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length of list len(list) resulting wrong value in Python

It might sound trivial but I am surprised by the output. Basically, I have am calculating y = m*x + b for given a, b & x. With below code I am able to get the desired result of y which a list of 20 values.
But when I am checking the length of the list, I am getting 1 in return. And the range is (0,1) which is weird as I was expecting it to be 20.
Am I making any mistake here?
a = 10
b = 0
x = df['x']
print(x)
0 0.000000
1 0.052632
2 0.105263
3 0.157895
4 0.210526
5 0.263158
6 0.315789
7 0.368421
8 0.421053
9 0.473684
10 0.526316
11 0.578947
12 0.631579
13 0.684211
14 0.736842
15 0.789474
16 0.842105
17 0.894737
18 0.947368
19 1.000000
y_new = []
for i in x:
y = a*x +b
y_new.append(y)
len(y_new)
Output: 1
print(y_new)
[0 0.000000
1 0.526316
2 1.052632
3 1.578947
4 2.105263
5 2.631579
6 3.157895
7 3.684211
8 4.210526
9 4.736842
10 5.263158
11 5.789474
12 6.315789
13 6.842105
14 7.368421
15 7.894737
16 8.421053
17 8.947368
18 9.473684
19 10.000000
Name: x, dtype: float64]

I would propose two solutions:
The first solution is : you convert your columnn df['x'] into a list by doing df['x'].tolist() and you re-run your code and also you should replace ax+b by ai+b
The second solution is (which I would do): You convert your df['x'] into an array by doing x = np.array(df['x']). By doing this you can do some array broadcasting.
So, your code will simply be :
x = np.array(df['x'])
y = a*x + b
This should give you the desired output.
I hope this would be helpful

With the code below, I have a length of 20 for the array y_new. Are you sure to print the right value? According to this post, df['x'] returns a panda Series so df['x'] is equivalent to pd.Series(...).
df['x'] — index a column named 'x'. Returns pd.Series
import pandas as pd
a = 10
b = 0
x = pd.Series(data=[0.000000,0.052632,0.105263,0.157895,0.210526, 0.263158, 0.315789, 0.368421, 0.421053,0.473684,0.526316,0.578947,0.631579
,0.684211,0.736842,0.789474,0.842105,0.894737,0.947368,1.000000])
y_new = []
for i in x:
y = a*x +b
y_new.append(y)
print("y_new length: " + str(len(y_new)) )
Output:
y_new length: 20

Calculating grid values given the distance in python

I have a cell grid of big dimensions. Each cell has an ID (p1), cell value (p3) and coordinates in actual measures (X, Y). This is how first 10 rows/cells look like
p1 p2 p3 X Y
0 0 0.0 0.0 0 0
1 1 0.0 0.0 100 0
2 2 0.0 12.0 200 0
3 3 0.0 0.0 300 0
4 4 0.0 70.0 400 0
5 5 0.0 40.0 500 0
6 6 0.0 20.0 600 0
7 7 0.0 0.0 700 0
8 8 0.0 0.0 800 0
9 9 0.0 0.0 900 0
Neighbouring cells of cell i in the p1 can be determined as (i-500+1, i-500-1, i-1, i+1, i+500+1, i+500-1).
For example: p1 of 5 has neighbours - 4,6,504,505,506. (these are the ID of rows in the upper table - p1).
What I am trying to is:
For the chosen value/row i in p1, I would like to know all neighbours in the chosen distance from i and sum all their p3 values.
I tried to apply this solution (link), but I don't know how to incorporate the distance parameter. The cell value can be taken with df.iloc, but the steps before this are a bit tricky for me.
Can you give me any advice?
EDIT:
Using the solution from Thomas and having df called CO:
p3
0 45
1 580
2 12000
3 12531
4 22456
I'd like to add another column and use the values from p3 columns
CO['new'] = format(sum_neighbors(data, CO['p3']))
But it doesn't work. If I add a number instead of a reference to row CO['p3'] it works like charm. But how can I use values from p3 column automatically in format function?
SOLVED:
It worked with:
CO['new'] = CO.apply(lambda row: sum_neighbors(data, row.p3), axis=1)

Solution:
import numpy as np
import pandas
# Generating toy data
N = 10
data = pandas.DataFrame({'p3': np.random.randn(N)})
print(data)
# Finding neighbours
get_candidates = lambda i: [i-500+1, i-500-1, i-1, i+1, i+500+1, i+500-1]
filter = lambda neighbors, N: [n for n in neighbors if 0<=n<N]
get_neighbors = lambda i, N: filter(get_candidates(i), N)
print("Neighbors of 5: {}".format(get_neighbors(5, len(data))))
# Summing p3 on neighbors
def sum_neighbors(data, i, col='p3'):
return data.iloc[get_neighbors(i, len(data))][col].sum()
print("p3 sum on neighbors of 5: {}".format(sum_neighbors(data, 5)))
Output:
p3
0 -1.106541
1 -0.760620
2 1.282252
3 0.204436
4 -1.147042
5 1.363007
6 -0.030772
7 -0.461756
8 -1.110459
9 -0.491368
Neighbors of 5: [4, 6]
p3 sum on neighbors of 5: -1.1778133703169344
Notes:
I assumed p1 was range(N) as seemed to be implied (so we don't need it at all).
I don't think that 505 is a neighbour of 5 given the list of neighbors of i defined by the OP.

