Unnamed: 0 int64 0 378k | id int64 49.9k 73.8M | title stringlengths 15 150 | question stringlengths 37 64.2k | answer stringlengths 37 44.1k | tags stringlengths 5 106 | score int64 -10 5.87k |
|---|---|---|---|---|---|---|
1,300 | 68,811,534 | Several problems when packaging with pyinstaller | <p>I was packaging my python program with PyInstaller, and several problems occurred. Here's my code below:</p>
<pre><code>#!/usr/bin/python
# -*- coding: UTF-8 -*-
from spleeter import separator
import tkinter as TKT
from tkinter import ttk
from tkinter import messagebox
import tensorflow
window = TKT.Tk()
screen_w... | <p>You need to exclude the PyQt5 Library in the pyinstaller command.
Adding the command below, in the pyinstaller command.</p>
<pre><code>--exclude-module "PyQt5"
</code></pre>
<p>You can use the auto-py-to-exe application to create an exe. This application use the pyinstaller library. It's really easy to han... | python|python-3.x|tensorflow|pyinstaller | 1 |
1,301 | 68,671,394 | Add New Values to Dataframe according to predictions | <p>I have the following dataframe called "lastDays":</p>
<pre><code> Units
Date
2021-06-01 00:00:00 3
2021-06-01 01:00:00 4
2021-06-01 02:00:00 1
2021-06-01 03:00:00 2
2021-06-01 04:00:00 8
2021-06-01 05:00:00 9
2021-06-01 06:00:00 3
2021-06-01 07:00:00 5
2021-0... | <p>I was able to find the solution that was the following:</p>
<p>Apply <code>pd.date-range</code> to create a new column of dates in the created dataframe "nextMonth" and reflect the forecasts. The applied function <code>nextMonth['Date'] = pd.date_range (start = lastDays.index [-1], period = len (lastDays),... | python|pandas|dataframe|datetime | 0 |
1,302 | 68,494,337 | Split rows of pandas textual dataframe | <p>I have a pandas textual dataframe which looks like this:</p>
<pre><code>+-----------------------------------+
| text |
+-----------------------------------+
| A very long sentence |
+-----------------------------------+
| Another very long sentence |
+----------------... | <p>you can try via <code>str.split()</code>+<code>str.join()</code> if the sentence are seperated by <code>' '</code> :</p>
<pre><code>df['text']=df['text'].str.split().str[:512].str.join(' ')
</code></pre>
<p>Now For checking the length(after running the above code) you can do:</p>
<pre><code>df['text_len']=df['text']... | python|pandas|dataframe|text | 0 |
1,303 | 68,641,777 | Excel file only opens with pandas in Python after being resaved | <p>I have some Excel files with measurements taken from National Instruments' LabView. I'm trying to use Pandas to be able to edit the data but when using read_excel on those Excel files I get the error <code>TypeError: expected <class 'openpyxl.styles.fills.Fill'></code>.</p>
<p>The strange part is that if I ope... | <p>Seems like the source file is corrupt to the point that a standard method of opening the file is not possible (e.g., <code>pd.read_excel()</code> or <code>pd.ExcelFile()</code>. If there are too many files to open manually and save...Try a non-standard way of opening the file.</p>
<p>One idea is using the code from... | python|excel|pandas | 1 |
1,304 | 68,449,166 | pandas rolling apply return np.nan | <p>I would like to apply a custom skewness function to rolling apply, but got np.nan instead.</p>
<pre><code>import pandas as pd
import numpy as np
def _get_skewness(col, q=(0.05, 0.95)):
if q[0] > 0:
quantiles = col.quantile(q)
col.loc[(col<quantiles[q[0]]) | (col > quantiles[q[1]])] = np... | <p>By using <code>loc</code> on <code>col</code> the actual DataFrame is being modified in each iteration. The introduction of <code>NaN</code> in the column eventually means the window becomes all <code>NaN</code>. The easiest fix (without understanding more about how the skewness is to be applied) would be to create ... | python|pandas | 1 |
