drop duplicates in pandas time series dataframe

The drop_duplicates() function is used to get Pandas series with duplicate values removed.,Previous: Series-droplevel() function Next: Indicate duplicate Series values,‘first’ : Drop duplicates except for the first occurrence.,‘last’ : Drop duplicates except for the last occurrence.

Series.drop_duplicates(self, keep = 'first', inplace = False)

With the ‘keep’ parameter, the selection behaviour of duplicated values can be changed. The value ‘first’ keeps the first occurrence for each set of duplicated entries. The default value of keep is ‘first’.,Return Series with duplicate values removed.,Related method on Series, indicating duplicate Series values.,The value False for parameter ‘keep’ discards all sets of duplicated entries. Setting the value of ‘inplace’ to True performs the operation inplace and returns None.

>>> s = pd.Series(['lama', 'cow', 'lama', 'beetle', 'lama', 'hippo'],
      ...name = 'animal') >>>
   s
0 lama
1 cow
2 lama
3 beetle
4 lama
5 hippo
Name: animal, dtype: object

>>> s.drop_duplicates()
0 lama
1 cow
3 beetle
5 hippo
Name: animal, dtype: object

>>> s.drop_duplicates(keep = 'last')
1 cow
3 beetle
4 lama
5 hippo
Name: animal, dtype: object

>>> s.drop_duplicates(keep = False, inplace = True) >>>
   s
1 cow
3 beetle
5 hippo
Name: animal, dtype: object

Pandas drop_duplicates() method helps in removing duplicates from the Pandas Dataframe In Python.,Python | Pandas dataframe.drop_duplicates(),In this example, rows having all values will be removed. Since the CSV file isn’t having such a row, a random row is duplicated and inserted into the data frame first.,Syntax: DataFrame.drop_duplicates(subset=None, keep=’first’, inplace=False)

    A B C
    0 TeamA 50 True
    1 TeamB 40 False
    3 TeamC 30 False

How to Drop Duplicate Rows in a Pandas DataFrame. The easiest way to drop duplicate rows in a pandas DataFrame is by using the drop_duplicates () function, which uses the following syntax: df.drop_duplicates (subset=None, keep=’first’, inplace=False) subset: Which columns to consider for identifying duplicates. Default is all columns. , 2 days ago Dec 18, 2020  · The easiest way to drop duplicate rows in a pandas DataFrame is by using the drop_duplicates () function, which uses the following syntax: df.drop_duplicates (subset=None, keep=’first’, inplace=False) subset: Which columns to consider for … , 1 week ago The line dfC.drop_duplicates () does not actually change the DataFrame that dfC is bound to (it just returns a copy of it with no duplicate rows). You can either specify that the DataFrame dfC is modified inplace by passing in the inplace keyword argument, dfC.drop_duplicates (inplace=True) or rebind the view of the de-duplicated DataFrame to ... , 1 week ago May 29, 2021  · Step 3: Remove duplicates from Pandas DataFrame. To remove duplicates from the DataFrame, you may use the following syntax that you saw at the beginning of this guide: df.drop_duplicates() Let’s say that you want to remove the duplicates across the two columns of Color and Shape. In that case, apply the code below in order to remove those ...

Index Time Value_A Value_B 0 1 A A 1 2 A A 2 2 B A 3 3 A A 4 5 A A

In[137]: cols = ["Value_A", "Value_B"] In[138]: df[~(df[cols] == df[cols].shift()).all(axis = 1)] Out[138]: Time Value_A Value_B Index 0 1 A A 2 2 B A 3 3 A A

import pandas as pd #create DataFrame df = pd.DataFrame({
   'team': ['a', 'b', 'b', 'c', 'c', 'd'],
   'points': [3, 7, 7, 8, 8, 9],
   'assists': [8, 6, 7, 9, 9, 3]
}) #display DataFrame print(df) team points assists 0 a3 8 1 b7 6 2 b7 7 3 c8 9 4 c8 9 5 d9 3

import pandas as pd    #create DataFrame  df = pd.DataFrame({'team': ['a', 'b', 'b', 'c', 'c', 'd'],
  'points': [3, 7, 7, 8, 8, 9],
  'assists': [8, 6, 7, 9, 9, 3]})    #display DataFrame  print(df)
team  points  assists  0    a
3
 8  1    b
7
 6  2    b
7
 7  3    c
8
 9  4    c
8
 9  5    d
9
 3

df.drop_duplicates() team points assists 0 a 3 8 1 b 7 6 2 b 7 7 3 c 8 9 5 d 9 3

df.drop_duplicates()team	points	assists  0	a	3	8  1	b	7	6  2	b	7	7  3	c	8	9  5	d	9	3  

df.drop_duplicates(keep = False) team points assists 0 a 3 8 1 b 7 6 2 b 7 7 5 d 9 3