Finding efficiently pandas (part of) rows with unique values - python

Given a pandas dataframe with a row per individual/record. A row includes a property value and its evolution across time (0 to N).
A schedule includes the estimated values of a variable 'property' for a number of entities from day 1 to day 10 in the following example.
I want to filter entities with unique values for a given period and get those values
csv=',property,1,2,3,4,5,6,7,8,9,10\n0,100011,0,0,0,0,3,3,3,3,3,0\n1,100012,0,0,0,0,2,2,2,8,8,0\n2, \
100012,0,0,0,0,2,2,2,2,2,0\n3,100012,0,0,0,0,0,0,0,0,0,0\n4,100011,0,0,0,0,2,2,2,2,2,0\n5, \
180011,0,0,0,0,2,2,2,2,2,0\n6,110012,0,0,0,0,0,0,0,0,0,0\n7,110011,0,0,0,0,3,3,3,3,3,0\n8, \
110012,0,0,0,0,3,3,3,3,3,0\n9,110013,0,0,0,0,0,0,0,0,0,0\n10,100011,0,0,0,0,3,3,3,3,4,0'
from StringIO import StringIO
import numpy as np
schedule = pd.read_csv(StringIO(csv), index_col=0)
print schedule
property 1 2 3 4 5 6 7 8 9 10
0 100011 0 0 0 0 3 3 3 3 3 0
1 100012 0 0 0 0 2 2 2 8 8 0
2 100012 0 0 0 0 2 2 2 2 2 0
3 100012 0 0 0 0 0 0 0 0 0 0
4 100011 0 0 0 0 2 2 2 2 2 0
5 180011 0 0 0 0 2 2 2 2 2 0
6 110012 0 0 0 0 0 0 0 0 0 0
7 110011 0 0 0 0 3 3 3 3 3 0
8 110012 0 0 0 0 3 3 3 3 3 0
9 110013 0 0 0 0 0 0 0 0 0 0
10 100011 0 0 0 0 3 3 3 3 4 0
I want to find records/individuals for who property has not changed during a given period and the corresponding unique values
Here is what i came with : I want to locate individuals with property in [100011, 100012, 1100012] between days 7 and 10
props = [100011, 100012, 1100012]
begin = 7
end = 10
res = schedule['property'].isin(props)
df = schedule.ix[res, begin:end]
print "df \n%s " %df
We have :
df
7 8 9
0 3 3 3
1 2 8 8
2 2 2 2
3 0 0 0
4 2 2 2
10 3 3 4
res = df.apply(lambda x: np.unique(x).size == 1, axis=1)
print "res : %s\n" %res
df_f = df.ix[res,]
print "df filtered %s \n" % df_f
res = pd.Series(df_f.values.ravel()).unique().tolist()
print "unique values : %s " %res
Giving :
res :
0 True
1 False
2 True
3 True
4 True
10 False
dtype: bool
df filtered
7 8 9
0 3 3 3
2 2 2 2
3 0 0 0
4 2 2 2
unique values : [3, 2, 0]
As those operations need to be run many times (in millions) on a million rows dataframe, i need to be able to run it as quickly as possible.
(#MaxU) : schedule can be seen as a database/repository updated many times. The repository is then requested as well many times for unique values
Would you have some ideas for improvements/ alternate ways ?


Given your df
7 8 9
0 3 3 3
1 2 8 8
2 2 2 2
3 0 0 0
4 2 2 2
10 3 3 4
You can simplify your code to:
df_f = df[df.apply(pd.Series.nunique, axis=1) == 1]
print(df_f)
7 8 9
0 3 3 3
2 2 2 2
3 0 0 0
4 2 2 2
And the final step to:
res = df_f.iloc[:,0].unique().tolist()
print(res)
[3, 2, 0]
It's not fully vectorised, but maybe this clarifies things a bit towards that?

Related

Increment the value in a new column based on a condition using an existing column

I have a pandas dataframe with two columns:
temp_1 flag
1 0
1 0
1 0
2 0
3 0
4 0
4 1
4 0
5 0
6 0
6 1
6 0
and I wanted to create a new column named "final" based on :
if "flag" has a value = 1 , then it increments "temp_1" by 1 and following values as well. If we find value = 1 again in flag column then the previous value in "final" with get incremented by 1 , please refer to expected output
I have tired using .cumsum() with filters but not getting the desired result.
Expected output
temp_1 flag final
1 0 1
1 0 1
1 0 1
2 0 2
3 0 3
4 0 4
4 1 5
4 0 5
5 0 6
6 0 7
6 1 8
6 0 8

