I am working with a Pandas dataframe where each element contains a list of values. I would like to run a regression between the lists in the first column and the lists in each subsequent column for every row in the dataframe, and store the t-stats of each regression (currently using a numpy array to store them). I am able to do this using a nested for loop that loops through each row and column, but the performance is not optimal for the amount of data I am working with.
Here is a quick sample of what I have so far:
import numpy as np
import pandas as pd
from scipy.stats import linregress
df = pd.DataFrame(
{'a': [list(np.random.rand(11)) for i in range(100)],
'b': [list(np.random.rand(11)) for i in range(100)],
'c': [list(np.random.rand(11)) for i in range(100)],
'd': [list(np.random.rand(11)) for i in range(100)],
'e': [list(np.random.rand(11)) for i in range(100)],
'f': [list(np.random.rand(11)) for i in range(100)]
}
)
Here is what the data looks like:
a b c d e f
0 [0.279347961395256, 0.07198822780319691, 0.209... [0.4733815106836531, 0.5807425586417414, 0.068... [0.9377037591435088, 0.9698329284595916, 0.241... [0.03984770879654953, 0.650429630364027, 0.875... [0.04654151678901641, 0.1959629573862498, 0.36... [0.01328000288459652, 0.10429773699794731, 0.0...
1 [0.1739544898167934, 0.5279297754363472, 0.635... [0.6464841177367048, 0.004013634850660308, 0.2... [0.0403944630279538, 0.9163938509072009, 0.350... [0.8818108296208096, 0.2910758930807579, 0.739... [0.5263032002243185, 0.3746299115677546, 0.122... [0.5511171062367501, 0.327702669239891, 0.9147...
2 [0.49678125158054476, 0.807770957943305, 0.396... [0.6218806473477556, 0.01720135741717188, 0.15... [0.6110516368605904, 0.20848099927159314, 0.51... [0.7473669581190695, 0.5107081859246958, 0.442... [0.8231961741887535, 0.9686869510163731, 0.473... [0.34358121300094313, 0.9787339533782848, 0.72...
3 [0.7672751789941814, 0.412055981587398, 0.9951... [0.8470471648467321, 0.9967427749160083, 0.818... [0.8591072331661481, 0.6279199806511635, 0.365... [0.9456189188046846, 0.5084362869897466, 0.586... [0.2685328112579779, 0.8893788305422594, 0.235... [0.029919732007230193, 0.6377951981939682, 0.1...
4 [0.21420195955828203, 0.15178914447352077, 0.9... [0.6865307542882283, 0.0620359602798356, 0.382... [0.6469510945986712, 0.676059598071864, 0.0396... [0.2320436872397288, 0.09558341089961908, 0.98... [0.7733653233006889, 0.2405189745554751, 0.016... [0.8359561624563979, 0.24335481664355396, 0.38...
... ... ... ... ... ... ...
95 [0.42373270776373506, 0.7731750012629109, 0.90... [0.9430465078763153, 0.8506292743184455, 0.567... [0.41367168515273345, 0.9040247409476362, 0.72... [0.23016875953835192, 0.8206550830081965, 0.26... [0.954233948805146, 0.995068745046983, 0.20247... [0.26269690906898413, 0.5032835345055103, 0.26...
96 [0.36114607798432685, 0.11322299769211142, 0.0... [0.729848741496316, 0.9946930423163686, 0.2265... [0.17207915211677138, 0.3270055732644267, 0.73... [0.13211243241239223, 0.28382298905995607, 0.2... [0.03915259352564071, 0.05639914089770948, 0.0... [0.12681415759423675, 0.006417761276839351, 0....
97 [0.5020186971295065, 0.04018166955309821, 0.19... [0.9082402680300308, 0.1334790715379094, 0.991... [0.7003469664104871, 0.9444397336912727, 0.113... [0.7982221018200218, 0.9097963438776192, 0.163... [0.07834894180973451, 0.7948519146738178, 0.56... [0.5833962514812425, 0.403689767723475, 0.7792...
