),
                   'two': pd.Series([1., 2., 1])})

I get a df that looks like this:

    one two
0   1   1
1   1   2
2   1   1

I imagine the first step is to find all the different unique rows, which I do by:

df.drop_duplicates()

This gives me the following df:

    one two
0   1   1
1   1   2

Now I want to take each row from the above df ([1 1] and [1 2]) and get a count of how many times each is in the initial df. My result would look something like this:

Row     Count
[1 1]     2
[1 2]     1

How should I go about doing this last step?

Edit:

Here's a larger example to make it more clear:

df = pd.DataFrame({'one': pd.Series([True, True, True, False]),
                   'two': pd.Series([True, False, False, True]),
                   'three': pd.Series([True, False, False, False])})

gives me:

    one three   two
0   True    True    True
1   True    False   False
2   True    False   False
3   False   False   True

I want a result that tells me:

       Row           Count
[True True True]       1
[True False False]     2
[False False True]     1

You can groupby on all the columns and call size the index indicates the duplicate values:

In [28]:
df.groupby(df.columns.tolist(),as_index=False).size()

Out[28]:
one    three  two  
False  False  True     1
True   False  False    2
       True   True     1
dtype: int64

How to Count Duplicates in Pandas DataFrame, In Python's Pandas library, Dataframe class provides a member function to find duplicate rows based on all columns or some specific columns i.e. It returns a Boolean Series with True value for each duplicated row. be checked for finding duplicate rows. You can count duplicates in pandas DataFrame using this approach: df.pivot_table (index= ['DataFrame Column'], aggfunc='size') Next, I’ll review the following 3 cases to demonstrate how to count duplicates in pandas DataFrame: (1) under a single column. (2) across multiple columns.

df.groupby(df.columns.tolist()).size().reset_index().\
    rename(columns={0:'records'})

   one  two  records
0    1    1        2
1    1    2        1

Pandas : Find duplicate rows in a Dataframe based on all or , duplicated¶. DataFrame.duplicated(*args, **kwargs)¶. Return boolean Series denoting duplicate rows, optionally only considering certain columns  In Python’s Pandas library, Dataframe class provides a member function to find duplicate rows based on all columns or some specific columns i.e. DataFrame.duplicated(subset=None, keep='first') It returns a Boolean Series with True value for each duplicated row.

pandas.DataFrame.duplicated, Write a Pandas program to count the duplicate rows of diamonds DataFrame. Sample Solution: Python Code: import pandas as pd diamonds  duplicated() function is used for find the duplicate rows of the dataframe in python pandas. df["is_duplicate"]= df.duplicated() df The above code finds whether the row is duplicate and tags TRUE if it is duplicate and tags FALSE if it is not duplicate. And assigns it to the column named “is_duplicate” of the dataframe df.

df = pd.DataFrame({'one' : pd.Series([1., 1, 1, 3]), 'two' : pd.Series([1., 2., 1, 3] ), 'three' : pd.Series([1., 2., 1, 2] )})
df['str_list'] = df.apply(lambda row: ' '.join([str(int(val)) for val in row]), axis=1)
df1 = pd.DataFrame(df['str_list'].value_counts().values, index=df['str_list'].value_counts().index, columns=['Count'])

Produces:

>>> df1
       Count
1 1 1      2
3 2 3      1
1 2 2      1

If the index values must be a list, you could take the above code a step further with:

df1.index = df1.index.str.split()

Produces:

           Count
[1, 1, 1]      2
[3, 2, 3]      1
[1, 2, 2]      1

Pandas Practice Set-1: Count the duplicate rows of diamonds , earlier. For example, using the given example, the returned value would be [False,False,True] . Count rows in a Pandas Dataframe that satisfies a condition using Dataframe.apply () Using Dataframe.apply () we can apply a function to all the rows of a dataframe to find out if elements of rows satisfies a condition or not. Based on the result it returns a bool series.

I use:

used_features =[
    "one",
    "two",
    "three"
]

df['is_duplicated'] = df.duplicated(used_features)
df['is_duplicated'].sum()

which gives count of duplicated rows, and then you can analyse them by a new column. I didn't see such solution here.

python pandas remove duplicate columns, I'm trying to count the number of duplicate rows in 50000 rows x 4 columns to use pandas.merge after converting the result of 2) to dataframe. Repeat or replicate the rows of dataframe in pandas python: Repeat the dataframe 3 times with concat function. Ignore_index=True does not repeat the index. So new index will be created for the repeated columns

python 3x, After passing columns, it will consider them only for duplicates. keep: Controls how to consider duplicate value. It has only three distinct value and default is 'first'​. –  Either of this can do ( df is the name of the DataFrame): Method 1: Using len function: len (df) will give the number of rows in a DataFrame named df. Method 2: using count function: df [col].count () will count the number of rows in a given column col. df.count () will give the number of rows for all the columns.

Python, John 1001 10o \\go\to\store JOE John 1001 100 \\go\to\store MATT. I am looking to create an additional column for frequency of duplicate rows  So setting keep to False will give you desired answer. DataFrame.drop_duplicates (*args, **kwargs) Return DataFrame with duplicate rows removed, optionally only considering certain columns. Parameters: subset : column label or sequence of labels, optional Only consider certain columns for identifying duplicates, by default use all of the columns keep : {‘first’, ‘last’, False}, default ‘first’ first : Drop duplicates except for the first occurrence. last : Drop duplicates except

Pandas Dataframe count entire row duplicates, I am trying to count the duplicates of each type of row in my dataframe. For example, say that I have a dataframe in pandas as follows: df = pd.DataFrame({'​one':  Python | Pandas Dataframe.duplicated() Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.

Comments
  • And you want to name the size column Count? Also do you really require each entry in Row to be a list of boolean, that's kind of unusual and going to blow out your line length?
  • I'm not sure I'm understanding this correctly. It appears to give me the number of 1's in my second column (2) as its first row [2 2], and then the number of 2's in my second column (1) as its second row [1 1]. I'm looking for the number of rows that are [1 1] and [1 2]. These happen to be the same in this case, but not in the general case. Or am I missing something?
  • This solution seems to fail if you deal with missing values (as np.NaN) because they are simply ignored by the groupby.
  • @pansen the OP did not specify that as part of their requirements, also how should np.NaN be treated anyway as they are missing values?
  • @pansen where is it stated that NaN should be treated as a valid value given that it's missing data and invalid? Where is this considered the norm?
  • @pansen it works the way it works currently because NaN cannot be compared with NaN like a normal value so it's disregarded, you could argue either way how it could work but you can't state it should be treated as a valid value because it fundamentally isn't