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Invalid Syntax on 'column name' When Dropping Rows based on Column Values

By : Jared
Date : September 17 2020, 02:00 AM
Hope that helps So I'm dropping rows based on column values. My df contains two specific columns that I'm filtering: ['Exclude', 'Lost Flag']. Both columns contain binary values. I dropped all rows where "Exclude" was 1 via: , try with a ` for space:
code :
df.query('`Lost Flag`!=1')
>>> df = pd.DataFrame({'A': range(1, 6),
...                    'B': range(10, 0, -2),
...                    'C C': range(10, 5, -1)})
>>> df
   A   B  C C
0  1  10   10
1  2   8    9
2  3   6    8
3  4   4    7
4  5   2    6

For columns with spaces in their name, you can use backtick quoting.

>>> df.query('B == `C C`')
   A   B  C C
0  1  10   10

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dropping rows in pandas dataframe based on column entries

By : user3564827
Date : March 29 2020, 07:55 AM
This might help you In a pandas dataframe df , Use .str.contains:
code :
    name  value
0  aaaaa      1
3  ccccc     10

Filtering a multiindex dataframe based on column values dropping all rows inside level

By : Cody Hulett
Date : March 29 2020, 07:55 AM
fixed the issue. Will look into that further I am trying to filter a DataFrame based on one or more values. Here is an example CSV: , try this:
code :
In [169]: df = df.drop(df[(df.classifier=='AlnCoverage') & (df.value < 1)].index)

In [170]: df
                                          classifier    value
AlignmentId          TranscriptId
ENSMUST00000025014-1 ENSMUST00000025014  AlnCoverage  1.00000
                     ENSMUST00000025014  AlnIdentity  0.96382
                     ENSMUST00000025014      Badness  0.03618

Dropping rows if values from a column do not meet the requirements in another column

By : monkeyBus
Date : March 29 2020, 07:55 AM
this one helps. So lets say I have this dataframe: , Using filter with any
code :
df.groupby('ID').filter(lambda x : ((x['col_3']=='Steak').any())&((x['col_3']=='Chicken').any()))
     ID col_2    col_3
0  1001     3    Steak
1  4003     8  Chicken
2  1001     2  Chicken
3  4003     1    Steak
df.groupby('ID').filter(lambda x : ~((x['col_3']=='Steak').any())&((x['col_3']=='Chicken').any()))
     ID col_2    col_3
4  7000     7  Chicken
5  7000     9  Chicken

Dropping pandas rows based on lenght of list in a column

By : Emeka Iwuagwu
Date : March 29 2020, 07:55 AM
this one helps. I have a pandas dataframe, one of the columns contains list of values like , Using str.len
code :
    A             B
0  11        [4, 5]
1  22  [10, 11, 12]
    A             B
0  11        [4, 5]
1  22  [10, 11, 12]

Pandas - Dropping DataFrame rows based on Datetime column value

By : Anurag Dwivedi
Date : March 29 2020, 07:55 AM
With these it helps I think you could try something like this using a list of Timestamps:
If you want to exclude rows with specific dates:
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