Deleting Specific Rows from a Data Frame

Drop a specific row in Pandas

df = pd.DataFrame([['Jhon',15,'A'],['Anna',19,'B'],['Paul',25,'D']])
df. columns = ['Name','Age','Grade']

df
Out[472]: 
   Name  Age Grade
0  Jhon   15     A
1  Anna   19     B
2  Paul   25     D

You can get the index of your row:

i = df[((df.Name == 'jhon') &( df.Age == 15) & (df.Grade == 'A'))].index

and then drop it:

df.drop(i)
Out[474]: 
   Name  Age Grade
1  Anna   19     B
2  Paul   25     D

As @jezrael pointed our, you can also just negate all three:

df[((df.Name != 'jhon') &( df.Age != 15) & (df.Grade != 'A'))]
Out[477]: 
   Name  Age Grade
1  Anna   19     B
2  Paul   25     D

How to delete the selected rows from the dataframe

Why even delete them? Just filter them out. You want to keep the rows that don't satisfy the conditions you specified here, so you want the negation of A&B, which is ~A|~B:

sv = sv[(sv['Visit_Duration'] != 'Never')  |  (sv['Visit_Plan'] != 'never')]

or equivalently (~(A&B)),

sv = sv[~((sv['Visit_Duration'] == 'Never')  &  (sv['Visit_Plan'] == 'never'))]

Remove all rows having a specific value in dataframe

you check df1 != 999.9 in this check only first row delete because for other row you have column that != 999.9.

Try this:

>>> mask = (df1 == 999.9).any(1)
>>> df1[~mask]

# for more explanation

>>> df1 == 999.9
       a    b
0   True    True
1   True    False
2   False   True
3   False   False
4   False   False

in your solution:

>>> (df1 != 999.9)
       a    b
0   False   False
1   False   True
2   True    False
3   True    True
4   True    True

>>> (df1 != 999.9).any(axis = 1) # for check rows
0    False
1     True
2     True
3     True
4     True
dtype: bool

>>> (df1 != 999.9).any(axis = 0) # for check columns
a    True
b    True
dtype: bool

Deleting DataFrame row in Pandas based on column value

If I'm understanding correctly, it should be as simple as:

df = df[df.line_race != 0]

Pandas delete a row in a dataframe based on a value

Find the row you want to delete, and use drop.

delete_row = df[df["Int"]==0].index
df = df.drop(delete_row)
print(df)
Code    Int
1   A   1
2   B   1

Further more. you can use iloc to find the row, if you know the position of the column

delete_row = df[df.iloc[:,1]==0].index
df = df.drop(delete_row)

Remove dataframe row containing a specific in a list value from a list

You can approach in the following steps:

You can use pd.Series.explode() on each column/element to expand the list of strings into multiple rows, with each row contains only strings (all lists already got expanded / exploded into rows).
Then check the dataframe for strings in the to_delete list by using .isin().
Group by index level 0 (which contains original row index before explode) to aggregate and summarize the multiple rows matching result back into one row (using .sum() under groupby()).
Then .sum(axis=1) to check row-wise any matching string to delete.
Check for rows with 0 match (those rows to retain) and form a boolean index of the resulting rows.
Finally, use .loc to filter the rows without matching to retain.

df.loc[df.apply(pd.Series.explode).isin(to_delete).groupby(level=0).sum().sum(axis=1).eq(0)]

Result:

         A        B          C           D           E
1  string2  string5  [string8]  [string13]  [string16]

The original dataframe can be built for testing from the following codes:

data = {'A': ['string1', 'string2', 'string3'],
 'B': ['string4', 'string5', 'string6'],
 'C': [['string7', 'string10'], ['string8'], ['string9']],
 'D': [['string11', 'string 12'], ['string13'], ['string14']],
 'E': [['string15'], ['string16'], ['string17']]}

 df = pd.DataFrame(data)

Removing specific rows from a dataframe

DF[ ! ( ( DF$sub ==1 & DF$day==2) | ( DF$sub ==3 & DF$day==4) ) , ]   # note the ! (negation)

Or if sub is a factor as suggested by your use of quotes:

DF[ ! paste(sub,day,sep="_") %in% c("1_2", "3_4"), ]

Could also use subset:

subset(DF,  ! paste(sub,day,sep="_") %in% c("1_2", "3_4") )

(And I endorse the use of which in Dirk's answer when using "[" even though some claim it is not needed.)

Remove rows in pandas dataframe after certain value (while for looping?)

You can check the condition and then use cummax to set the condition to True after the first time it occurs within group. Then we slice the DataFrame:

mask = ~(a['event_type'].eq('purchase').groupby(a['user_session']).cummax())

a[mask]
#   user_session event_type product_id
#0             1       view          a
#1             1       cart          b
#2             2       cart          b
#3             2       cart          c
#4             2       view          d

Or if you need to also keep the purchase row use two groupbys, with a shift for the second:

mask = ~(a['event_type'].eq('purchase')
          .groupby(a['user_session']).cummax()
          .groupby(a['user_session']).shift()
          .fillna(False))

a[mask]
#   user_session event_type product_id
#0             1       view          a
#1             1       cart          b
#2             2       cart          b
#3             2       cart          c
#4             2       view          d
#5             2   purchase          d

Remove rows in pandas dataframe if any of specific columns contains a specific value

I think this is what you're asking:

df[~(df.le(95) & df.columns.str.contains("test"))].dropna()

Example (df):

    pump.test  Speed  feed.test  water
0   100        1000   70         0.2
1   100        2000   100        0.3
2   100        3000   100        0.4
3   95         4000   100        0.5

Output of the operation above:

    pump.test  Speed  feed.test  water
1   100        2000   100        0.3
2   100        3000   100        0.4