1,305 | 68,649,384 | Pandas Dataframe remove rows depending on two columns with equal values | <p>basically i have a dataframe where is a lot of columns, but the main are ITEM_ID and PRICE.</p>
<p>For example:</p>
<pre><code>ID ITEM_ID ITEM PRICE
1 1 potato 20
2 1 potato 20
3 1 potato 25
4 2 tomato 50
5 2 tomato 55
</code></pre>
<p>And I wa... | <p>Solution to this problem is:</p>
<pre><code>df.drop_duplicates(subset=['item_id', 'price'], inplace=True)
</code></pre> | python|pandas|dataframe|duplicates | 2 |
1,306 | 36,458,573 | Torch/Lua equivalent function to MATLAB or Numpy 'Unique' | <p>In Python one can do the following to get the unique values in a vector/matrix/tensor:</p>
<pre><code>import numpy as np
a = np.unique([1, 1, 2, 2, 3, 3])
# Now a = array([1, 2, 3])
</code></pre>
<p>There is a similar function in MATLAB as well:</p>
<pre><code>A = [9 2 9 5];
C = unique(A)
%Now C = [9, 2, 9]
</co... | <p>Nope, there is no such a standard function in stock Lua and/or Torch.</p>
<p>Considering using some implementation of <code>set</code> data structure, rolling Your own implementation of <code>unique()</code> or redesigning Your application not to require this kind of functionality.</p>
<p>Example 11-liner:</p>
<p... | python|matlab|numpy|lua|torch | 1 |
1,307 | 53,216,162 | How to train Tensorflow Object Detection images that do not contain objects? | <p>I am training an object detection network using Tensorflow's object detection,</p>
<p><a href="https://github.com/tensorflow/models/tree/master/research/object_detection" rel="noreferrer">https://github.com/tensorflow/models/tree/master/research/object_detection</a></p>
<p>I can successfully train a network based ... | <p>An Object Detection CNN can learn what is not an object, simply by letting it see examples of images without any labels.</p>
<p>There are two main architecture types: </p>
<ol>
<li>two-stages, with first stage object/region proposal (RPN), and second - classification and bounding box fine-tuning; </li>
<li>one-sta... | python|tensorflow|deep-learning|object-detection|object-detection-api | 8 |
1,308 | 52,939,153 | Creating new dataframe based on maximum element | <p>I have two dataframes-</p>
<pre><code>cols = ['A','B']
data = [[-1,2],[0,2],[5,1]]
data = np.asarray(data)
indices = np.arange(0,len(data))
df = pd.DataFrame(data, index=indices, columns=cols)
cols = ['A','B']
data2 = [[-13,2],[-1,2],[0,4],[2,1],[5,0]]
data2 = np.asarray(data2)
indices = np.arange(0,len(data2))
... | <p>Using <code>drop_duplicates</code></p>
<pre><code>pd.concat([df2,df]).sort_values('B').drop_duplicates('A',keep='last')
Out[80]:
A B
3 2 1
2 5 1
0 -13 2
0 -1 2
2 0 4
</code></pre> | python|pandas | 4 |
1,309 | 65,495,533 | Getting an error when trying to get pandas to read my json file | <p>I'm getting <code>ValueError: Expected object or value</code> when trying to get pandas to read my json file.</p>
<p>Here is the code I'm using:</p>
<pre class="lang-py prettyprint-override"><code>import pandas as pd
import json
dataframe = pd.read_json(r'C:\Users\stans\WFH Project\data.json')
</code></pre>
<p>This ... | <pre><code>import json
import panda as pd
f = open('C:/Users/stans/WFH Project/data.json',)
data = json.load(f)
df = pd.DataFrame(data)
f.close()
</code></pre> | python|json|pandas | 0 |
1,310 | 65,816,766 | How do I save a file in f4 format using numpy in python | <p>I have generated a coupling file and save it like this, <br></p>
<p>np.save('J', Jindep,)</p>
<p>This saves in J.npy format. How do I convert it in 'f4'format?</p> | <p>You can use open function to create files.</p>
<pre><code>numpy_array = []
f = open('j.f4','w')
