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Is there a drop duplicates option with combine first (pandas)


Is there a drop duplicates option with combine first (pandas)

By : Lawrence.Y
Date : November 21 2020, 03:00 PM
Does that help You don't need combine_first anymore, just compare and see what changed.
code :
r = source[~(source == dest)]
r['inventory number'] = source['inventory number']

print(r)
                cat    cost inventory number    map
1236            NaN   21.80              110    NaN
19497   Electronics  100.69              111    NaN
27358  Home/Kitchen   49.99              011    NaN
5123            NaN  169.03              010  False
5188            NaN   33.86              101    NaN


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drop duplicates in Python Pandas DataFrame not removing duplicates

drop duplicates in Python Pandas DataFrame not removing duplicates


By : Inge Lore
Date : March 29 2020, 07:55 AM
I wish this help you I have a problem with removing the duplicates. My program is based around a loop which generates tuples (x,y) which are then used as nodes in a graph. The final array/matrix of nodes is : , If I copy-paste in your data, I get:
code :
>>> df
          0         1
0  1.000000  1.000000
1  1.122733  1.153222
2  0.941207  0.778028
3  0.843013  0.916605
4  0.930963  1.213833
5  0.843013  0.916605
6  0.755064  1.079864

>>> df.drop_duplicates() 
          0         1
0  1.000000  1.000000
1  1.122733  1.153222
2  0.941207  0.778028
3  0.843013  0.916605
4  0.930963  1.213833
6  0.755064  1.079864
df = df.ix[~df.apply(np.round, args=[4]).duplicated()]
grouped = df.groupby([df[i].round(4) for i in df.columns])
subbed = grouped.apply(lambda g: g.apply(lambda row: g.irow(0), axis=1))
subbed.drop_index(level=list(df.columns), drop=True, inplace=True)
                        0         1
0      1                           
0.7551 1.0799 6  0.755064  1.079864
0.8430 0.9166 3  0.843013  0.916605
              5  0.843013  0.916605
0.9310 1.2138 4  0.930963  1.213833
0.9412 0.7780 2  0.941207  0.778028
1.0000 1.0000 0  1.000000  1.000000
1.1227 1.1532 1  1.122733  1.153222
How to do this steps better in Pandas: count, drop columns, drop duplicates

How to do this steps better in Pandas: count, drop columns, drop duplicates


By : user2162141
Date : March 29 2020, 07:55 AM
will be helpful for those in need Use GroupBy.count for Series and then call Series.plot.bar:
code :
df.groupby('user')['event'].count().plot.bar()
Using pandas drop duplicates but doesn't correctly drop the duplicates

Using pandas drop duplicates but doesn't correctly drop the duplicates


By : scgarris
Date : March 29 2020, 07:55 AM
it fixes the issue In the function word_in_text, you update the four dict: wordNT, wordNF, kaiT and kaiF.
And you call word_in_text twice while iterating the dataframe:
code :
# iterate every text in data
for index, row in data.iterrows():
    word_in_text('foo', row['text'], row['label'])
    word_in_text('bar', row['text'], row['label'])
def search(text):
    wordNT = {}
    wordNF = {}
    kaiT = {}
    kaiF = {}

    # iterate every text in data
    for index, row in data.iterrows():
        word_in_text(text, row['text'], row['label'])

    # make pandas data frame out of dict
    wordTDf = pd.DataFrame.from_dict(wordNT)
    wordFDf = pd.DataFrame.from_dict(wordNF)
    kaiTDf = pd.DataFrame.from_dict(kaiT)
    kaiFDf = pd.DataFrame.from_dict(kaiF)

    # drop duplicates
    wordTDf = wordTDf.drop_duplicates()
    wordFDf = wordFDf.drop_duplicates()
    kaiTDf = kaiTDf.drop_duplicates()
    kaiFDf = kaiFDf.drop_duplicates()

    # count how many 
    wordTrueCount = len(wordTDf.index)
    wordFalseCount = len(wordFDf.index)
    kaiTrueCount = len(kaiTDf.index)
    kaiFalseCount = len(kaiFDf.index)

    print(wordTrueCount + wordFalseCount + kaiTrueCount + kaiFalseCount)

search('foo')
search('bar')
Slicing pandas dataframe based on rearranged duplicates (or how to drop rearranged duplicates)

Slicing pandas dataframe based on rearranged duplicates (or how to drop rearranged duplicates)


By : user3484694
Date : March 29 2020, 07:55 AM
I wish this helpful for you You can sorting both columns by np.sort and assign back, then use DataFrame.drop_duplicates with specify some columns:
code :
df[['col1','col2']] = np.sort(df[['col1','col2']], axis=1)
df1 = df.drop_duplicates(['col1','col2'])
print (df1)
  col1 col2  val1  val2
0    A    B   0.8   0.1
2    A    C   0.3   0.9
3    A    D   0.2   0.8
df2 = df.drop_duplicates()
print (df2)
  col1 col2  val1  val2
0    A    B   0.8   0.1
2    A    C   0.3   0.9
3    A    D   0.2   0.8
Pandas drop duplicates with partially completed data in each row and combine data

Pandas drop duplicates with partially completed data in each row and combine data


By : user5723936
Date : March 29 2020, 07:55 AM
To fix this issue Here's one approach using apply and create new columns, using dict creation for pd.Series
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