I would like to remove all rows in a pandas df that have an index value within 4 counts of the index value of the previous row.
In the pandas df below,
A B
0 1 1
5 5 5
8 9 9
9 10 10
Only the row with index value 0 should remain.
Thanks!
get the differences between the current and previous row as a list and pass to loc. Chose to get it as a list so i could return a dataframe as a final output.
ind = [ a for a,b in zip(df.index,df.index[1:]) if b-a > 4]
df.loc[ind]
A B
0 1 1
You can use reset_index, diff and shift:
In [1309]: df
Out[1309]:
A B
0 1 1
5 5 5
8 9 9
9 10 10
In [1310]: d = df.reset_index()
In [1313]: df = d[d['index'].diff(1).shift(-1) >=4].drop('index', 1)
In [1314]: df
Out[1313]:
A B
0 1 1
I have a dataset containing 3 columns, I’m trying to group them and print each group in sorted fashion (based on highest value in each group). The records in each group also have to be in sorted fashion.
Dataset looks like below.
key1,key2,val
b,y,21
c,y,25
c,z,10
b,x,20
b,z,5
c,x,17
a,x,15
a,y,18
a,z,100
df=pd.read_csv('/tmp/hello.csv')
df['max'] = df.groupby(['key1'])['val'].transform('max')
dff=df.sort_values(['max', 'val'], ascending=False).drop('max', axis=1)
I'm applying transform as it works per group basis and then sorting the values.
Above code results in my desired dataframe:
a,z,100
a,y,18
a,x,15
c,y,25
c,x,17
c,z,10
b,y,21
b,x,20
b,z,5
But, the same code fails for below dataset.
key1,key2,val
b,y,10
c,y,10
c,z,10
b,x,2
b,z,2
c,x,2
a,x,2
a,y,2
a,z,2
Below is the desired output
key1,key2,val
c,y,10
c,z,10
c,x,2
b,y,10
b,x,2
b,z,2
a,x,2
a,y,2
a,z,2
Please help me in properly grouping and sorting the dataframe for my scenario.
Add column key1 to sort_values because in second DataFrame are multiple maximum values 10 per groups, so sorting cannot distingush groups:
df['max'] = df.groupby(['key1'])['val'].transform('max')
dff=df.sort_values(['max','key1', 'val'], ascending=False).drop('max', axis=1)
print (dff)
key1 key2 val
8 a z 100
7 a y 18
6 a x 15
1 c y 25
5 c x 17
2 c z 10
0 b y 21
3 b x 20
4 b z 5
df['max'] = df.groupby(['key1'])['val'].transform('max')
dff=df.sort_values(['max','key1', 'val'], ascending=False).drop('max', axis=1)
print (dff)
key1 key2 val
1 c y 10
2 c z 10
5 c x 2
0 b y 10
3 b x 2
4 b z 2
6 a x 2
7 a y 2
8 a z 2
I have a dataframe
A B C
1 2 3
2 3 4
3 8 7
I want to take only rows where there is a sequence of 3,4 in columns C (in this scenario - first two rows)
What will be the best way to do so?
You can use rolling for general solution working with any pattern:
pat = np.asarray([3,4])
N = len(pat)
mask= (df['C'].rolling(window=N , min_periods=N)
.apply(lambda x: (x==pat).all(), raw=True)
.mask(lambda x: x == 0)
.bfill(limit=N-1)
.fillna(0)
.astype(bool))
df = df[mask]
print (df)
A B C
0 1 2 3
1 2 3 4
Explanation:
use rolling.apply and test pattern
replace 0s to NaNs by mask
use bfill with limit for filling first NANs values by last previous one
fillna NaNs to 0
last cast to bool by astype
Use shift
In [1085]: s = df.eq(3).any(1) & df.shift(-1).eq(4).any(1)
In [1086]: df[s | s.shift()]
Out[1086]:
A B C
0 1 2 3
1 2 3 4
I know how to set the pandas data frame equal to a column.
i.e.:
df = df['col1']
what is the equivalent for a row? let's say taking the index? and would I eliminate one or more of them?
Many thanks.
If you want to take a copy of a row then you can either use loc for label indexing or iloc for integer based indexing:
In [104]:
df = pd.DataFrame({'a':np.random.randn(10),'b':np.random.randn(10)})
df
Out[104]:
a b
0 1.216387 -1.298502
1 1.043843 0.379970
2 0.114923 -0.125396
3 0.531293 -0.386598
4 -0.278565 1.224272
5 0.491417 -0.498816
6 0.222941 0.183743
7 0.322535 -0.510449
8 0.695988 -0.300045
9 -0.904195 -1.226186
In [106]:
row = df.iloc[3]
row
Out[106]:
a 0.531293
b -0.386598
Name: 3, dtype: float64
If you want to remove that row then you can use drop:
In [107]:
df.drop(3)
Out[107]:
a b
0 1.216387 -1.298502
1 1.043843 0.379970
2 0.114923 -0.125396
4 -0.278565 1.224272
5 0.491417 -0.498816
6 0.222941 0.183743
7 0.322535 -0.510449
8 0.695988 -0.300045
9 -0.904195 -1.226186
You can also use a slice or pass a list of labels:
In [109]:
rows = df.loc[[3,5]]
row_slice = df.loc[3:5]
print(rows)
print(row_slice)
a b
3 0.531293 -0.386598
5 0.491417 -0.498816
a b
3 0.531293 -0.386598
4 -0.278565 1.224272
5 0.491417 -0.498816
Similarly you can pass a list to drop:
In [110]:
df.drop([3,5])
Out[110]:
a b
0 1.216387 -1.298502
1 1.043843 0.379970
2 0.114923 -0.125396
4 -0.278565 1.224272
6 0.222941 0.183743
7 0.322535 -0.510449
8 0.695988 -0.300045
9 -0.904195 -1.226186
If you wanted to drop a slice then you can slice your index and pass this to drop:
In [112]:
df.drop(df.index[3:5])
Out[112]:
a b
0 1.216387 -1.298502
1 1.043843 0.379970
2 0.114923 -0.125396
5 0.491417 -0.498816
6 0.222941 0.183743
7 0.322535 -0.510449
8 0.695988 -0.300045
9 -0.904195 -1.226186
We can use .idxmax to get the maximum value of a dataframe(df). My problem is that I have a df with several columns (more than 10), one of a column has identifiers of same value. I need to extract the identifiers with the maximum value:
>df
id value
a 0
b 1
b 1
c 0
c 2
c 1
Now, this is what I'd want:
>df
id value
a 0
b 1
c 2
I am trying to get it by using df.groupy(['id']), but it is a bit tricky:
df.groupby(["id"]).ix[df['value'].idxmax()]
Of course, that doesn't work. I fear that I am not on the right path, so I thought I'd ask you guys! Thanks!
Close! Groupby the id, then use the value column; return the max for each group.
In [14]: df.groupby('id')['value'].max()
Out[14]:
id
a 0
b 1
c 2
Name: value, dtype: int64
Op wants to provide these locations back to the frame, just create a transform and assign.
In [17]: df['max'] = df.groupby('id')['value'].transform(lambda x: x.max())
In [18]: df
Out[18]:
id value max
0 a 0 0
1 b 1 1
2 b 1 1
3 c 0 2
4 c 2 2
5 c 1 2