f.write(numpy_array)
f.close()
</code></pre> | python|numpy | 0 |
1,311 | 65,797,608 | Most efficient way to iterate over large vector? | <p>I have an input ndarray, <code>pointsCount</code>, with shape (4000000, 1). I have another ndarray, <code>clusters</code>, with shape (2,1). I then want to perform the following:</p>
<pre><code>distances = np.zeros((pointsCount, n_clusters))
for x in range(len(trainPoints)):
for c in range(len(clusters)):
... | <p>All you need to do is transpose <code>clusters</code>. For example, given initial arrays:</p>
<pre><code>>>> pointsCount # I have considered 4 instead of 4 mil
array([[2],
[4],
[7],
[6]])
>>> clusters
array([[2],
[3]])
# Your code:
>>> np.array([(x-cluster).T... | python|numpy | 1 |
1,312 | 63,585,285 | Why is history storing auc and val_auc with incrementing integers (auc_2, auc_4, ...)? | <p>I am beginner with keras and today I bumped into this sort of issue I don't know how to handle. The values for <code>auc</code> and <code>val_auc</code> are being stored in <code>history</code> with the first even integers, like <code>auc</code>, <code>auc_2</code>, <code>auc_4</code>, <code>auc_6</code>... and so o... | <p>Use tf.keras.backend.clear_session()</p>
<p><a href="https://www.tensorflow.org/api_docs/python/tf/keras/backend/clear_session" rel="nofollow noreferrer">https://www.tensorflow.org/api_docs/python/tf/keras/backend/clear_session</a></p> | python|tensorflow|keras|deep-learning | 1 |
1,313 | 63,398,403 | Replace date "/" tobe "-" in existing column .csv pandas | <p>I have data in wind.csv format :</p>
<pre><code>Date,Time,Wind
13/08/2020,12.00z, 13020knot
14/08/2020,12.00z, 14004knot
15/08/2020,12.00z, 10005knot
</code></pre>
<p>I want to replace the sign "/" to "-", the Date Data.</p>
<pre><code>import pandas as pd
import numpy as np
df = pd.read_csv(&quo... | <p>Here is my solution. It gets the result that you want but I don't know if it fit your desire.</p>
<pre><code>import pandas as pd
df = pd.read_csv("Date.csv")
df["Date"] = df["Date"].str.replace("/","-")
</code></pre>
<p>This one work but take long line of code</p>
<p... | python|pandas | 0 |
1,314 | 63,369,555 | Google Cloud Function Build timeout - all requirements have been loaded | <p>I have the following code on my cloud function -</p>
<pre><code>import os
import numpy as np
import requests
import torch
from torch import nn
from torch.nn import functional as F
import math
from torch.nn import BCEWithLogitsLoss
from torch.utils.data import TensorDataset
from transformers import AdamW, XLNetToke... | <p>It might be due to the <code>torch</code> import, as this causes you to import the PyTorch lib that contains CUDA and hence requires a GPU, which isn't available on Cloud Functions.</p>
<p>Instead, you can use a direct link to the cpu only version in your requirements.txt, like this:</p>
<pre><code>certifi==2020.6.2... | python|google-cloud-functions|pytorch|cloud|requirements.txt | 1 |
1,315 | 24,884,399 | How to perform a simple signal backtest in python pandas | <p>I want to perform a simple and quick backtest in pandas by providing buy signals as DatetimeIndex to check against ohlc quotes DataFrame (adjusted close price) and am not sure if I am doing this right.</p>
<p>To be clear I want to calculate the cummulated returns of all swapping buy signals (and stock returns as we... | <p>You don't have enough information to run a backtest. Your "strategy" currently just has True or False. When it's True, how much do you want to buy? If it's True twice in a row, does that mean buy-and-hold or buy at both times? Does False mean liquidate or not to buy?</p>
<p>You need to:</p>
<ol>
<li>Translate your... | python|numpy|pandas|finance|quantitative-finance | 2 |
