Deleting DataFrame row in Pandas based on column value
Question:
I have the following DataFrame:
daysago line_race rating rw wrating
line_date
2007-03-31 62 11 56 1.000000 56.000000
2007-03-10 83 11 67 1.000000 67.000000
2007-02-10 111 9 66 1.000000 66.000000
2007-01-13 139 10 83 0.880678 73.096278
2006-12-23 160 10 88 0.793033 69.786942
2006-11-09 204 9 52 0.636655 33.106077
2006-10-22 222 8 66 0.581946 38.408408
2006-09-29 245 9 70 0.518825 36.317752
2006-09-16 258 11 68 0.486226 33.063381
2006-08-30 275 8 72 0.446667 32.160051
2006-02-11 475 5 65 0.164591 10.698423
2006-01-13 504 0 70 0.142409 9.968634
2006-01-02 515 0 64 0.134800 8.627219
2005-12-06 542 0 70 0.117803 8.246238
2005-11-29 549 0 70 0.113758 7.963072
2005-11-22 556 0 -1 0.109852 -0.109852
2005-11-01 577 0 -1 0.098919 -0.098919
2005-10-20 589 0 -1 0.093168 -0.093168
2005-09-27 612 0 -1 0.083063 -0.083063
2005-09-07 632 0 -1 0.075171 -0.075171
2005-06-12 719 0 69 0.048690 3.359623
2005-05-29 733 0 -1 0.045404 -0.045404
2005-05-02 760 0 -1 0.039679 -0.039679
2005-04-02 790 0 -1 0.034160 -0.034160
2005-03-13 810 0 -1 0.030915 -0.030915
2004-11-09 934 0 -1 0.016647 -0.016647
I need to remove the rows where line_race is equal to 0. What’s the most efficient way to do this?
Answers:
If I’m understanding correctly, it should be as simple as:
df = df[df.line_race != 0]
The best way to do this is with boolean masking:
In [56]: df
Out[56]:
line_date daysago line_race rating raw wrating
0 2007-03-31 62 11 56 1.000 56.000
1 2007-03-10 83 11 67 1.000 67.000
2 2007-02-10 111 9 66 1.000 66.000
3 2007-01-13 139 10 83 0.881 73.096
4 2006-12-23 160 10 88 0.793 69.787
5 2006-11-09 204 9 52 0.637 33.106
6 2006-10-22 222 8 66 0.582 38.408
7 2006-09-29 245 9 70 0.519 36.318
8 2006-09-16 258 11 68 0.486 33.063
9 2006-08-30 275 8 72 0.447 32.160
10 2006-02-11 475 5 65 0.165 10.698
11 2006-01-13 504 0 70 0.142 9.969
12 2006-01-02 515 0 64 0.135 8.627
13 2005-12-06 542 0 70 0.118 8.246
14 2005-11-29 549 0 70 0.114 7.963
15 2005-11-22 556 0 -1 0.110 -0.110
16 2005-11-01 577 0 -1 0.099 -0.099
17 2005-10-20 589 0 -1 0.093 -0.093
18 2005-09-27 612 0 -1 0.083 -0.083
19 2005-09-07 632 0 -1 0.075 -0.075
20 2005-06-12 719 0 69 0.049 3.360
21 2005-05-29 733 0 -1 0.045 -0.045
22 2005-05-02 760 0 -1 0.040 -0.040
23 2005-04-02 790 0 -1 0.034 -0.034
24 2005-03-13 810 0 -1 0.031 -0.031
25 2004-11-09 934 0 -1 0.017 -0.017
In [57]: df[df.line_race != 0]
Out[57]:
line_date daysago line_race rating raw wrating
0 2007-03-31 62 11 56 1.000 56.000
1 2007-03-10 83 11 67 1.000 67.000
2 2007-02-10 111 9 66 1.000 66.000
3 2007-01-13 139 10 83 0.881 73.096
4 2006-12-23 160 10 88 0.793 69.787
5 2006-11-09 204 9 52 0.637 33.106
6 2006-10-22 222 8 66 0.582 38.408
7 2006-09-29 245 9 70 0.519 36.318
8 2006-09-16 258 11 68 0.486 33.063
9 2006-08-30 275 8 72 0.447 32.160
10 2006-02-11 475 5 65 0.165 10.698
UPDATE: Now that pandas 0.13 is out, another way to do this is df.query('line_race != 0').
