We Keep Coding

How to remove unwanted lines in azure(python)

/usr/local/lib/python3.8/dist-packages/attr/__init__.py 27 0 100%
/usr/local/lib/python3.8/dist-packages/attr/_cmp.py 55 45 18% 51-100, 108-114, 122-137, 144-147, 154
/usr/local/lib/python3.8/dist-packages/attr/_compat.py 96 48 50% 22-24, 28-107, 123, 132, 153-156, 175, 191-212, 234, 241-242
/usr/local/lib/python3.8/dist-packages/attr/_config.py 9 4 56% 19-22, 33
/usr/local/lib/python3.8/dist-packages/attr/_funcs.py 96 84 12% 54-116, 130-189, 225-289, 301, 323-341, 360-370, 409-422
/usr/local/lib/python3.8/dist-packages/attr/_make.py 977 346 65% 84, 87, 90, 115-116, 121, 274, 280, 285, 293, 296, 299, 351-352, 413, 431, 450, 457-481, 501-507, 529-532, 556, 581, 590-591, 602, 611, 623-634, 642, 649, 734-754, 763, 792-796, 807-810, 838-839, 847, 881, 914-915, 918, 929-939, 954, 962-971, 1011, 1064, 1069-1090, 1098-1099, 1105-1106, 1112-1113, 1130, 1134, 1145, 1156, 1163, 1170-1171, 1186, 1212-1216, 1501, 1509, 1514, 1523, 1552, 1571, 1576, 1583, 1596, 1610, 1620, 1641-1646, 1690-1698, 1722-1732, 1758-1762, 1788-1799, 1829, 1840-1843, 1849-1852, 1858-1861, 1867-1870, 1928, 1954-2015, 2047-2054, 2075-2082, 2093-2099, 2103, 2131, 2138, 2144-2147, 2149, 2200, 2213, 2224, 2235-2287, 2313, 2336, 2344, 2380, 2388-2396, 2407-2418, 2428, 2447, 2454-2469, 2488, 2544-2553, 2558-2560, 2564-2569, 2694, 2702, 2732-2734, 2748-2752, 2759, 2768, 2771-2776, 2925-2929, 2941-2946, 2981, 2987-2988, 3035-3079, 3095-3096, 3109-3117, 3135-3173
/usr/local/lib/python3.8/dist-packages/attr/_next_gen.py 37 24 35% 82-147, 175, 198, 214
/usr/local/lib/python3.8/dist-packages/attr/_version_info.py 37 17 54% 60-69, 72-77, 80-87
/usr/local/lib/python3.8/dist-packages/attr/converters.py 58 47 19% 40-62, 83-114, 143-155
/usr/local/lib/python3.8/dist-packages/attr/exceptions.py 18 4 78% 89-91, 94
/usr/local/lib/python3.8/dist-packages/attr/filters.py 16 9 44% 17, 32-37, 49-54
/usr/local/lib/python3.8/dist-packages/attr/setters.py 28 16 43% 21-26, 37, 46-55, 65-69
/usr/local/lib/python3.8/dist-packages/yaml/resolver.py 135 97 28% 22-23, 30, 33, 51-89, 92-112, 115-118, 122-141, 144-165
/usr/local/lib/python3.8/dist-packages/yaml/scanner.py 753 672 11% 39-44, 60-109, 115-123, 128-133, 137-141, 146-154, 159-258, 272-277, 286-293, 301-310, 314-321, 340-347, 351-355, 364-367, 374-388, 393-400, 403, 406, 411-422, 425, 428, 433-445, 448, 451, 456-468, 473-482, 487-515, 520-543, 548-599, 604-610, 615-621, 626-632, 635, 638, 643-649, 652, 655, 660-666, 671-679, 687-688, 693-696, 701-704, 709, 714-719, 724-729, 745-746, 772-785, 789-804, 808-825, 829-842, 846-855, 859-865, 869-874, 878-883, 887-897, 908-933, 937-974, 979-1049, 1054-1090, 1094-1104, 1108-1119, 1123-1132, 1141-1155, 1187-1226, 1230-1250, 1254-1268, 1276-1309, 1315-1346, 1352-1370, 1375-1395, 1399-1414, 1425-1435
/usr/local/lib/python3.8/dist-packages/yaml/serializer.py 85 70 18% 17-25, 28-34, 37-41, 47-58, 61-72, 75-76, 79-110
/usr/local/lib/python3.8/dist-packages/yaml/tokens.py
these lines are checking for other repos,
So how to remove all these unwanted pipelines in azure, while running the pipeline
Please provide the solution

Can a Dataframe of NBA players be sorted by various conditions: to combine the rows of players w/ multiple entries bc they played on many teams?