1,316 | 53,459,046 | Pandas dataframe change all value if greater than 0 | <p>I have a dataframe</p>
<pre><code>df=
A B C
1 2 55
0 44 0
0 0 0
</code></pre>
<p>and I want to change values to 1 if the value is >0.</p>
<p>Is this the right approach:
df.loc[df>0,]=1</p>
<pre><code>to give:
A B C
1 1 1
0 1 0
0 0 0
</code></pre> | <p>Use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.clip_upper.html" rel="nofollow noreferrer"><code>clip_upper</code></a>:</p>
<pre><code>df = df.clip_upper(1)
print (df)
A B C
0 1 1 1
1 0 1 0
2 0 0 0
</code></pre>
<p>Numpy alternative:</p>
<pre><code>df = pd.DataFra... | pandas|dataframe | 6 |
1,317 | 17,387,219 | Sorting numpy matrix for a given column | <p>I've tried to use Ned Batchelder code to sort in human order a <code>NumPy</code> matrix, as it was proposed in this following post:</p>
<p><a href="https://stackoverflow.com/q/7638738">Sort numpy string array with negative numbers?</a></p>
<p>The code runs on a one-dimensional array, the command being:</p>
<pre>... | <p>If you have a <code>np.matrix</code>, called <code>m</code>:</p>
<pre><code>col = 1
m[np.array(m[:,col].argsort(axis=0).tolist()).ravel()]
</code></pre>
<p>If you have a <code>np.ndarray</code>, called <code>a</code>:</p>
<pre><code>col = 1
a[a[:,col].argsort(axis=0)]
</code></pre>
<p>If you have a structured ar... | python|numpy|sorting|matrix | 5 |
1,318 | 17,451,425 | Hist in matplotlib: Bins are not centered and proportions not correct on the axis | <p>take a look at this example:</p>
<pre><code> import matplotlib.pyplot as plt
l = [3,3,3,2,1,4,4,5,5,5,5,5,5,5,5,5]
plt.hist(l,normed=True)
plt.show()
</code></pre>
<p>The output is posted as a picture. I have two questions:</p>
<p>a) Why are only the 4 and 5 bins centered around its value? Shouldn't the others... | <p>You should adjust the keyword arguments of the <code>plt.hist</code> function. There are many of them and the <a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.hist" rel="noreferrer">documentation</a> can help you answer many of these questions.</p>
<p>a. ) You can pass the keywords <code>bins=ra... | python|numpy|matplotlib | 16 |
1,319 | 71,988,044 | Financial performance and risk analysis statistics from sample DataFrame | <p>How do I output detailed financial performance and risk analysis statistics from this sample pandas DataFrame?</p>
<p>Can anyone show how this could be done with Quantstats, Pyfolio or another similar approach?</p>
<p><strong>Code</strong></p>
<pre><code>start_amount = 100000
np.random.seed(8)
win_loss_df = pd.Data... | <p>We will use the profit column and use <a href="https://github.com/ranaroussi/quantstats" rel="nofollow noreferrer">quantstats</a> to generate reports.</p>
<h4>Code</h4>
<pre><code>import quantstats as qs
import numpy as np
import pandas as pd
start_amount = 100000
np.random.seed(8)
win_loss_df = pd.DataFrame(
... | python|pandas|finance | 1 |
1,320 | 71,950,486 | I have unlabelled data and I want to make it into labelled into three category 1.Refilling 2. theft 3. sloshing | <pre><code>timestamp vehicle_speed fuel_in_lit
2022-01-01 00:00:03 0 61
2022-01-01 00:00:23 2 60
2022-01-01 00:00:33 0 59
2022-01-01 00:00:43 0 58
2022-01-01 00:00:53 0 56... | <p>IIUC, you can use a set of masks and <a href="https://numpy.org/doc/stable/reference/generated/numpy.select.html" rel="nofollow noreferrer"><code>numpy.select</code></a>:</p>
<pre><code>diff = df['fuel_in_lit'].diff()
# speed is 0
m1 = df['vehicle_speed'].eq(0)
# fuel is increasing
m2 = diff.gt(0)
# fuel is decreasi... | python|pandas|dataframe|numpy | -1 |
1,321 | 72,031,917 | How to compare two dataframes' structures | <p>I have two pandas dataframes and I want to compare their structures only.