But for any future bypassers you could mention that df = df[df.line_race != 0] doesn’t do anything when trying to filter for None/missing values.
Does work:
df = df[df.line_race != 0]
Doesn’t do anything:
df = df[df.line_race != None]
Does work:
df = df[df.line_race.notnull()]
The given answer is correct nontheless as someone above said you can use df.query('line_race != 0') which depending on your problem is much faster. Highly recommend.
just to add another solution, particularly useful if you are using the new pandas assessors, other solutions will replace the original pandas and lose the assessors
df.drop(df.loc[df['line_race']==0].index, inplace=True)
Another way of doing it. May not be the most efficient way as the code looks a bit more complex than the code mentioned in other answers, but still alternate way of doing the same thing.
df = df.drop(df[df['line_race']==0].index)
Though the previous answer are almost similar to what I am going to do, but using the index method does not require using another indexing method .loc(). It can be done in a similar but precise manner as
df.drop(df.index[df['line_race'] == 0], inplace = True)
If you want to delete rows based on multiple values of the column, you could use:
df[(df.line_race != 0) & (df.line_race != 10)]
To drop all rows with values 0 and 10 for line_race.
Just adding another way for DataFrame expanded over all columns:
for column in df.columns:
df = df[df[column]!=0]
Example:
def z_score(data,count):
threshold=3
for column in data.columns:
mean = np.mean(data[column])
std = np.std(data[column])
for i in data[column]:
zscore = (i-mean)/std
if(np.abs(zscore)>threshold):
count=count+1
data = data[data[column]!=i]
return data,count
I compiled and run my code. This is accurate code. You can try it your own.
data = pd.read_excel('file.xlsx')
If you have any special character or space in column name you can write it in '' like in the given code:
data = data[data['expire/t'].notnull()]
print (date)
If there is just a single string column name without any space or special
character you can directly access it.
data = data[data.expire ! = 0]
print (date)
In case of multiple values and str dtype
I used the following to filter out given values in a col:
def filter_rows_by_values(df, col, values):
return df[~df[col].isin(values)]
Example:
In a DataFrame I want to remove rows which have values "b" and "c" in column "str"
df = pd.DataFrame({"str": ["a","a","a","a","b","b","c"], "other": [1,2,3,4,5,6,7]})
df
str other
0 a 1
1 a 2
2 a 3
3 a 4
4 b 5
5 b 6
6 c 7
filter_rows_by_values(df, "str", ["b","c"])
str other
0 a 1
1 a 2
2 a 3
3 a 4
Adding one more way to do this.
df = df.query("line_race!=0")
One of the efficient and pandaic way is using eq() method:
df[~df.line_race.eq(0)]
Just in case you need to delete the row, but the value can be in different columns.
In my case I was using percentages so I wanted to delete the rows which has a value 1 in any column, since that means that it’s the 100%
for x in df:
df.drop(df.loc[df[x]==1].index, inplace=True)
Is not optimal if your df have too many columns.
It doesn’t make much difference for simple example like this, but for complicated logic, I prefer to use drop() when deleting rows because it is more straightforward than using inverse logic. For example, delete rows where A=1 AND (B=2 OR C=3).
Here’s a scalable syntax that is easy to understand and can handle complicated logic:
df.drop( df.query(" `line_race` == 0 ").index)
You can try using this:
df.drop(df[df.line_race != 0].index, inplace = True)
.
There are various ways to achieve that. Will leave below various options, that one can use, depending on specificities of one’s use case.
One will consider that OP’s dataframe is stored in the variable df.
Option 1
For OP’s case, considering that the only column with values 0 is the line_race, the following will do the work
df_new = df[df != 0].dropna()
[Out]:
line_date daysago line_race rating rw wrating
0 2007-03-31 62 11.0 56 1.000000 56.000000
1 2007-03-10 83 11.0 67 1.000000 67.000000
2 2007-02-10 111 9.0 66 1.000000 66.000000
3 2007-01-13 139 10.0 83 0.880678 73.096278
4 2006-12-23 160 10.0 88 0.793033 69.786942
5 2006-11-09 204 9.0 52 0.636655 33.106077
6 2006-10-22 222 8.0 66 0.581946 38.408408
7 2006-09-29 245 9.0 70 0.518825 36.317752
8 2006-09-16 258 11.0 68 0.486226 33.063381
9 2006-08-30 275 8.0 72 0.446667 32.160051
10 2006-02-11 475 5.0 65 0.164591 10.698423
However, as that is not always the case, would recommend checking the following options where one will specify the column name.