I want to remove any players who didn't have over 1000 MP(minutes played).
I could easily write:
league_stats= pd.read_csv("1996.csv")
league_stats = league_stats.drop("Player-additional", axis=1)
league_stats_1000 = league_stats[league_stats['MP'] > 1000]
However, because players sometimes play for multiple teams in a year...this code doesn't account for that.
For example, Sam Cassell has four entries and none are above 1000 MP, but in total his MP for the season was over 1000. By running the above code I remove him from the new dataframe.
I am wondering if there is a way to sort the Dataframe by matching Rank(the RK column gives players who played on different teams the same rank number for each team they played on) and then sort it by... if the total of their MP is 1000=<.
This is the page I got the data from: 1996-1997 season.
Above the data table and to the left of the blue check box there is a dropdown menu called "Share and Export". From there I clicked on "Get table as CSV (for Excel)". After that I saved the CSV to a text editor and change the file extension to .csv to upload it to Jupyter Notebook.
This is a solution I came up with:
url = 'https://www.basketball-reference.com/leagues/NBA_1997_totals.html'
df = pd.read_html(url)[0]
tot_df = df.loc[df['Tm'] == 'TOT']
mp_1000 = tot_df.loc[tot_df["MP"] < 1000]
# Create list of indexes with unnecessary entries to be removed. We have TOT and don't need these rows.
# *** For the record, I came up with this list by manually going through the data.
indexes_to_remove = [5,6,24, 25, 66, 67, 248, 249, 447, 448, 449, 275, 276, 277, 19, 20, 21, 377, 378, 477, 478, 479,
54, 55, 451, 452, 337, 338, 156, 157, 73, 74, 546, 547, 435, 436, 437, 142, 143, 421, 42, 43, 232,
233, 571, 572, 363, 364, 531, 532, 201, 202, 111, 112, 139, 140, 307, 308, 557, 558, 93, 94, 512,
513, 206, 207, 208, 250, 259, 286, 287, 367, 368, 271, 272, 102, 103, 34, 35, 457, 458, 190, 191,
372, 373, 165, 166
]
df_drop_tot = df.drop(labels=indexes_to_remove, axis=0)
df_drop_tot

First off, no need to manually download the csv and then read it into pandas. You can load in the table using pandas' .read_html().
And yes, you can simply get the list of ranks, player names, or whatever, that have greater than 1000 MP, then use that list to filter the dataframe.
import pandas as pd
url = 'https://www.basketball-reference.com/leagues/NBA_1997_totals.html'
df = pd.read_html(url)[0]
df = df[df['Rk'].ne('Rk')]
df['MP'] = df['MP'].astype(int)
players_1000_rk_list = list(df[df['MP'] >= 1000]['Rk']) #<- coverts the "Rk" column into a list. I can then use that in the next line to only keep the "Rk" values that are in the list of "Rk"s that are >= 1000 MPs
players_df = df[df['Rk'].isin(players_1000_rk_list)]
Output: filters down from 574 rows to 282 rows
print(players_df)
Rk Player Pos Age Tm G ... AST STL BLK TOV PF PTS
0 1 Mahmoud Abdul-Rauf PG 27 SAC 75 ... 189 56 6 119 174 1031
1 2 Shareef Abdur-Rahim PF 20 VAN 80 ... 175 79 79 225 199 1494
3 4 Cory Alexander PG 23 SAS 80 ... 254 82 16 146 148 577
7 6 Ray Allen* SG 21 MIL 82 ... 210 75 10 149 218 1102
10 9 Greg Anderson C 32 SAS 82 ... 34 63 67 73 225 322
.. ... ... .. .. ... .. ... ... ... .. ... ... ...
581 430 Walt Williams SF 26 TOR 73 ... 197 97 62 174 282 1199
582 431 Corliss Williamson SF 23 SAC 79 ... 124 60 49 157 263 915
583 432 Kevin Willis PF 34 HOU 75 ... 71 42 32 119 216 842
589 438 Lorenzen Wright C 21 LAC 77 ... 49 48 60 79 211 561
590 439 Sharone Wright C 24 TOR 60 ... 28 15 50 93 146 390
[282 rows x 30 columns]