I tried to do this:</p>
<pre><code>df0Info = df0.info()
df1Info = df1.info()
if df0Info == df1Info:
print("They are same")
else:
print("They are diff")
</code></pre>
<p>I found the result always is same whether the ... | <p><code>pandas.DataFrame.info</code> prints a summary of a DataFrame and returns <code>None</code>, so comparing outputs to one another will always be True because you're essentially testing <code>None == None</code>.</p> | python|pandas | 0 |
1,322 | 72,046,021 | Save multiple dataframes to the same file, one after the other | <p>Lets say I have three dfs</p>
<pre><code>x,y,z
0,1,1,1
1,2,2,2
2,3,3,3
a,b,c
0,4,4,4
1,5,5,5
2,6,6,6
d,e,f
0,7,7,7
1,8,8,8
2,9,9,9
</code></pre>
<p>How can I stick them all together so that i get:</p>
<pre><code>x,y,z
0,1,1,1
1,2,2,2
2,3,3,3
a,b,c
0,4,4,4
1,5,5,5
2,6,6,6
d,e,f
0,7,7,7
1,8,8,8
2,9,9,9
</code></pre>... | <p>If you want to save all your dataframes in the <strong>same file</strong> one after the other, use a simple loop with <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_csv.html" rel="nofollow noreferrer"><code>to_csv</code></a> and use the file append mode (<strong>a</strong>):</p>
<pre class... | python|pandas|dataframe | 2 |
1,323 | 19,071,199 | Drop columns whose name contains a specific string from pandas DataFrame | <p>I have a pandas dataframe with the following column names:</p>
<p>Result1, Test1, Result2, Test2, Result3, Test3, etc...</p>
<p>I want to drop all the columns whose name contains the word "Test". The numbers of such columns is not static but depends on a previous function.</p>
<p>How can I do that?</p> | <p>Here is one way to do this:</p>
<pre><code>df = df[df.columns.drop(list(df.filter(regex='Test')))]
</code></pre> | python|pandas|dataframe | 283 |
1,324 | 17,833,119 | Lowpass Filter in python | <p>I am trying to convert a Matlab code to Python. I want to implement <code>fdesign.lowpass()</code> of Matlab in Python. What will be the exact substitute of this Matlab code using <code>scipy.signal.firwin()</code>:</p>
<pre><code>demod_1_a = mod_noisy * 2.*cos(2*pi*Fc*t+phi);
d = fdesign.lowpass('N,Fc', 10, 40, 16... | <p>A very basic approach would be to invoke</p>
<pre><code># spell out the args that were passed to the Matlab function
N = 10
Fc = 40
Fs = 1600
# provide them to firwin
h = scipy.signal.firwin(numtaps=N, cutoff=40, nyq=Fs/2)
# 'x' is the time-series data you are filtering
y = scipy.signal.lfilter(h, 1.0, x)
</code></... | python|numpy|filter|scipy | 6 |
1,325 | 55,272,152 | Pandas - Calculate average of columns with condition based on values in other columns | <p>I struggle to create a new column in my data frame, which would be the result of going through each row a data frame and calculating the average based on some conditions.
That is how the data frame looks like</p>
<pre><code>ID, 1_a, 1_b, 1_c, 2_a, 2_b, 2_c, 3_a, 3_b, 3_c
0, 0, 145, 0.8, 0, 555, 0.7, 1, 335, 0.7
1... | <p>If your columns are in a similar range for both '_a' and '_c', you can simply loop through them;</p>
<pre><code>r = range(1,4)
for i in r:
df.loc[df["{}_a".format(i)] != 1, "{}_c".format(i)] = np.NaN
df['NEW'] = df[['{}_c'.format(i) for i in r]].mean(axis=1)
</code></pre> | python|pandas|if-statement|iteration | 1 |
1,326 | 55,159,061 | store values from a list into an array after each iteration consists of specified column of files in python | <p>For a file in files:</p>
<p>This is my list which consists of values from 3 files after each iteration. </p>
<pre><code>import pandas ... | <p>Your question is very general. Try to provide a minimal complete verifiable example otherwise it is difficult to help you.</p>
<p>What is in the files? Is each line a number? Too few information.</p>
<p>In Python you can use multiple iteration variables. </p>
<pre><code>#open the files first
f1 = open("file1", "r... | python|pandas|numpy | 0 |
1,327 | 55,193,810 | Binary Image classification using TensorFlow | <p>I am writing a code to classify between dogs and cats in python(Tensorflow) but the code is displaying this error:</p>
<p><strong>IndexError: index 0 is out of bounds for axis 0 with size 0</strong></p>
<p>I am stuck here. Any help is appreciated. </p>
<p>Also can you please help me I cant figure out here how One... | <blockquote>
<p>IndexError: index 0 is out of bounds for axis 0 with size 0</p>
</blockquote>
<p>It means you don't have the index you are trying to reference. </p>