Option 2
tshauck’s approach ends up being better than Option 1, because one is able to specify the column. There are, however, additional variations depending on how one wants to refer to the column:
For example, using the position in the dataframe
df_new = df[df[df.columns[2]] != 0]
Or by explicitly indicating the column as follows
df_new = df[df['line_race'] != 0]
One can also follow the same login but using a custom lambda function, such as
df_new = df[df.apply(lambda x: x['line_race'] != 0, axis=1)]
[Out]:
line_date daysago line_race rating rw wrating
0 2007-03-31 62 11.0 56 1.000000 56.000000
1 2007-03-10 83 11.0 67 1.000000 67.000000
2 2007-02-10 111 9.0 66 1.000000 66.000000
3 2007-01-13 139 10.0 83 0.880678 73.096278
4 2006-12-23 160 10.0 88 0.793033 69.786942
5 2006-11-09 204 9.0 52 0.636655 33.106077
6 2006-10-22 222 8.0 66 0.581946 38.408408
7 2006-09-29 245 9.0 70 0.518825 36.317752
8 2006-09-16 258 11.0 68 0.486226 33.063381
9 2006-08-30 275 8.0 72 0.446667 32.160051
10 2006-02-11 475 5.0 65 0.164591 10.698423
Option 3
Using pandas.Series.map and a custom lambda function
df_new = df['line_race'].map(lambda x: x != 0)
[Out]:
line_date daysago line_race rating rw wrating
0 2007-03-31 62 11.0 56 1.000000 56.000000
1 2007-03-10 83 11.0 67 1.000000 67.000000
2 2007-02-10 111 9.0 66 1.000000 66.000000
3 2007-01-13 139 10.0 83 0.880678 73.096278
4 2006-12-23 160 10.0 88 0.793033 69.786942
5 2006-11-09 204 9.0 52 0.636655 33.106077
6 2006-10-22 222 8.0 66 0.581946 38.408408
7 2006-09-29 245 9.0 70 0.518825 36.317752
8 2006-09-16 258 11.0 68 0.486226 33.063381
9 2006-08-30 275 8.0 72 0.446667 32.160051
10 2006-02-11 475 5.0 65 0.164591 10.698423
Option 4
Using pandas.DataFrame.drop as follows
df_new = df.drop(df[df['line_race'] == 0].index)
[Out]:
line_date daysago line_race rating rw wrating
0 2007-03-31 62 11.0 56 1.000000 56.000000
1 2007-03-10 83 11.0 67 1.000000 67.000000
2 2007-02-10 111 9.0 66 1.000000 66.000000
3 2007-01-13 139 10.0 83 0.880678 73.096278
4 2006-12-23 160 10.0 88 0.793033 69.786942
5 2006-11-09 204 9.0 52 0.636655 33.106077
6 2006-10-22 222 8.0 66 0.581946 38.408408
7 2006-09-29 245 9.0 70 0.518825 36.317752
8 2006-09-16 258 11.0 68 0.486226 33.063381
9 2006-08-30 275 8.0 72 0.446667 32.160051
10 2006-02-11 475 5.0 65 0.164591 10.698423
Option 5
Using pandas.DataFrame.query as follows
df_new = df.query('line_race != 0')
[Out]:
line_date daysago line_race rating rw wrating
0 2007-03-31 62 11.0 56 1.000000 56.000000
1 2007-03-10 83 11.0 67 1.000000 67.000000
2 2007-02-10 111 9.0 66 1.000000 66.000000
3 2007-01-13 139 10.0 83 0.880678 73.096278
4 2006-12-23 160 10.0 88 0.793033 69.786942
5 2006-11-09 204 9.0 52 0.636655 33.106077
6 2006-10-22 222 8.0 66 0.581946 38.408408
7 2006-09-29 245 9.0 70 0.518825 36.317752
8 2006-09-16 258 11.0 68 0.486226 33.063381
9 2006-08-30 275 8.0 72 0.446667 32.160051
10 2006-02-11 475 5.0 65 0.164591 10.698423
Option 6