98 [0.16413822314461857, 0.40683312270714234, 0.4... [0.07366489230864415, 0.2706766599711766, 0.71... [0.6410967759869383, 0.5780018716586993, 0.622... [0.5466463581695835, 0.4949639043264169, 0.749... [0.40235314091318986, 0.8305539205264385, 0.35... [0.009668651763079184, 0.8071825962911674, 0.0...
99 [0.8189246990381518, 0.69175150213841, 0.82687... [0.40469941577758317, 0.49004906937461257, 0.7... [0.4940080411615112, 0.33621539942693246, 0.67... [0.8637418291877355, 0.34876318713083676, 0.09... [0.3526913672876807, 0.5177762589812651, 0.746... [0.3463129199717484, 0.9694802522161138, 0.732...
100 rows × 6 columns
My code to run the regressions and store the t-stats:
rows = len(df)
cols = len(df.columns)
tstats = np.zeros(shape=(rows,cols-1))
for i in range(0,rows):
for j in range(1,cols):
lg = linregress(df.iloc[i,0],df.iloc[i,j])
tstats[i,j-1] = lg.slope/lg.stderr
The code above works just fine and is doing exactly what I need, however as I mentioned above the performance begins to slow down when the # of rows and columns in df increases substantially.
I'm hoping someone could offer advice on how to optimize my code for better performance.
Thank you!
I am newbie to this but I do optimization your original code:
by purely use python builtin list object (there is no need to use pandas and to be honest I cannot find a better way to solve your problem in pandas than you original code :D)
by using numpy, which should be (at least they claimed) faster than python builtin list.
You can jump to see the code, its in Jupyter notebook format so you need to install Jupyter first.
Conclusion
Here is the test result:
On a (100, 100) matrix containing (30,) length random lists,
the total time difference is around 1 second.
Time elapsed to run 1 times on new method is 24.282760 seconds.
Time elapsed to run 1 times on old method is 25.954801 seconds.
Refer to
test_perf
in sample code for result.
PS: During test only one thread is used, so maybe multi-thread will help to improve performance, but that's out of my ability...
Idea
I think numpy.nditer is suitable for your request, though the result of optimization is not that significant. Here is my idea:
Generate the input array
I have altered you first part of script, I think using list comprehension along is enough to build a matrix of random lists. Refer to
get_matrix_from_builtin.
Please note I have stored the random lists in another 1-element tuple to keep the shape as ndarray generate from numpy.
As a compare, you can also construct such matrix with numpy. Refer to
get_matrix_from_numpy.
Because ndarray try to boardcast list-like object (and I don't know how to stop it), I have to wrap it into a tuple to avoid auto boardcast from numpy.array constructor. If anyone have a better solution please note it, thanks :)
Calculate the result
I altered you original code using pandas.DataFrame to access element by row/col index, but it is not that way.
Pandas provides some iteration tool for DataFrame: pipe, apply, agg, and appymap, search API for more info, but it seems not suitable for your request here, as you want to obtain the current index of row and col during iteration.
I searched and found numpy.nditer can provide that needs: it return a iterator of ndarray, which have an attribution multi_index that provide the row/col pair of current element. see iterating-over-arrays
Explain on solve.ipynb
I use Jupyter Notebook to test this, you might need got one, here is the instruction of install.
I have altered your original code, which remove the request of pandas and purely used builtin list. Refer to
old_calc_tstat
in the sample code.
Also, I used numpy.nditer to calc your tstats matrix, Refer to
new_calc_tstat
in the sample code.
Then, I tested if the result of both methods are equal, I used same input array to ensure random won't affect the test. Refer to
test_equal
for result.
Finally, do the time performance. I am not patient so I only run it for one time, you may add the repeats count of test in the
test_perf function.