How to make dataframe from list of list

I have searched but till not able to figure out how to make data frame from below:
0 ([179, 142, 176, 177, 176, 180, 180, 180, 180,...
1 ([353, 314, 349, 349, 344, 359, 359, 359, 359,...
2 ([535, 504, 535, 535, 535, 540, 540, 540, 540,...
3 ([711, 664, 703, 703, 703, 721, 721, 721, 721,...
4 ([850, 810, 822, 822, 842, 857, 857, 857, 857,.
below is how single data looks
([179, 142, 176],
['Qtr- Oct-20','Qtr- Oct-20','Qtr- Oct-20',],
['High','Low','Close'],
[43.8, 26.05,33.1])
what i want is
0 1 2 3
0 179 Qtr- Oct-20 High 43.8
1 142 Qtr- Oct-20 Low 26.05
2 176 Qtr- Oct-20 High_Volume 1123132
3 177 Qtr- Oct-20 High_Delivery 42499
what i am getting
0
0 ([179, 142, 176, 177, 176, 180, 180, 180, 180,...
1 ([353, 314, 349, 349, 344, 359, 359, 359, 359,...
2 ([535, 504, 535, 535, 535, 540, 540, 540, 540,...

Let's do apply + pd.Series.explode:
pd.DataFrame(df['col'].tolist()).apply(pd.Series.explode).reset_index(drop=True)
0 1 2 3
0 179 Qtr- Oct-20 High 43.8
1 142 Qtr- Oct-20 Low 26.05
2 176 Qtr- Oct-20 Close 33.1
Note: df['col'] is the column in the dataframe which contains list of lists.

