Actually, drop rows 0 and 1 only requires (any observations containing matched A and C is kept.):

In [335]:

df['AC']=df.A+df.C
In [336]:

print df.drop_duplicates('C', take_last=True) #this dataset is a special case, in general, one may need to first drop_duplicates by 'c' and then by 'a'.
     A  B  C    AC
2  foo  1  B  fooB
3  bar  1  A  barA

[2 rows x 4 columns]

But I suspect what you really want is this (one observation containing matched A and C is kept.):

In [337]:

print df.drop_duplicates('AC')
     A  B  C    AC
0  foo  0  A  fooA
2  foo  1  B  fooB
3  bar  1  A  barA

[3 rows x 4 columns]

Edit:

Now it is much clearer, therefore:

In [352]:
DG=df.groupby(['A', 'C'])   
print pd.concat([DG.get_group(item) for item, value in DG.groups.items() if len(value)==1])
     A  B  C
2  foo  1  B
3  bar  1  A

[2 rows x 3 columns]

use groupby and filter

import pandas as pd
df = pd.DataFrame({"A":["foo", "foo", "foo", "bar"], "B":[0,1,1,1], "C":["A","A","B","A"]})
df.groupby(["A", "C"]).filter(lambda df:df.shape[0] == 1)

Just want to add to Ben’s answer on drop_duplicates:

keep : {‘first’, ‘last’, False}, default ‘first’

• first : Drop duplicates except for the first occurrence.

• last : Drop duplicates except for the last occurrence.

• False : Drop all duplicates.

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 for the last occurrence. False : Drop all
duplicates. take_last : deprecated inplace : boolean, default False
Whether to drop duplicates in place or to return a copy cols : kwargs
only argument of subset [deprecated] Returns: deduplicated :
DataFrame

If you want result to be stored in another dataset:

df.drop_duplicates(keep=False)

or

df.drop_duplicates(keep=False, inplace=False)

If same dataset needs to be updated:

df.drop_duplicates(keep=False, inplace=True)

Above examples will remove all duplicates and keep one, similar to DISTINCT * in SQL

Try these various things

df = pd.DataFrame({"A":["foo", "foo", "foo", "bar","foo"], "B":[0,1,1,1,1], "C":["A","A","B","A","A"]})

>>>df.drop_duplicates( "A" , keep='first')

or

>>>df.drop_duplicates( keep='first')

or

>>>df.drop_duplicates( keep='last')

If you want to check 2 columns with try and except statements, this one can help out.

if "column_2" in df.columns:
    try:
        df[['column_1', "column_2"]] = df[['header', "column_2"]].drop_duplicates(subset = ["column_2", "column_1"] ,keep="first")
    except:
        df[["column_2"]] = df[["column_2"]].drop_duplicates(subset="column_2" ,keep="first")
        print(f"No column_1 for {path}.")
try:
    df[["column_1"]] = df[["column_1"]].drop_duplicates(subset="column_1" ,keep="first")
except:
    print(f"No column_1 or column_2 for {path}.")

You can use duplicated() to flag all duplicates and filter out flagged rows. If you need to assign columns to new_df later, make sure to call .copy() so that you don’t get SettingWithCopyWarning later on.

new_df = df[~df.duplicated(subset=['A', 'C'], keep=False)].copy()

One nice feature of this method is that you can conditionally drop duplicates with it. For example, to drop all duplicated rows only if column A is equal to 'foo', you can use the following code.

new_df = df[~( df.duplicated(subset=['A', 'B', 'C'], keep=False) & df['A'].eq('foo') )].copy()

Also, if you don’t wish to write out columns by name, you can pass slices of df.columns to subset=. This is also true for drop_duplicates() as well.

# to consider all columns for identifying duplicates
df[~df.duplicated(subset=df.columns, keep=False)].copy()

# the same is true for drop_duplicates
df.drop_duplicates(subset=df.columns, keep=False)

# to consider columns in positions 0 and 2 (i.e. 'A' and 'C') for identifying duplicates
df.drop_duplicates(subset=df.columns[[0, 2]], keep=False)