Just do cumsum for flag:
>>> df['final'] = df['temp_1'] + df['flag'].cumsum()
>>> df
temp_1 flag final
0 1 0 1
1 1 0 1
2 1 0 1
3 2 0 2
4 3 0 3
5 4 0 4
6 4 1 5
7 4 0 5
8 5 0 6
9 6 0 7
10 6 1 8
11 6 0 8
>>>

how to add a DataFrame to some columns of another DataFrame

I want to add a DataFrame a (containing a loadprofile) to some of the columns of another DataFrame b (also containing one load profile per column). So some columns (load profiles) of b should be overlaid withe the load profile of a.
So lets say my DataFrames look like:
a:
P[kW]
0 0
1 0
2 0
3 8
4 8
5 0
b:
P1[kW] P2[kW] ... Pn[kW]
0 2 2 2
1 3 3 3
2 3 3 3
3 4 4 4
4 2 2 2
5 2 2 2
Now I want to overlay some colums of b:
b.iloc[:, [1]] += a.iloc[:, 0]
I would expect this:
b:
P1[kW] P2[kW] ... Pn[kW]
0 2 2 2
1 3 3 3
2 3 3 3
3 4 12 4
4 2 10 2
5 2 2 2
but what I actually get:
b:
P1[kW] P2[kW] ... Pn[kW]
0 2 nan 2
1 3 nan 3
2 3 nan 3
3 4 nan 4
4 2 nan 2
5 2 nan 2
That's not exactly what my code and data look like, but the principle is the same as in this abstract example.
Any guesses, what could be the problem?
Many thanks for any help in advance!
EDIT:
I actually have to overlay more than one column.Another example:
load = [0,0,0,0,0,0,0]
data = pd.DataFrame(load)
for i in range(1, 10):
data[i] = data[0]
data
overlay = pd.DataFrame([0,0,0,0,6,6,0])
overlay
data.iloc[:, [1,2,4,5,7,8]] += overlay.iloc[:, 0]
data
WHAT??! The result is completely crazy. Columns 1 and 2 aren't changed at all. Columns 4 and 5 are changed, but in every row. Columns 7 and 8 are nans. What am I missing?
That is what I would expect the result to look like:

Please do not pass the column index '1' of dataframe 'b' as a list but as an element.
Code
b.iloc[:, 1] += a.iloc[:, 0]
b
Output
P1[kW] P2[kW] Pn[kW]
0 2 2 2
1 3 3 3
2 3 3 3
3 4 12 4
4 2 10 2
5 2 2 2
Edit
Seems like this what we are looking for i.e to sum certain columns of data df with overlay df
Two Options
Option 1
cols=[1,2,4,5,7,8]
data[cols] = data[cols] + overlay.values
data
Option 2, if we want to use iloc
cols=[1,2,4,5,7,8]
data[cols] = data.iloc[:,cols] + overlay.iloc[:].values
data
Output
0 1 2 3 4 5 6 7 8 9
0 0 0 0 0 0 0 0 0 0 0
1 0 0 0 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0 0 0 0
4 0 6 6 0 6 6 0 6 6 0
5 0 6 6 0 6 6 0 6 6 0
6 0 0 0 0 0 0 0 0 0 0

Padding and reshaping pandas dataframe

I have a dataframe with the following form:
data = pd.DataFrame({'ID':[1,1,1,2,2,2,2,3,3],'Time':[0,1,2,0,1,2,3,0,1],
'sig':[2,3,1,4,2,0,2,3,5],'sig2':[9,2,8,0,4,5,1,1,0],
'group':['A','A','A','B','B','B','B','A','A']})
print(data)
ID Time sig sig2 group
0 1 0 2 9 A
1 1 1 3 2 A
2 1 2 1 8 A
3 2 0 4 0 B
4 2 1 2 4 B
5 2 2 0 5 B
6 2 3 2 1 B
7 3 0 3 1 A
8 3 1 5 0 A
I want to reshape and pad such that each 'ID' has the same number of Time values, the sig1,sig2 are padded with zeros (or mean value within ID) and the group carries the same letter value. The output after repadding would be :
data_pad = pd.DataFrame({'ID':[1,1,1,1,2,2,2,2,3,3,3,3],'Time':[0,1,2,3,0,1,2,3,0,1,2,3],
'sig1':[2,3,1,0,4,2,0,2,3,5,0,0],'sig2':[9,2,8,0,0,4,5,1,1,0,0,0],
'group':['A','A','A','A','B','B','B','B','A','A','A','A']})
print(data_pad)
ID Time sig1 sig2 group
0 1 0 2 9 A
1 1 1 3 2 A
2 1 2 1 8 A
3 1 3 0 0 A
4 2 0 4 0 B
5 2 1 2 4 B
6 2 2 0 5 B
7 2 3 2 1 B
8 3 0 3 1 A
9 3 1 5 0 A
10 3 2 0 0 A
11 3 3 0 0 A
My end goal is to ultimately reshape this into something with shape (number of ID, number of time points, number of sequences {2 here}).
It seems that if I pivot data, it fills in with nan values, which is fine for the signal values, but not the groups. I am also hoping to avoid looping through data.groupby('ID'), since my actual data has a large number of groups and the looping would likely be very slow.