The code
# To add a new cell, type '# %%'
# To add a new markdown cell, type '# %% [markdown]'
# %% [markdown]
# [origin question](https://stackoverflow.com/questions/69228572/running-scipy-linregress-across-dataframe-where-each-element-is-a-list)
#
# %%
import sys
import time
import numpy as np
from scipy.stats import linregress
# %%
def get_matrix_from_builtin():
# use builtin list to construct matrix of random list
# note I put random list inside a tuple to keep it same shape
# as I later use numpy to do the same thing.
return [
[(list(np.random.rand(11)),)
for col in range(6)]
for row in range(100)
]
# %timeit get_matrix_from_builtin()
# %%
def get_matrix_from_numpy(
gen=np.random.rand,
shape=(1, 1),
nest_shape=(1, ),
):
# custom dtype for random lists
mydtype = [
('randonlist', 'f', nest_shape)
]
a = np.empty(shape, dtype=mydtype)
# [DOC] moditfying array values
# https://numpy.org/doc/stable/reference/arrays.nditer.html#modifying-array-values
# enable per operation flags 'readwrite' to modify element in ndarray
# enable global flag 'refs_ok' to allow use callable function 'gen' in iteration
with np.nditer(a, op_flags=['readwrite'], flags=['refs_ok']) as it:
for x in it:
# pack list in a 1-d turple to prevent numpy boardcast it
x[...] = (gen(nest_shape[0]), )
return a
def test_get_matrix_from_numpy():
gen = np.random.rand # generator of random list
shape = (6, 100) # shape of matrix to hold random lists
nest_shape = (11, ) # shape of random lists
return get_matrix_from_numpy(gen, shape, nest_shape)
# access a random list by a[row][col][0]
# %timeit test_get_matrix_from_numpy()
# %%
def test_get_matrix_from_numpy():
gen = np.random.rand
shape = (6, 100)
nest_shape = (11, )
return get_matrix_from_numpy(gen, shape, nest_shape)
# %%
def old_calc_tstat(a=None):
if a is None:
a = get_matrix_from_builtin()
a = np.array(a)
rows, cols = a.shape[:2]
tstats = np.zeros(shape=(rows, cols))
for i in range(0, rows):
for j in range(1, cols):
lg = linregress(a[i][0][0], a[i][j][0])
tstats[i, j-1] = lg.slope/lg.stderr
return tstats
# %%
def new_calc_tstat(a=None):
# read input metrix of random lists
if a is None:
gen = np.random.rand
shape = (6, 100)
nest_shape = (11, )
a = get_matrix_from_numpy(gen, shape, nest_shape)
# construct ndarray for t-stat result
tstats = np.empty(a.shape)
# enable global flags 'multi_index' to retrive index of current element
# [DOC] Tracking an Index or Multi-Index
# https://numpy.org/doc/stable/reference/arrays.nditer.html#tracking-an-index-or-multi-index
it = np.nditer(tstats, op_flags=['readwrite'], flags=['multi_index'])
# obtain total columns count of tstats's shape
col = tstats.shape[1]
for x in it:
i, j = it.multi_index
# trick to avoid IndexError: substract len(list) after +1 to index
j = j + 1 - col
lg = linregress(
a[i][0][0],
a[i][j][0]
)
# note: nditer ignore ZeroDivisionError by default, and return np.inf to the element
# you have to override it manually:
if lg.stderr == 0:
x[...] = 0
else:
x[...] = lg.slope / lg.stderr
return tstats
# new_calc_tstat()
# %%
def test_equal():
"""Test if the new method has equal output to old one"""
# use same input list to avoid affect of rand
a = test_get_matrix_from_numpy()
old = old_calc_tstat(a)
new = new_calc_tstat(a)
print(
"Is the shape of old and new same ?\n%s. old: %s, new: %s\n" % (
old.shape == new.shape, old.shape, new.shape),
)
res = (old == new)
print(
"Is the result object same?"
)
if res.all() == True:
print("True.")
else:
print("False. Difference(new - old) as below:\n")
print(new - old)
return old, new
old, new = test_equal()
# %%
# the only diff is the last element
# in old method it is 0
# in new method it is inf
# if you perfer the old method, just add condition in new method to override
# [new[x][99] for x in range(6)]
# %%
# python version: 3.8.8
timer = time.clock if sys.platform[:3] == 'win' else time.time
def total(func, *args, _reps=1, **kwargs):
start = timer()
for i in range(_reps):
ret = func(*args, **kwargs)
elapsed = timer() - start
return elapsed
def test_perf():
"""Test of performance"""
# first, get a larger input array
gen = np.random.rand
shape = (1000, 100)
nest_shape = (30, )
a = get_matrix_from_numpy(gen, shape, nest_shape)
# repeat how many time for each test
reps = 1
# then, time both old and new calculation method
old = total(old_calc_tstat, a, _reps=reps)
new = total(new_calc_tstat, a, _reps=reps)
msg = "Time elapsed to run %d times on %s is %f seconds."
print(msg % (reps, 'new method', new))
print(msg % (reps, 'old method', old))
test_perf()
I have a vector, it is np.array([[1,2,3]]).