Using pandas.DataFrame.drop and pandas.DataFrame.query as follows
df_new = df.drop(df.query('line_race == 0').index)
[Out]:
line_date daysago line_race rating rw wrating
0 2007-03-31 62 11.0 56 1.000000 56.000000
1 2007-03-10 83 11.0 67 1.000000 67.000000
2 2007-02-10 111 9.0 66 1.000000 66.000000
3 2007-01-13 139 10.0 83 0.880678 73.096278
4 2006-12-23 160 10.0 88 0.793033 69.786942
5 2006-11-09 204 9.0 52 0.636655 33.106077
6 2006-10-22 222 8.0 66 0.581946 38.408408
7 2006-09-29 245 9.0 70 0.518825 36.317752
8 2006-09-16 258 11.0 68 0.486226 33.063381
9 2006-08-30 275 8.0 72 0.446667 32.160051
10 2006-02-11 475 5.0 65 0.164591 10.698423
Option 7
If one doesn’t have strong opinions on the output, one can use a vectorized approach with numpy.select
df_new = np.select([df != 0], [df], default=np.nan)
[Out]:
[['2007-03-31' 62 11.0 56 1.0 56.0]
['2007-03-10' 83 11.0 67 1.0 67.0]
['2007-02-10' 111 9.0 66 1.0 66.0]
['2007-01-13' 139 10.0 83 0.880678 73.096278]
['2006-12-23' 160 10.0 88 0.793033 69.786942]
['2006-11-09' 204 9.0 52 0.636655 33.106077]
['2006-10-22' 222 8.0 66 0.581946 38.408408]
['2006-09-29' 245 9.0 70 0.518825 36.317752]
['2006-09-16' 258 11.0 68 0.486226 33.063381]
['2006-08-30' 275 8.0 72 0.446667 32.160051]
['2006-02-11' 475 5.0 65 0.164591 10.698423]]
This can also be converted to a dataframe with
df_new = pd.DataFrame(df_new, columns=df.columns)
[Out]:
line_date daysago line_race rating rw wrating
0 2007-03-31 62 11.0 56 1.0 56.0
1 2007-03-10 83 11.0 67 1.0 67.0
2 2007-02-10 111 9.0 66 1.0 66.0
3 2007-01-13 139 10.0 83 0.880678 73.096278
4 2006-12-23 160 10.0 88 0.793033 69.786942
5 2006-11-09 204 9.0 52 0.636655 33.106077
6 2006-10-22 222 8.0 66 0.581946 38.408408
7 2006-09-29 245 9.0 70 0.518825 36.317752
8 2006-09-16 258 11.0 68 0.486226 33.063381
9 2006-08-30 275 8.0 72 0.446667 32.160051
10 2006-02-11 475 5.0 65 0.164591 10.698423
With regards to the most efficient solution, that would depend on how one wants to measure efficiency. Assuming that one wants to measure the time of execution, one way that one can go about doing it is with time.perf_counter().
If one measures the time of execution for all the options above, one gets the following
method time
0 Option 1 0.00000110000837594271
1 Option 2.1 0.00000139995245262980
2 Option 2.2 0.00000369996996596456
3 Option 2.3 0.00000160001218318939
4 Option 3 0.00000110000837594271
5 Option 4 0.00000120000913739204
6 Option 5 0.00000140001066029072
7 Option 6 0.00000159995397552848
8 Option 7 0.00000150001142174006
However, this might change depending on the dataframe one uses, on the requirements (such as hardware), and more.
Notes:
-
There are various suggestions on using inplace=True. Would suggest reading this: https://stackoverflow.com/a/59242208/7109869
-
There are also some people with strong opinions on .apply(). Would suggest reading this: When should I (not) want to use pandas apply() in my code?