Here's one approach creating the new index with pd.MultiIndex.from_product and using it to reindex on the Time column:
df = data.set_index(['ID', 'Time'])
# define a the new index
ix = pd.MultiIndex.from_product([df.index.levels[0],
df.index.levels[1]],
names=['ID', 'Time'])
# reindex using the above multiindex
df = df.reindex(ix, fill_value=0)
# forward fill the missing values in group
df['group'] = df.group.mask(df.group.eq(0)).ffill()
print(df.reset_index())
ID Time sig sig2 group
0 1 0 2 9 A
1 1 1 3 2 A
2 1 2 1 8 A
3 1 3 0 0 A
4 2 0 4 0 B
5 2 1 2 4 B
6 2 2 0 5 B
7 2 3 2 1 B
8 3 0 3 1 A
9 3 1 5 0 A
10 3 2 0 0 A
11 3 3 0 0 A

IIUC:
(data.pivot_table(columns='Time', index=['ID','group'], fill_value=0)
.stack('Time')
.sort_index(level=['ID','Time'])
.reset_index()
)
Output:
ID group Time sig sig2
0 1 A 0 2 9
1 1 A 1 3 2
2 1 A 2 1 8
3 1 A 3 0 0
4 2 B 0 4 0
5 2 B 1 2 4
6 2 B 2 0 5
7 2 B 3 2 1
8 3 A 0 3 1
9 3 A 1 5 0
10 3 A 2 0 0
11 3 A 3 0 0

Create new column where two columns from two different data frames are the same

I have two dataframes.
1:
pid cluster
7993355 0 0
8180238 0 5
8174589 0 7
8168267 0 10
8264548 0 10
8252159 0 0
8388741 0 6
8346358 0 2
8194226 0 8
8187866 0 3
8133728 0 1
8215624 0 6
8124250 0 0
8382996 0 5
8151852 0 0
8130044 0 2
8017035 0 5
8108438 0 0
8245152 0 1
8047538 0 3
8070691 0 7
8344660 0 5
8148647 0 6
8157608 0 10
8352127 0 8
2:
pid cluster count
0 0 0 8
1 0 1 2
2 0 2 3
3 0 3 2
4 0 4 1
5 0 5 5
6 0 6 4
7 0 7 3
8 0 8 4
9 0 10 3
My goal is to join these two dataframes when both pid and cluster are the same for example if the pid and cluster are both 0 I would like the next dataframe to have the value 8 for count.
I would like to this autonomously.
I have tried using a fucntion :train['count'] = np.where(((sample['pid'] == train['pid'])&(sample['cluster']==train['cluster'])), sample['count'], 0) But it doesnt work.
Pd.Merge etc will not work as both are of different dimensions I have only provide a small snippet of the dataframes.
Any help would be appreciated!!

Try this
df2[df2['pid'].isin(pd.unique(df1['pid'])) & df2['count'].isin(pd.unique(df1['count'])) ]

Merge multiple group ids to form a single consolidated group id?

I have following dataset in pandas Dataframe.
group_id sub_group_id
0 0
0 1
1 0
2 0
2 1
2 2
3 0
3 0
But the I want to those group ids and form a consolidated group id
group_id sub_group_id consolidated_group_id
0 0 0
0 1 1
1 0 2
2 0 3
2 1 4
2 2 5
2 2 5
3 0 6
3 0 6
Is there any generic or mathematical way to do it?

cols = ['group_id', 'sub_group_id']
df.assign(
consolidated_group_id=pd.factorize(
pd.Series(list(zip(*df[cols].values.T.tolist())))
)[0]
)
group_id sub_group_id consolidated_group_id
0 0 0 0
1 0 1 1
2 1 0 2
3 2 0 3
4 2 1 4
5 2 2 5
6 3 0 6
7 3 0 6

You need convert values to tuples and then use factorize:
df['consolidated_group_id'] = pd.factorize(df.apply(tuple,axis=1))[0]
print (df)
group_id sub_group_id consolidated_group_id
0 0 0 0
1 0 1 1
2 1 0 2
3 2 0 3
4 2 1 4
5 2 2 5
6 3 0 6
7 3 0 6
Numpy solutions are a bit modify this answer - change ordering by [::-1] with selecting by [0] for return array (numpy.unique):
a = df.values
def unique_return_inverse_2D(a): # a is array
a1D = a.dot(np.append((a.max(0)+1)[:0:-1].cumprod()[::-1],1))
return np.unique(a1D, return_inverse=1)[::-1][0]
def unique_return_inverse_2D_viewbased(a): # a is array
a = np.ascontiguousarray(a)
void_dt = np.dtype((np.void, a.dtype.itemsize * np.prod(a.shape[1:])))
return np.unique(a.view(void_dt).ravel(), return_inverse=1)[::-1][0]
df['consolidated_group_id'] = unique_return_inverse_2D(a)
df['consolidated_group_id1'] = unique_return_inverse_2D_viewbased(a)
print (df)
group_id sub_group_id consolidated_group_id consolidated_group_id1
0 0 0 0 0
1 0 1 1 1
2 1 0 2 2
3 2 0 3 3
4 2 1 4 4
5 2 2 5 5
6 3 0 6 6
7 3 0 6 6

Categories

Resources