How to Remove All Rows from a Data.Frame

How to delete all rows in a dataframe?

The latter is possible and strongly recommended - "inserting" rows row-by-row is highly inefficient. A sketch could be

>>> import numpy as np
>>> import pandas as pd
>>> index = np.arange(0, 10)
>>> df = pd.DataFrame(index=index, columns=['foo', 'bar'])
>>> df
Out[268]: 
   foo  bar
0  NaN  NaN
1  NaN  NaN
2  NaN  NaN
3  NaN  NaN
4  NaN  NaN
5  NaN  NaN
6  NaN  NaN
7  NaN  NaN
8  NaN  NaN
9  NaN  NaN

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]

How to remove rows in a Pandas dataframe if the same row exists in another dataframe?

You an use merge with parameter indicator and outer join, query for filtering and then remove helper column with drop:

DataFrames are joined on all columns, so on parameter can be omit.

print (pd.merge(a,b, indicator=True, how='outer')
         .query('_merge=="left_only"')
         .drop('_merge', axis=1))
   0   1
0  1  10
2  3  30

Pandas: How to remove rows from a dataframe based on a list?

You need

new_df = sales[~sales.CustomerID.isin(badcu)]