I want to save the vector and the name of the vector in the same row.
For example
Vector1 1 2 3
Vector2 4 5 6
Vector3 7 8 9
Then, I have ever tried like this
import csv
import numpy as np
a=np.array([[1,2,3]])
b='Vector1'
c=[b,a]
with open ('testfile.csv','ab') as fxx:
w=csv.writer(fxx)
for row in c:
w.writerow(row)
the result is
then I also have tried this
import csv
import numpy as np
a=np.array([[1,2,3]])
b='Vector1'
c=np.append(b,a)
with open ('testfile3.csv','ab') as fxx:
w=csv.writer(fxx)
for row in c:
w.writerow(row)
And then the result is
But the result what I want is
import csv
import numpy as np
a=np.array([[1,2,3]])
b='Vector1'
c=np.append(b,a)
print(c)
with open ('testfile3.csv','a') as fxx:
w=csv.writer(fxx)
w.writerow(c)
writerow() expects a sequence where each element will fill a column, ie
`writer.writerow(["foo", 42, "bar")
will result in (quoting and delimiter depending on your writer params):
"foo",42,"bar"
In your code you create a list c as:
c=['Vector1', [[1, 2, 3]]]
ie your first element is a string and the second a list of one element which is another list.
then iterate over this list to pass each of it's elements to your writer, so basically what you're doing is:
writer.writerow("Vector1")
writer.writerow([[1, 2, 3]])
For the first line, since strings are sequences, it will fill one column per character resulting in (quoting and delim depending on your writer config) :
"V","e","c","t","o","r","1"
and for the second, since you pass a list of one element, it will fill one single column with the string representation of the single element in the list:
"[1, 2, 3]"
If what you want is
"Vector1",1,2,3
then the obvious solution is to pass the correct sequence to writerow:
writer.writerow(["Vector1", 1, 2, 3])
Now you just have to change your code to correctly produce this sequence...
import pandas as pd
a = [[1, 2, 3]]
b = 'Vector1'
a[0].append(b)
print(a)
df = pd.DataFrame(a)
print(df)
df.to_csv('data.csv')
output:
0 1 2 3
0 1 2 3 Vector1
I have a pandas dataframe which has a column 'INTENSITY' and a numpy array of same length containing the error for each intensity. I would like to generate columns with randomly generated numbers in the error range.
So far I use two nested for loops to create the new columns but I feel like this is inefficient:
theor_err = [ sqrt(abs(x)) for x in theor_df[str(INTENSITY)] ]
theor_err = np.asarray(theor_err)
for nr_sample in range(2):
sample = np.zeros(len(theor_df[str(INTENSITY)]))
for i, error in enumerate(theor_err):
sample[i] = theor_df[str(INTENSITY)][i] + random.uniform(-error, error)
theor_df['gen_{}'.format(nr_sample)] = Series(sample, index=theor_df.index)
theor_df.head()
Is there a more efficient way of approaching a problem like this?
Numpy can handle arrays for you. So, you can do it like this:
import pandas as pd
import numpy as np
a=pd.DataFrame([10,20,15,30],columns=['INTENSITY'])
a['theor_err']=np.sqrt(np.abs(a.INTENSITY))
a['sample']=np.random.uniform(-a['theor_err'],a['theor_err'])
Suppose you want to generate 6 samples. You can try to code below. You can tune the number of samples you want by setting the value k.
df = pd.DataFrame([[1],[2],[3],[4],[-5]], columns=["intensity"])
k = 6
sample_names = ["sample" + str(i+1) for i in range(k)]
df["err"] = np.sqrt(np.abs((df["intensity"])))
df[sample_names] = pd.DataFrame(
df["err"].map(lambda x: np.random.uniform(-x, x, k)).values.tolist())
df.loc[:,sample_names] = df.loc[:,sample_names].add(df.intensity, axis=0)
I am new to python, I really appreciate it if you could help me.
