Pandas Deleting Row with Df.Drop Doesn't Work

Pandas deleting row with df.drop doesn't work

While dropping new DataFrame returns. If you want to apply changes to the current DataFrame you have to specify inplace parameter.

Option 1
Assigning back to df -

df = df.drop(790)

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Option 2
Inplace argument -

df.drop(790, inplace=True)

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]

dropping column using pandas-drop()-function not working

So there are 2 problems:

First need change separator to tab, because read_csv have default sep=',' as commented @cᴏʟᴅsᴘᴇᴇᴅ:

df = read_csv('USrealGDPGrowthPred_Quarterly.txt', header=0, sep='\t')

Or use read_table with default sep='\t':

df = df.read_table('USrealGDPGrowthPred_Quarterly.txt', header=0)

And then assign output back or use inplace=True in drop:

dataset = dataset.drop('DATE', axis=1)

Or:

dataset.drop('DATE', axis=1, inplace=True)`

dropping rows from dataframe based on a not in condition

You can use pandas.Dataframe.isin.

pandas.Dateframe.isin will return boolean values depending on whether each element is inside the list a or not. You then invert this with the ~ to convert True to False and vice versa.

import pandas as pd

a = ['2015-01-01' , '2015-02-01']

df = pd.DataFrame(data={'date':['2015-01-01' , '2015-02-01', '2015-03-01' , '2015-04-01', '2015-05-01' , '2015-06-01']})

print(df)
#         date
#0  2015-01-01
#1  2015-02-01
#2  2015-03-01
#3  2015-04-01
#4  2015-05-01
#5  2015-06-01

df = df[~df['date'].isin(a)]

print(df)
#         date
#2  2015-03-01
#3  2015-04-01
#4  2015-05-01
#5  2015-06-01

How to delete any row for which a corresponding column has a negative value using Pandas?

You can drop rows for which in a specific column the value is negative using pandas.DataFrame.drop as follows:

import pandas as pd
df = pd.DataFrame({
    'colA': [-1, 2, 3, 4, None],
    'colB': [True, True, False, False, True],
})

df = df.drop(df.index[df['colA'] < 0])

Output:

>>> df
   colA   colB
1   2.0   True
2   3.0  False
3   4.0  False
4   NaN   True

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