<p>Since this is a binary classification problem, you don't required one_hot encoding for pre-processing labels. if you have more than two labels then ... | tensorflow|deep-learning|classification|conv-neural-network | 0 |
1,328 | 56,695,811 | Is it possible to calculate accuracy and ROC-AUC score at the same time with GridSearchCV? | <pre><code>rf = RandomForestClassifier(random_state=0)
parameters = {'bootstrap': [True, False], 'min_samples_split':[2,3,4],
'criterion':['entropy', 'gini'], 'n_estimators':[100, 200] }
grid_search = GridSearchCV(estimator=rf, param_grid=parameters,
scoring='accuracy', cv=10, n_jobs=-1)
</code></pre>
<p... | <p>Per the <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html" rel="nofollow noreferrer">docs</a>, you can pass a <code>list</code> of strings. In this specific case, <code>scoring=['accuracy', 'roc_auc']</code> would be what you want.</p> | python|pandas|numpy|cross-validation|grid-search | 0 |
1,329 | 56,850,868 | How do I add numbers to a column based on another column? (dictionary) | <p>I have a dictionary with values that I need to add to a column in a dataframe. The dictionary looks like this:</p>
<pre><code>{1:123, 2:345, 3:678}
</code></pre>
<p>and the column of the dataframe looks like this:</p>
<pre><code>col1
1
2
3
</code></pre>
<p>and I want this result:</p>
<pre><code>col1
1123... | <p>Use <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.astype.html" rel="nofollow noreferrer"><code>Series.astype</code></a> to cast as <code>str</code> and <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.map.html" rel="nofollow noreferrer"><code>Serie... | python|pandas|dataframe | 2 |
1,330 | 56,455,555 | Adding weights to edges in networkx automatically depending of the number of connections form pandas dataframe | <p>I am trying to create out of a pandas dataframe a directed graph right now with networkx, so far i can use:</p>
<pre><code>nx.from_pandas_edgelist(df, 'Activity', 'Activity followed', create_using=nx.DiGraph())
</code></pre>
<p>which shows me all the nodes and edges from Activity --> Activity followed.</p>
<p>In ... | <p>You could try adding the <code>weight</code> attribute as a column, using <a href="http://OutEdgeDataView([('Lunch',%20'Dinner',%20%7B'weight':%202%7D),%20('Breakfast',%20'Lunch',%20%7B'weight':%201%7D)])" rel="noreferrer"><code>groupby.transform</code></a>, then pass ... | python|pandas|networkx | 5 |
1,331 | 56,775,346 | Numpy 2d array, clipping each index of each row to the minimum of that index and a specific column | <p>Given a 2d array, I want to take a specific column of that array.</p>
<p>I then want to take every value of each row in the array, and change that value to whatever the minimum is between its current value, and the value in the specified column <em>for that row</em> is.</p>
<p>What is an efficient way to do this? ... | <p>Use <code>numpy.minimum</code>. You need to broadcast to keep the dimensions of the column so you aren't comparing row-wise with the entire column.</p>
<pre><code>np.minimum(a, a[:, col, None])
</code></pre>
<hr>
<p><strong><em>MCVE</em></strong></p>
<pre><code>a = np.array([[1, 3, 1, 9, 4],
[2, 3... | python|numpy | 2 |
1,332 | 56,601,817 | Conditional Cumulative Sums in Pandas | <p>I am a former Excel power user repenting for his sins. I need help recreating a common calculation for me.</p>
<p>I am trying to calculate the performance of a loan portfolio. In the numerator, I am calculating the cumulative total of losses. In the denominator, I need the original balance of the loans included ... | <p>You use a complex conditions depending on variables. It is easy to find a vectorized way for simple cumulative sums, but I cannot imagine a nice way for the Cumulative NCO.</p>
<p>So I would revert to Python comprehensions:</p>
<pre><code>data = [
{ 'Reference Age': ref,
'Outstanding Balance': df.loc[df.... | python|pandas|pandas-groupby | 2 |
1,333 | 56,611,648 | CUMSUM addition as below | <p>i have to calculate the cumsum addition in the below. A should be blank. B should be as it, c should 31 + 30 = 61, previous item and addition of present item, D = 61 + 31 = 92 and so on. </p>
<p>data: </p>
<pre><code> 0 1 cumsum
1 A 31
2 B 31 31
3 C 30 61
4 D 31 92
5 E 30 122
6 ... | <p>I think you need </p>
<pre><code>df['1'].shift(-1).cumsum().shift(1)
1 NaN
2 31.0
3 61.0
4 92.0
5 122.0
6 153.0
7 184.0
8 214.0
9 245.0
10 275.0
Name: 1, dtype: float64
</code></pre> | python|pandas|python-2.7 | 3 |
1,334 | 25,596,639 | Get the expected array with SciPy's Fisher exact test? | <p>SciPy allows you to conduct both chi square tests and Fisher exact tests.