-
If one has missing values, one might want to consider as well pandas.DataFrame.dropna. Using the option 2, it would be something like
df = df[df['line_race'] != 0].dropna()
-
There are additional ways to measure the time of execution, so I would recommend this thread: How do I get time of a Python program's execution?
so many options provided(or maybe i didnt pay much attention to it, sorry if its the case), but no one mentioned this:
we can use this notation in pandas: ~ (this gives us the inverse of the condition)
df = df[~df["line_race"] == 0]
I have the following DataFrame:
daysago line_race rating rw wrating
line_date
2007-03-31 62 11 56 1.000000 56.000000
2007-03-10 83 11 67 1.000000 67.000000
2007-02-10 111 9 66 1.000000 66.000000
2007-01-13 139 10 83 0.880678 73.096278
2006-12-23 160 10 88 0.793033 69.786942
2006-11-09 204 9 52 0.636655 33.106077
2006-10-22 222 8 66 0.581946 38.408408
2006-09-29 245 9 70 0.518825 36.317752
2006-09-16 258 11 68 0.486226 33.063381
2006-08-30 275 8 72 0.446667 32.160051
2006-02-11 475 5 65 0.164591 10.698423
2006-01-13 504 0 70 0.142409 9.968634
2006-01-02 515 0 64 0.134800 8.627219
2005-12-06 542 0 70 0.117803 8.246238
2005-11-29 549 0 70 0.113758 7.963072
2005-11-22 556 0 -1 0.109852 -0.109852
2005-11-01 577 0 -1 0.098919 -0.098919
2005-10-20 589 0 -1 0.093168 -0.093168
2005-09-27 612 0 -1 0.083063 -0.083063
2005-09-07 632 0 -1 0.075171 -0.075171
2005-06-12 719 0 69 0.048690 3.359623
2005-05-29 733 0 -1 0.045404 -0.045404
2005-05-02 760 0 -1 0.039679 -0.039679
2005-04-02 790 0 -1 0.034160 -0.034160
2005-03-13 810 0 -1 0.030915 -0.030915
2004-11-09 934 0 -1 0.016647 -0.016647
I need to remove the rows where line_race is equal to 0. What’s the most efficient way to do this?
If I’m understanding correctly, it should be as simple as:
df = df[df.line_race != 0]
The best way to do this is with boolean masking:
In [56]: df
Out[56]:
line_date daysago line_race rating raw wrating
0 2007-03-31 62 11 56 1.000 56.000
1 2007-03-10 83 11 67 1.000 67.000
2 2007-02-10 111 9 66 1.000 66.000
3 2007-01-13 139 10 83 0.881 73.096
4 2006-12-23 160 10 88 0.793 69.787
5 2006-11-09 204 9 52 0.637 33.106
6 2006-10-22 222 8 66 0.582 38.408
7 2006-09-29 245 9 70 0.519 36.318
8 2006-09-16 258 11 68 0.486 33.063
9 2006-08-30 275 8 72 0.447 32.160
10 2006-02-11 475 5 65 0.165 10.698
11 2006-01-13 504 0 70 0.142 9.969
12 2006-01-02 515 0 64 0.135 8.627
13 2005-12-06 542 0 70 0.118 8.246
14 2005-11-29 549 0 70 0.114 7.963
15 2005-11-22 556 0 -1 0.110 -0.110
16 2005-11-01 577 0 -1 0.099 -0.099
17 2005-10-20 589 0 -1 0.093 -0.093
18 2005-09-27 612 0 -1 0.083 -0.083
19 2005-09-07 632 0 -1 0.075 -0.075
20 2005-06-12 719 0 69 0.049 3.360
21 2005-05-29 733 0 -1 0.045 -0.045
22 2005-05-02 760 0 -1 0.040 -0.040
23 2005-04-02 790 0 -1 0.034 -0.034
24 2005-03-13 810 0 -1 0.031 -0.031
25 2004-11-09 934 0 -1 0.017 -0.017
In [57]: df[df.line_race != 0]
Out[57]:
line_date daysago line_race rating raw wrating
0 2007-03-31 62 11 56 1.000 56.000
1 2007-03-10 83 11 67 1.000 67.000
2 2007-02-10 111 9 66 1.000 66.000
3 2007-01-13 139 10 83 0.881 73.096
4 2006-12-23 160 10 88 0.793 69.787
5 2006-11-09 204 9 52 0.637 33.106
6 2006-10-22 222 8 66 0.582 38.408
7 2006-09-29 245 9 70 0.519 36.318
8 2006-09-16 258 11 68 0.486 33.063
9 2006-08-30 275 8 72 0.447 32.160
10 2006-02-11 475 5 65 0.165 10.698
UPDATE: Now that pandas 0.13 is out, another way to do this is df.query('line_race != 0').