I have a 2 column array i.e. d.T, and a 1 column array i.e. result and want to unite them in a 3 column array, I tried many times around but could not find the best way to do so, even I tried np.vstack but it doesn't work due to different dimensions.
import numpy as np
import math
n=3
m=3
T=4;
xmin=0; xmax=l=4
zmin=0; zmax=h=2
nx=5; nz=5
dx=(xmax-xmin)*1.0/(nx-1)
dz=(zmax-zmin)*1.0/(nz-1)
dt=0.00001
nt=1
k_z=n*2*math.pi/h
k_x=m*2*math.pi/l
w_theo=np.zeros((nz,nx),dtype='float64')
xx=[]
for i in range(0,nx):
xx.append(i*dx)
zz=[]
for k in range(0,nz):
zz.append(k*dz)
[x,z]=np.meshgrid(xx,zz)
for i in range(0,nz):
for k in range(0,nx):
t=0+nt*dt; omega=2*math.pi/T;
w_theo[i,k]=round(np.sin(k_z*i*dz*1.0)*np.sin(k_x*k*dx*1.0-omega*t),10)
print w_theo
np.savetxt('Theoretical_result.txt', np.array(w_theo), delimiter="\t")
d = np.array([x.flatten(), z.flatten()])
result=[]
for i in range(0,nz):
for k in range(0,nx):
result.append(w_theo[nz-1-i,k])
myarray=np.asarray(result)
print myarray.shape, d.T.shape`
# data=[]
# data=np.vstack((d.T,myarray))
# np.savetxt('datafile_id', data)
Try
data = np.column_stack((d.T, myarray))
No need for data = []
I'm trying to write a function that takes a continuous time series and returns a data structure which describes any missing gaps in the data (e.g. a DF with columns 'start' and 'end'). It seems like a fairly common issue for time series, but despite messing around with groupby, diff, and the like -- and exploring SO -- I haven't been able to come up with much better than the below.
It's a priority for me that this use vectorized operations to remain efficient. There has got to be a more obvious solution using vectorized operations -- hasn't there? Thanks for any help, folks.
import pandas as pd
def get_gaps(series):
"""
#param series: a continuous time series of data with the index's freq set
#return: a series where the index is the start of gaps, and the values are
the ends
"""
missing = series.isnull()
different_from_last = missing.diff()
# any row not missing while the last was is a gap end
gap_ends = series[~missing & different_from_last].index
# count the start as different from the last
different_from_last[0] = True
# any row missing while the last wasn't is a gap start
gap_starts = series[missing & different_from_last].index
# check and remedy if series ends with missing data
if len(gap_starts) > len(gap_ends):
gap_ends = gap_ends.append(series.index[-1:] + series.index.freq)
return pd.Series(index=gap_starts, data=gap_ends)
For the record, Pandas==0.13.1, Numpy==1.8.1, Python 2.7
This problem can be transformed to find the continuous numbers in a list. find all the indices where the series is null, and if a run of (3,4,5,6) are all null, you only need to extract the start and end (3,6)
import numpy as np
import pandas as pd
from operator import itemgetter
from itertools import groupby
# create an example
data = [2, 3, 4, 5, 12, 13, 14, 15, 16, 17]
s = pd.series( data, index=data)
s = s.reindex(xrange(18))
print find_gap(s)
def find_gap(s):
""" just treat it as a list
"""
nullindex = np.where( s.isnull())[0]
ranges = []
for k, g in groupby(enumerate(nullindex), lambda (i,x):i-x):
group = map(itemgetter(1), g)
ranges.append((group[0], group[-1]))
startgap, endgap = zip(* ranges)
return pd.series( endgap, index= startgap )
reference : Identify groups of continuous numbers in a list