How to interpolate a 2D curve in Python

I have a set of x & y coordinate which is a curve / shape, I want the smooth the curve / sharp and plot a graph.
I tried different interpolation to smooth the curve / shape, But it still cannot fit my expectation. Using point to draw a smooth curve / shape.
Like the following, using x, y point to get a smooth circle / curve
However, I get something like
circle.jpg
curve.jpg
square.jpg
I also get trouble on spline interpolation, and rbf interpolation.
for cubic_spline_interpolation, I got
ValueError: Error on input data
for univariate_spline_interpolated, I got
ValueError: x must be strictly increasing
for rbf, I got
numpy.linalg.linalg.LinAlgError: Matrix is singular.
I have on idea to fix them and get correct sharp and curve. Many thanks for help.
Edit
For those cannot download the source code and x, y coordinate file, I post the code and x, y coordinate in question.
The following is my code:
#!/usr/bin/env python3
from std_lib import *
import os
import numpy as np
import cv2
from scipy import interpolate
import matplotlib.pyplot as plt
CUR_DIR = os.getcwd()
CIRCLE_FILE = "circle.txt"
CURVE_FILE = "curve.txt"
SQUARE_FILE = "square.txt"
#test
CIRCLE_NAME = "circle"
CURVE_NAME = "curve"
SQUARE_NAME = "square"
SYS_TOKEN_CNT = 2 # x, y
total_pt_cnt = 0 # total no. of points
x_arr = np.array([]) # x position set
y_arr = np.array([]) # y position set
def convert_coord_to_array(file_path):
global total_pt_cnt
global x_arr
global y_arr
if file_path == "":
return FALSE
with open(file_path) as f:
content = f.readlines()
content = [x.strip() for x in content]
total_pt_cnt = len(content)
if (total_pt_cnt <= 0):
return FALSE
##
x_arr = np.empty((0, total_pt_cnt))
y_arr = np.empty((0, total_pt_cnt))
#compare the first and last x
# if ((content[0][0]) > (content[-1])):
# is_reverse = TRUE
for x in content:
token_cnt = get_token_cnt(x, ',')
if (token_cnt != SYS_TOKEN_CNT):
return FALSE
for idx in range(token_cnt):
token_string = get_token_string(x, ',', idx)
token_string = token_string.strip()
if (not token_string.isdigit()):
return FALSE
# save x, y set
if (idx == 0):
x_arr = np.append(x_arr, int(token_string))
else:
y_arr = np.append(y_arr, int(token_string))
return TRUE
def linear_interpolation(fig, axs):
xnew = np.linspace(x_arr.min(), x_arr.max(), len(x_arr))
f = interpolate.interp1d(xnew , y_arr)
axs.plot(xnew, f(xnew))
axs.set_title('linear')
def cubic_interpolation(fig, axs):
xnew = np.linspace(x_arr.min(), x_arr.max(), len(x_arr))
f = interpolate.interp1d(xnew , y_arr, kind='cubic')
axs.plot(xnew, f(xnew))
axs.set_title('cubic')
def cubic_spline_interpolation(fig, axs):
xnew = np.linspace(x_arr.min(), x_arr.max(), len(x_arr))
tck = interpolate.splrep(x_arr, y_arr, s=0) #always fail (ValueError: Error on input data)
ynew = interpolate.splev(xnew, tck, der=0)
axs.plot(xnew, ynew)
axs.set_title('cubic spline')
def parametric_spline_interpolation(fig, axs):
xnew = np.linspace(x_arr.min(), x_arr.max(), len(x_arr))
tck, u = interpolate.splprep([x_arr, y_arr], s=0)
out = interpolate.splev(xnew, tck)
axs.plot(out[0], out[1])
axs.set_title('parametric spline')
def univariate_spline_interpolated(fig, axs):
s = interpolate.InterpolatedUnivariateSpline(x_arr, y_arr)# ValueError: x must be strictly increasing
xnew = np.linspace(x_arr.min(), x_arr.max(), len(x_arr))
ynew = s(xnew)
axs.plot(xnew, ynew)
axs.set_title('univariate spline')
def rbf(fig, axs):
xnew = np.linspace(x_arr.min(), x_arr.max(), len(x_arr))
rbf = interpolate.Rbf(x_arr, y_arr) # numpy.linalg.linalg.LinAlgError: Matrix is singular.
fi = rbf(xnew)
axs.plot(xnew, fi)
axs.set_title('rbf')
def interpolation():
fig, axs = plt.subplots(nrows=4)
axs[0].plot(x_arr, y_arr, 'r-')
axs[0].set_title('org')
cubic_interpolation(fig, axs[1])
# cubic_spline_interpolation(fig, axs[2])
parametric_spline_interpolation(fig, axs[2])
# univariate_spline_interpolated(fig, axs[3])
# rbf(fig, axs[3])
linear_interpolation(fig, axs[3])
plt.show()
#------- main -------
if __name__ == "__main__":
# np.seterr(divide='ignore', invalid='ignore')
file_name = CUR_DIR + "/" + CIRCLE_FILE
convert_coord_to_array(file_name)
#file_name = CUR_DIR + "/" + CURVE_FILE
#convert_coord_to_array(file_name)
#file_name = CUR_DIR + "/" + SQUARE_FILE
#convert_coord_to_array(file_name)
#
interpolation()
circle x, y coordinate
307, 91
308, 90
339, 90
340, 91
348, 91
349, 92
351, 92
352, 93
357, 93
358, 94
361, 94
362, 95
364, 95
365, 96
369, 96
370, 97
374, 97
375, 98
376, 98
377, 99
379, 99
380, 100
382, 100
383, 101
386, 101
387, 102
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390, 103
392, 103
393, 104
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395, 105
398, 105
399, 106
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401, 107
402, 107
403, 108
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410, 110
411, 111
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415, 112
416, 113
417, 113
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420, 115
421, 115
422, 116
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425, 118
426, 118
428, 120
429, 120
430, 121
430, 122
431, 122
433, 124
434, 124
435, 125
435, 126
437, 128
437, 129
441, 133
441, 134
442, 135
442, 137
443, 137
444, 138
444, 140
445, 141
445, 142
446, 143
446, 146
447, 147
447, 148
448, 149
448, 153
449, 154
449, 191
448, 192
448, 223
447, 224
447, 240
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422, 305
420, 307
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387, 342
387, 343
386, 344
384, 344
383, 345
382, 345
380, 347
379, 347
377, 349
376, 349
374, 351
373, 351
373, 352
372, 353
370, 353
369, 354
368, 354
367, 355
366, 355
365, 356
364, 356
363, 357
362, 357
359, 360
358, 360
357, 361
356, 361
355, 362
353, 362
353, 363
352, 364
348, 364
347, 365
314, 365
313, 364
297, 364
296, 363
284, 363
283, 362
280, 362
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273, 361
272, 360
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265, 359
264, 358
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260, 357
258, 355
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252, 111
253, 110
256, 110
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286, 98
287, 97
288, 97
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291, 95
293, 95
295, 93
298, 93
299, 92
302, 92
303, 91
Solved
def linear_interpolateion(self, x, y):
points = np.array([x, y]).T # a (nbre_points x nbre_dim) array
# Linear length along the line:
distance = np.cumsum( np.sqrt(np.sum( np.diff(points, axis=0)**2, axis=1 )) )
distance = np.insert(distance, 0, 0)
alpha = np.linspace(distance.min(), int(distance.max()), len(x))
interpolator = interpolate.interp1d(distance, points, kind='slinear', axis=0)
interpolated_points = interpolator(alpha)
out_x = interpolated_points.T[0]
out_y = interpolated_points.T[1]
return out_x, out_y