But for any future bypassers you could mention that df = df[df.line_race != 0] doesn’t do anything when trying to filter for None/missing values.
Does work:
df = df[df.line_race != 0]
Doesn’t do anything:
df = df[df.line_race != None]
Does work:
df = df[df.line_race.notnull()]
The given answer is correct nontheless as someone above said you can use df.query('line_race != 0') which depending on your problem is much faster. Highly recommend.
just to add another solution, particularly useful if you are using the new pandas assessors, other solutions will replace the original pandas and lose the assessors
df.drop(df.loc[df['line_race']==0].index, inplace=True)
Another way of doing it. May not be the most efficient way as the code looks a bit more complex than the code mentioned in other answers, but still alternate way of doing the same thing.
df = df.drop(df[df['line_race']==0].index)
Though the previous answer are almost similar to what I am going to do, but using the index method does not require using another indexing method .loc(). It can be done in a similar but precise manner as
df.drop(df.index[df['line_race'] == 0], inplace = True)
If you want to delete rows based on multiple values of the column, you could use:
df[(df.line_race != 0) & (df.line_race != 10)]
To drop all rows with values 0 and 10 for line_race.
Just adding another way for DataFrame expanded over all columns:
for column in df.columns:
df = df[df[column]!=0]
Example:
def z_score(data,count):
threshold=3
for column in data.columns:
mean = np.mean(data[column])
std = np.std(data[column])
for i in data[column]:
zscore = (i-mean)/std
if(np.abs(zscore)>threshold):
count=count+1
data = data[data[column]!=i]
return data,count
I compiled and run my code. This is accurate code. You can try it your own.
data = pd.read_excel('file.xlsx')
If you have any special character or space in column name you can write it in '' like in the given code:
data = data[data['expire/t'].notnull()]
print (date)
If there is just a single string column name without any space or special
character you can directly access it.
data = data[data.expire ! = 0]
print (date)
In case of multiple values and str dtype
I used the following to filter out given values in a col:
def filter_rows_by_values(df, col, values):
return df[~df[col].isin(values)]
Example:
In a DataFrame I want to remove rows which have values "b" and "c" in column "str"
df = pd.DataFrame({"str": ["a","a","a","a","b","b","c"], "other": [1,2,3,4,5,6,7]})
df
str other
0 a 1
1 a 2
2 a 3
3 a 4
4 b 5
5 b 6
6 c 7
filter_rows_by_values(df, "str", ["b","c"])
str other
0 a 1
1 a 2
2 a 3
3 a 4
Adding one more way to do this.
df = df.query("line_race!=0")
One of the efficient and pandaic way is using eq() method:
df[~df.line_race.eq(0)]
Just in case you need to delete the row, but the value can be in different columns.
In my case I was using percentages so I wanted to delete the rows which has a value 1 in any column, since that means that it’s the 100%
for x in df:
df.drop(df.loc[df[x]==1].index, inplace=True)
Is not optimal if your df have too many columns.
It doesn’t make much difference for simple example like this, but for complicated logic, I prefer to use drop() when deleting rows because it is more straightforward than using inverse logic. For example, delete rows where A=1 AND (B=2 OR C=3).
Here’s a scalable syntax that is easy to understand and can handle complicated logic:
df.drop( df.query(" `line_race` == 0 ").index)
You can try using this:
df.drop(df[df.line_race != 0].index, inplace = True)
.
There are various ways to achieve that. Will leave below various options, that one can use, depending on specificities of one’s use case.
One will consider that OP’s dataframe is stored in the variable df.
Option 1
For OP’s case, considering that the only column with values 0 is the line_race, the following will do the work
df_new = df[df != 0].dropna()
[Out]:
line_date daysago line_race rating rw wrating
0 2007-03-31 62 11.0 56 1.000000 56.000000
1 2007-03-10 83 11.0 67 1.000000 67.000000
2 2007-02-10 111 9.0 66 1.000000 66.000000
3 2007-01-13 139 10.0 83 0.880678 73.096278
4 2006-12-23 160 10.0 88 0.793033 69.786942
5 2006-11-09 204 9.0 52 0.636655 33.106077
6 2006-10-22 222 8.0 66 0.581946 38.408408
7 2006-09-29 245 9.0 70 0.518825 36.317752
8 2006-09-16 258 11.0 68 0.486226 33.063381
9 2006-08-30 275 8.0 72 0.446667 32.160051
10 2006-02-11 475 5.0 65 0.164591 10.698423
However, as that is not always the case, would recommend checking the following options where one will specify the column name.