While the output of the chi square test includes the expected array, the Fisher exact does not. </p>
<p>e.g.:</p>
<pre><code>from scipy import stats
import numpy as np
obs = np.array(
[[1100,6848],
[11860,75292]])
stats.chi2_continge... | <p>Fisher's exact test (<a href="http://en.wikipedia.org/wiki/Fisher%27s_exact_test" rel="nofollow">http://en.wikipedia.org/wiki/Fisher%27s_exact_test</a>) doesn't involve computing an expected array. That's why <a href="http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.fisher_exact.html" rel="nofollow">... | python|numpy|scipy | 2 |
1,335 | 25,936,899 | Select a subset of a Pandas DataFrame based on a list of criteria built from another DataFrame | <p>Suppose we have the following DataFrame</p>
<pre><code>>>> import pandas as pd
>>> df_org = pd.DataFrame({'A' : [1,2,3,4,5,6],
'B' : [1,1,1,1,2,2],
'C' : [1,2,3,4,1,2]})
A B C
0 1 1 1
1 2 1 2
2 3 1 3
3 4 1 4
4 5 2 1
5 6 ... | <p>A simple inner merge would work:</p>
<pre><code>In [285]:
df_org.merge(df_criteria, on=['B','C'])
Out[285]:
A B C
0 1 1 1
1 5 2 1
</code></pre> | python|pandas|dataframe | 7 |
1,336 | 26,390,895 | Why isn't pip updating my numpy and scipy? | <p>My problem is that pip won't update my Python Packages, even though there are no errors. </p>
<p>It is similar to <a href="https://stackoverflow.com/questions/21473600/matplotlib-version">this one</a>, but I am still now sure what to do. Basically, ALL my packages for python appear to be ridiculously outdated, even... | <p>In OS X 10.9, Apple's Python comes with a bunch of pre-installed extra packages, in a directory named <code>/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python</code>. Including <code>numpy</code>.</p>
<p>And the way they're installed (as if by using <code>easy_install</code> with an ancient ... | python|macos|numpy|pip|package-managers | 14 |
1,337 | 66,829,674 | Can't create pandas DataFrame with MultiIndex columns from dicts with tuples as columns | <p>I have this Python data structure:</p>
<pre><code>a = [
{
('Temperature', 'C'): 25,
('Temperature', 'F'): 77
},
{
('Temperature', 'C'): 30,
('Temperature', 'F'): 86
}
]
</code></pre>
<p>I try to convert this data structure to a tab separated string like this, having 2 ... | <p>How about using <code>MultiIndex.from_tuples</code></p>
<pre class="lang-py prettyprint-override"><code>df = pd.DataFrame(a)
df.columns = pd.MultiIndex.from_tuples(df.columns)
</code></pre>
<p><code>df</code> is now:</p>
<pre><code> Temperature
C F
0 25 77
1 30 86
</code></pre>
<pr... | python|pandas | 2 |
1,338 | 67,113,949 | make pandas recognize a list containing column names the same of of the columns in its dataframe? | <p>Let's say I have a list called x</p>
<pre><code>x = ['Sales', 'Total', 'Quantity']
</code></pre>
<p>and I have an excel dataframe with columns named 'Employee', 'Age', 'Sex', 'Sales' , 'Quantity' and 'Total'.