Python: How to create weighted quantiles in Pandas?

I understand how to create simple quantiles in Pandas using pd.qcut. But after searching around, I don't see anything to create weighted quantiles. Specifically, I wish to create a variable which bins the values of a variable of interest (from smallest to largest) such that each bin contains an equal weight. So far this is what I have:
def wtdQuantile(dataframe, var, weight = None, n = 10):
if weight == None:
return pd.qcut(dataframe[var], n, labels = False)
else:
dataframe.sort_values(var, ascending = True, inplace = True)
cum_sum = dataframe[weight].cumsum()
cutoff = max(cum_sum)/n
quantile = cum_sum/cutoff
quantile[-1:] -= 1
return quantile.map(int)
Is there an easier way, or something prebuilt from Pandas that I'm missing?
Edit: As requested, I'm providing some sample data. In the following, I'm trying to bin the "Var" variable using "Weight" as the weight. Using pd.qcut, we get an equal number of observations in each bin. Instead, I want an equal weight in each bin, or in this case, as close to equal as possible.
Weight Var pd.qcut(n=5) Desired_Rslt
10 1 0 0
14 2 0 0
18 3 1 0
15 4 1 1
30 5 2 1
12 6 2 2
20 7 3 2
25 8 3 3
29 9 4 3
45 10 4 4

I don't think this is built-in to Pandas, but here is a function that does what you want in a few lines:
import numpy as np
import pandas as pd
from pandas._libs.lib import is_integer
def weighted_qcut(values, weights, q, **kwargs):
'Return weighted quantile cuts from a given series, values.'
if is_integer(q):
quantiles = np.linspace(0, 1, q + 1)
else:
quantiles = q
order = weights.iloc[values.argsort()].cumsum()
bins = pd.cut(order / order.iloc[-1], quantiles, **kwargs)
return bins.sort_index()
We can test it on your data this way:
data = pd.DataFrame({
'var': range(1, 11),
'weight': [10, 14, 18, 15, 30, 12, 20, 25, 29, 45]
})
data['qcut'] = pd.qcut(data['var'], 5, labels=False)
data['weighted_qcut'] = weighted_qcut(data['var'], data['weight'], 5, labels=False)
print(data)
The output matches your desired result from above:
var weight qcut weighted_qcut
0 1 10 0 0
1 2 14 0 0
2 3 18 1 0
3 4 15 1 1
4 5 30 2 1
5 6 12 2 2
6 7 20 3 2
7 8 25 3 3
8 9 29 4 3
9 10 45 4 4

Difficulty displaying histogram for every variable

I need to plot histograms for numeric variables, in order to determine if their distributions are skewed. Below is the function definition, and the function being called.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sys
def variable_type(df, nominal_level = 3):
categorical, numeric, nominal = [],[],[]
for variable in df.columns.values:
if np.issubdtype(np.array(df[variable]).dtype, int) or np.issubdtype(np.array(df[variable]).dtype, float): #if srray variable is of type int or float
if len(np.unique(np.array(df[variable]))) <= nominal_level:
nominal.append(variable)
else:
numeric.append(variable)
else:
categorical.append(variable)
return numeric,categorical,nominal
def draw_histograms(df, variables, n_rows, n_cols):
fig = plt.figure()
import math
for i in range(min(n_rows * n_cols, len(variables))):
index = n_rows * 100 + n_cols * 10 + i + 1
ax = fig.add_subplot(index)
df[variables[i]].hist(bins = 20, ax = ax)
plt.title(variables[i]+' distribution')
#plt.xlabel(variables[i])
#plt.ylabel('Count')
plt.show()
def main():
df = read_data()
col_names = df.columns.tolist()
numeric,categorical,nominal = variable_type(df)
util.draw_histograms(df, numeric, 3, 3)
if __name__ == "__main__":
main()
My program only works when I use 3, 3 for n_rows and n_cols in the calling function, and this is a problem because it only plots 9 of the 20 variables. If I try any other numbers, I get a ValueError: num must be 1 <= num <= 18, not 0 or some other range depending on my chosen n_rows and n_cols. What can I do to plot all 20 numeric variables as subplots on one figure? or should I break it into different figures? This is a sample of my data frame.
TARGET_B ID GiftCnt36 GiftCntAll GiftCntCard36 GiftCntCardAll \
0 0 14974 2 4 1 3
1 0 6294 1 8 0 3
2 1 46110 6 41 3 20
3 1 185937 3 12 3 8
4 0 29637 1 1 1 1
GiftAvgLast GiftAvg36 GiftAvgAll GiftAvgCard36 ... \
0 17 13.50 9.25 17.00 ...
1 20 20.00 15.88 NaN ...
2 6 5.17 3.73 5.00 ...
3 10 8.67 8.50 8.67 ...
4 20 20.00 20.00 20.00 ...
PromCntCardAll StatusCat96NK StatusCatStarAll DemCluster DemAge \
0 13 A 0 0 NaN
1 24 A 0 23 67
2 22 S 1 0 NaN
3 16 E 1 0 NaN
4 6 F 0 35 53
DemGender DemHomeOwner DemMedHomeValue DemPctVeterans DemMedIncome
0 F U $0 0 $0
1 F U $186,800 85 $0
2 M U $87,600 36 $38,750
3 M U $139,200 27 $38,942
4 M U $168,100 37 $71,509