Option 2
tshauck’s approach ends up being better than Option 1, because one is able to specify the column. There are, however, additional variations depending on how one wants to refer to the column:
For example, using the position in the dataframe
df_new = df[df[df.columns[2]] != 0]
Or by explicitly indicating the column as follows
df_new = df[df['line_race'] != 0]
One can also follow the same login but using a custom lambda function, such as
df_new = df[df.apply(lambda x: x['line_race'] != 0, axis=1)]
[Out]:
line_date daysago line_race rating rw wrating
0 2007-03-31 62 11.0 56 1.000000 56.000000
1 2007-03-10 83 11.0 67 1.000000 67.000000
2 2007-02-10 111 9.0 66 1.000000 66.000000
3 2007-01-13 139 10.0 83 0.880678 73.096278
4 2006-12-23 160 10.0 88 0.793033 69.786942
5 2006-11-09 204 9.0 52 0.636655 33.106077
6 2006-10-22 222 8.0 66 0.581946 38.408408
7 2006-09-29 245 9.0 70 0.518825 36.317752
8 2006-09-16 258 11.0 68 0.486226 33.063381
9 2006-08-30 275 8.0 72 0.446667 32.160051
10 2006-02-11 475 5.0 65 0.164591 10.698423
Option 3
Using pandas.Series.map and a custom lambda function
df_new = df['line_race'].map(lambda x: x != 0)
[Out]:
line_date daysago line_race rating rw wrating
0 2007-03-31 62 11.0 56 1.000000 56.000000
1 2007-03-10 83 11.0 67 1.000000 67.000000
2 2007-02-10 111 9.0 66 1.000000 66.000000
3 2007-01-13 139 10.0 83 0.880678 73.096278
4 2006-12-23 160 10.0 88 0.793033 69.786942
5 2006-11-09 204 9.0 52 0.636655 33.106077
6 2006-10-22 222 8.0 66 0.581946 38.408408
7 2006-09-29 245 9.0 70 0.518825 36.317752
8 2006-09-16 258 11.0 68 0.486226 33.063381
9 2006-08-30 275 8.0 72 0.446667 32.160051
10 2006-02-11 475 5.0 65 0.164591 10.698423
Option 4
Using pandas.DataFrame.drop as follows
df_new = df.drop(df[df['line_race'] == 0].index)
[Out]:
line_date daysago line_race rating rw wrating
0 2007-03-31 62 11.0 56 1.000000 56.000000
1 2007-03-10 83 11.0 67 1.000000 67.000000
2 2007-02-10 111 9.0 66 1.000000 66.000000
3 2007-01-13 139 10.0 83 0.880678 73.096278
4 2006-12-23 160 10.0 88 0.793033 69.786942
5 2006-11-09 204 9.0 52 0.636655 33.106077
6 2006-10-22 222 8.0 66 0.581946 38.408408
7 2006-09-29 245 9.0 70 0.518825 36.317752
8 2006-09-16 258 11.0 68 0.486226 33.063381
9 2006-08-30 275 8.0 72 0.446667 32.160051
10 2006-02-11 475 5.0 65 0.164591 10.698423
Option 5
Using pandas.DataFrame.query as follows
df_new = df.query('line_race != 0')
[Out]:
line_date daysago line_race rating rw wrating
0 2007-03-31 62 11.0 56 1.000000 56.000000
1 2007-03-10 83 11.0 67 1.000000 67.000000
2 2007-02-10 111 9.0 66 1.000000 66.000000
3 2007-01-13 139 10.0 83 0.880678 73.096278
4 2006-12-23 160 10.0 88 0.793033 69.786942
5 2006-11-09 204 9.0 52 0.636655 33.106077
6 2006-10-22 222 8.0 66 0.581946 38.408408
7 2006-09-29 245 9.0 70 0.518825 36.317752
8 2006-09-16 258 11.0 68 0.486226 33.063381
9 2006-08-30 275 8.0 72 0.446667 32.160051
10 2006-02-11 475 5.0 65 0.164591 10.698423
Option 6
Using pandas.DataFrame.drop and pandas.DataFrame.query as follows
df_new = df.drop(df.query('line_race == 0').index)
[Out]:
line_date daysago line_race rating rw wrating
0 2007-03-31 62 11.0 56 1.000000 56.000000
1 2007-03-10 83 11.0 67 1.000000 67.000000