Because the interpolation is wanted for generic 2d curve i.e. (x, y)=f(s) where s is the coordinates along the curve, rather than y = f(x), the distance along the line s have to be computed first. Then, the interpolation for each coordinates is performed relatively to s. (for instance, in the circle case y = f(x) have two solutions)
s (or distance in the code here) is calculated as the cumulative sum of the length of each segments between the given points.
import numpy as np
from scipy.interpolate import interp1d
import matplotlib.pyplot as plt
# Define some points:
points = np.array([[0, 1, 8, 2, 2],
[1, 0, 6, 7, 2]]).T # a (nbre_points x nbre_dim) array
# Linear length along the line:
distance = np.cumsum( np.sqrt(np.sum( np.diff(points, axis=0)**2, axis=1 )) )
distance = np.insert(distance, 0, 0)/distance[-1]
# Interpolation for different methods:
interpolations_methods = ['slinear', 'quadratic', 'cubic']
alpha = np.linspace(0, 1, 75)
interpolated_points = {}
for method in interpolations_methods:
interpolator = interp1d(distance, points, kind=method, axis=0)
interpolated_points[method] = interpolator(alpha)
# Graph:
plt.figure(figsize=(7,7))
for method_name, curve in interpolated_points.items():
plt.plot(*curve.T, '-', label=method_name);
plt.plot(*points.T, 'ok', label='original points');
plt.axis('equal'); plt.legend(); plt.xlabel('x'); plt.ylabel('y');
which gives:
Regarding the graphs, it seems you are looking for a smoothing method rather than an interpolation of the points. Here, is a similar approach use to fit a spline separately on each coordinates of the given curve (see Scipy UnivariateSpline):
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import UnivariateSpline
# Define some points:
theta = np.linspace(-3, 2, 40)
points = np.vstack( (np.cos(theta), np.sin(theta)) ).T
# add some noise:
points = points + 0.05*np.random.randn(*points.shape)
# Linear length along the line:
distance = np.cumsum( np.sqrt(np.sum( np.diff(points, axis=0)**2, axis=1 )) )
distance = np.insert(distance, 0, 0)/distance[-1]
# Build a list of the spline function, one for each dimension:
splines = [UnivariateSpline(distance, coords, k=3, s=.2) for coords in points.T]
# Computed the spline for the asked distances:
alpha = np.linspace(0, 1, 75)
points_fitted = np.vstack( spl(alpha) for spl in splines ).T
# Graph:
plt.plot(*points.T, 'ok', label='original points');
plt.plot(*points_fitted.T, '-r', label='fitted spline k=3, s=.2');
plt.axis('equal'); plt.legend(); plt.xlabel('x'); plt.ylabel('y');
which gives:

Making pair by 2 rows and slicing from each row

I have a dataframe like:
x1 y1 x2 y2
0 149 2653 2152 2656
1 149 2465 2152 2468
2 149 1403 2152 1406
3 149 1215 2152 1218
4 170 2692 2170 2695
5 170 2475 2170 2478
6 170 1413 2170 1416
7 170 1285 2170 1288
I need to pair by each two rows from data frame index. i.e., [0,1], [2,3], [4,5], [6,7] etc.,
and extract x1,y1 from first row of the pair x2,y2 from second row of the pair, similarly for each pair of rows.
Sample Output:
[[149,2653,2152,2468],[149,1403,2152,1218],[170,2692,2170,2478],[170,1413,2170,1288]]
Please feel free to ask if it's not clear.
So far I tried grouping by pairs, and tried shift operation.
But I didn't manage to make make pair records.

Python solution:
Select values of columns by positions to lists:
a = df[['x2', 'y2']].iloc[1::2].values.tolist()
b = df[['x1', 'y1']].iloc[0::2].values.tolist()
And then zip and join together in list comprehension:
L = [y + x for x, y in zip(a, b)]
print (L)
[[149, 2653, 2152, 2468], [149, 1403, 2152, 1218],
[170, 2692, 2170, 2478], [170, 1413, 2170, 1288]]
Thank you, #user2285236 for another solution:
L = np.concatenate([df.loc[::2, ['x1', 'y1']], df.loc[1::2, ['x2', 'y2']]], axis=1).tolist()
Pure pandas solution:
First DataFrameGroupBy.shift by each 2 rows:
df[['x2', 'y2']] = df.groupby(np.arange(len(df)) // 2)[['x2', 'y2']].shift(-1)
print (df)
x1 y1 x2 y2
0 149 2653 2152.0 2468.0
1 149 2465 NaN NaN
2 149 1403 2152.0 1218.0
3 149 1215 NaN NaN
4 170 2692 2170.0 2478.0
5 170 2475 NaN NaN
6 170 1413 2170.0 1288.0
7 170 1285 NaN NaN
Then remove NaNs rows, convert to int and then to list:
print (df.dropna().astype(int).values.tolist())
[[149, 2653, 2152, 2468], [149, 1403, 2152, 1218],
[170, 2692, 2170, 2478], [170, 1413, 2170, 1288]]

Here's one solution via numpy.hstack. Note it is natural to feed numpy arrays directly to pd.DataFrame, since this is how Pandas stores data internally.
import numpy as np
arr = np.hstack((df[['x1', 'y1']].values[::2],
df[['x2', 'y2']].values[1::2]))
res = pd.DataFrame(arr)
print(res)
0 1 2 3
0 149 2653 2152 2468
1 149 1403 2152 1218
2 170 2692 2170 2478
3 170 1413 2170 1288

Here's a solution using a custom iterator based on iterrows(), but it's a bit clunky:
import pandas as pd
df = pd.DataFrame( columns=['x1','y1','x2','y2'], data=
[[149, 2653, 2152, 2656], [149, 2465, 2152, 2468], [149, 1403, 2152, 1406], [149, 1215, 2152, 1218],
[170, 2692, 2170, 2695], [170, 2475, 2170, 2478], [170, 1413, 2170, 1416], [170, 1285, 2170, 1288]] )
def iter_oddeven_pairs(df):
row_it = df.iterrows()
try:
while True:
_,row = next(row_it)
yield row[0:2]
_,row = next(row_it)
yield row[2:4]
except StopIteration:
pass
print(pd.concat([pair for pair in iter_oddeven_pairs(df)]))