There is a NaN in your 10th attribute. Can your code handle this?
An you plot the 10th attribute?

How to column stack arrays ignoring nan in Python?

I have data of the form in a text file.
Text file entry
#x y z
1 1 1
2 4
3 9
4 16
5 25
6 36
7 49
8 64 512
9 81 729
10 100 1000
11 121
12 144 1728
13 169
14 196
15 225
16 256 4096
17 289
18 324
19 361 6859
20 400
21 441 9261
22 484
23 529 12167
24 576
25 625
Some of the entries in the third column are empty. I am trying to create an array of x (column 1) and z (column 3) ignoring nan. Let the array be B. The contents of B should be:
1 1
8 512
9 729
10 1000
12 1728
16 4096
19 6859
21 9261
23 12167
I tried doing this using the code:
import numpy as np
A = np.genfromtxt('data.dat', comments='#', delimiter='\t')
B = []
for i in range(len(A)):
if ~ np.isnan(A[i, 2]):
B = np.append(B, np.column_stack((A[i, 0], A[i, 2])))
print B.shape
This does not work. It creates a column vector. How can this be done in Python?

Using pandas would make your life quite easier (note the regular expression to define delimiter):
from pandas import read_csv
data = read_csv('data.dat', delimiter='\s+').values
print(data[~np.isnan(data[:, 2])][:, [0, 2]])
Which results in:
array([[ 8.00000000e+00, 5.12000000e+02],
[ 9.00000000e+00, 7.29000000e+02],
[ 1.00000000e+01, 1.00000000e+03],
[ 1.20000000e+01, 1.72800000e+03],
[ 1.60000000e+01, 4.09600000e+03],
[ 1.90000000e+01, 6.85900000e+03],
[ 2.10000000e+01, 9.26100000e+03],
[ 2.30000000e+01, 1.21670000e+04]])

If you read your data.dat file and assign the content to a variable, say data:
You can iterate over the lines and split them and process only the ones that have 3 elements:
B=[]
for line in data.split('\n'):
if len(line.split()) == 3:
x,y,z = line.split()
B.append((x,z)) # or B.append(str(x)+'\t'+str(z)+'\n')
# or any othr format you need

Not always the functions provided by the libraries are easy to use, as you found out. The following program does it manually, and creates an array with the values from the datafile.
import numpy as np
def main():
B = np.empty([0, 2], dtype = int)
with open("data.dat") as inf:
for line in inf:
if line[0] == "#": continue
l = line.split()
if len(l) == 3:
l = [int(d) for d in l[1:]]
B = np.vstack((B, l))
print B.shape
print B
return 0
if __name__ == '__main__':
main()
Note that:
1) The append() function works on lists, not on arrays - at least not in the syntax you used. The easiest way to extend arrays is 'piling' rows, using vstack (or hstack for columns)
2) Specifying a delimiter in genfromtxt() can come to bite you. By default the delimiter is any white space, which is normally what you want.

From your input dataframe:
In [33]: df.head()
Out[33]:
x y z
0 1 1 1
1 2 4 NaN
2 3 9 NaN
3 4 16 NaN
4 5 25 NaN
.. you can get to the output dataframe B by doing this :
In [34]: df.dropna().head().drop('y', axis=1)
Out[34]:
x z
0 1 1
7 8 512
8 9 729
9 10 1000
11 12 1728