2 2007-02-10 111 9.0 66 1.000000 66.000000
3 2007-01-13 139 10.0 83 0.880678 73.096278
4 2006-12-23 160 10.0 88 0.793033 69.786942
5 2006-11-09 204 9.0 52 0.636655 33.106077
6 2006-10-22 222 8.0 66 0.581946 38.408408
7 2006-09-29 245 9.0 70 0.518825 36.317752
8 2006-09-16 258 11.0 68 0.486226 33.063381
9 2006-08-30 275 8.0 72 0.446667 32.160051
10 2006-02-11 475 5.0 65 0.164591 10.698423
Option 7
If one doesn’t have strong opinions on the output, one can use a vectorized approach with numpy.select
df_new = np.select([df != 0], [df], default=np.nan)
[Out]:
[['2007-03-31' 62 11.0 56 1.0 56.0]
['2007-03-10' 83 11.0 67 1.0 67.0]
['2007-02-10' 111 9.0 66 1.0 66.0]
['2007-01-13' 139 10.0 83 0.880678 73.096278]
['2006-12-23' 160 10.0 88 0.793033 69.786942]
['2006-11-09' 204 9.0 52 0.636655 33.106077]
['2006-10-22' 222 8.0 66 0.581946 38.408408]
['2006-09-29' 245 9.0 70 0.518825 36.317752]
['2006-09-16' 258 11.0 68 0.486226 33.063381]
['2006-08-30' 275 8.0 72 0.446667 32.160051]
['2006-02-11' 475 5.0 65 0.164591 10.698423]]
This can also be converted to a dataframe with
df_new = pd.DataFrame(df_new, columns=df.columns)
[Out]:
line_date daysago line_race rating rw wrating
0 2007-03-31 62 11.0 56 1.0 56.0
1 2007-03-10 83 11.0 67 1.0 67.0
2 2007-02-10 111 9.0 66 1.0 66.0
3 2007-01-13 139 10.0 83 0.880678 73.096278
4 2006-12-23 160 10.0 88 0.793033 69.786942
5 2006-11-09 204 9.0 52 0.636655 33.106077
6 2006-10-22 222 8.0 66 0.581946 38.408408
7 2006-09-29 245 9.0 70 0.518825 36.317752
8 2006-09-16 258 11.0 68 0.486226 33.063381
9 2006-08-30 275 8.0 72 0.446667 32.160051
10 2006-02-11 475 5.0 65 0.164591 10.698423
With regards to the most efficient solution, that would depend on how one wants to measure efficiency. Assuming that one wants to measure the time of execution, one way that one can go about doing it is with time.perf_counter().
If one measures the time of execution for all the options above, one gets the following
method time
0 Option 1 0.00000110000837594271
1 Option 2.1 0.00000139995245262980
2 Option 2.2 0.00000369996996596456
3 Option 2.3 0.00000160001218318939
4 Option 3 0.00000110000837594271
5 Option 4 0.00000120000913739204
6 Option 5 0.00000140001066029072
7 Option 6 0.00000159995397552848
8 Option 7 0.00000150001142174006
However, this might change depending on the dataframe one uses, on the requirements (such as hardware), and more.
Notes:
-
There are various suggestions on using
inplace=True. Would suggest reading this: https://stackoverflow.com/a/59242208/7109869 -
There are also some people with strong opinions on
.apply(). Would suggest reading this: When should I (not) want to use pandas apply() in my code? -
If one has missing values, one might want to consider as well
pandas.DataFrame.dropna. Using the option 2, it would be something likedf = df[df['line_race'] != 0].dropna() -
There are additional ways to measure the time of execution, so I would recommend this thread: How do I get time of a Python program's execution?
so many options provided(or maybe i didnt pay much attention to it, sorry if its the case), but no one mentioned this:
we can use this notation in pandas: ~ (this gives us the inverse of the condition)
df = df[~df["line_race"] == 0]