How do I make pandas only pick the columns of the dataframe that have the same name as of those in the list?... | <p>Just do:</p>
<pre><code>x = ['Sales', 'Total', 'Quantity']
df = df[x]
</code></pre>
<p>Since <code>x</code> is already a list of columns, use it inside <code>single-brackets</code> to subset the dataframe.</p>
<p><strong>OR</strong> use <code>Index.intersection</code>:</p>
<pre><code>df = df[df.columns.intersection... | python|pandas | 2 |
1,339 | 67,128,738 | Sort and Filter Pandas Dataframe in the most efficient manner | <p>I want to filter by the column name 'duration' and then display values greater than 200. This is just a snippet of the dataset. I have a very huge dataset. I can use df[df.duration > 200]. However, this runs on the entire dataframe. Is there any way in which I can specifically target the column duration and then ... | <p>Using pandas, I think <code>df[df.duration > 200]</code> would be among the best choices, but eager to compare with any alternatives.</p> | python|pandas | 0 |
1,340 | 10,907,917 | Python numpy addition error | <p>I'm getting a very odd error using a basic shortcut method in python. It seems, unless I'm being very stupid, I get different values for A = A + B, and A += B. Here is my code:</p>
<pre><code>def variance(phi,sigma,numberOfIterations):
variance = sigma
for k in range(1,numberOfIterations):
phik = np... | <p>The culprit is:</p>
<pre><code>variance = sigma
</code></pre>
<p>If you change that to:</p>
<pre><code>variance = sigma.copy()
</code></pre>
<p>You'll see the correct result.</p>
<p>This is because <code>+=</code> actually performs a (more efficient) in-place addition… And since both <code>variance</code> and <... | python|numpy | 7 |
1,341 | 68,441,433 | Get time data was submitted from Yfinance | <p>I am working on a project using the Yfinance module to get information from the stock market. My problem is, I need the date/time that the data was submitted, and I don't know how to access it. There is nothing about this that I can find on the documentation, but i know it is there because when i run:</p>
<pre><code... | <p>you can store the datetime data in a list</p>
<pre><code>from os import pardir
import yfinance as yf
data = yf.download(tickers='UBER', period='5d', interval='5m')
stored_datetime = data.index
print(stored_datetime)
</code></pre>
<p>Output:</p>
<pre><code>DatetimeIndex(['2021-07-16 09:30:00-04:00', '2021-07-16 09:3... | python|pandas|datetime|yfinance | 0 |
1,342 | 59,273,860 | How can I change my code so that string is NOT changed to float | <p>I am trying to write a code that detects fake news. Unfortunately, I keep getting the same error message. Please could someone explain where I've gone wrong? I have got some lines of codes from <a href="https://data-flair.training/blogs/advanced-python-project-detecting-fake-news/" rel="nofollow noreferrer">https://... | <p>I imagine you have text data in <code>df['headline']</code> column, you need a few steps to first convert the text data to a number based format, then pass it to machine learning models to handle. </p>
<p>You might want to refer to sklearn's <code>CountVectorizer</code> and <code>TfidfTransformer</code> <a href="ht... | python|pandas|numpy|scikit-learn | 1 |
1,343 | 44,991,076 | One line solution for editing a numpy array of counts? (python) | <p>I want to make a numpy array that contains how many times a value (between 1-3) occurs at a specific location. For example, if I have:</p>
<pre><code>a = np.array([[1,2,3],
[3,2,1],
[2,1,3],
[1,1,1]])
</code></pre>
<p>I want to get back an array like so:</p>
<pre><code... | <p>IIUC, you could take advantage of broadcasting:</p>
<pre><code>In [93]: ((a[:, None] - 1) == np.arange(3)[:, None]).swapaxes(2, 1).astype(int)
Out[93]:
array([[[1, 0, 0],
[0, 1, 0],
[0, 0, 1]],
[[0, 0, 1],
[0, 1, 0],
[1, 0, 0]],
[[0, 1, 0],
[1, 0, 0],
... | python|arrays|numpy | 3 |
1,344 | 45,126,821 | Saving confusion matrix | <p>Is there any possibility to save the confusion matrix which is generated by <code>sklearn.metrics</code>? </p>
<p>I would like to save multiple results of different classification algorithms in an array or maybe a pandas data frame so I can show which algorithm works best.</p>
<pre><code>print('Neural net: \n',con... | <p>First getting to array not defined problem.
In python list is declared as :</p>
<pre><code>array=[]
</code></pre>
<p>Since size of list is not given during declaration, no space is allocated. Hence we can't assign values the place which is not allocated.</p>
<pre><code>array[i]=some value, but no space is allocat... | python|pandas|dataframe|confusion-matrix | 1 |
1,345 | 45,255,167 | Use numpy.argwhere to obtain the matching values in an np.array | <p>I'd like to use <code>np.argwhere()</code> to obtain the values in an <code>np.array</code>.</p>
<p>For example:</p>
<pre><code>z = np.arange(9).reshape(3,3)
[[0 1 2]
[3 4 5]
[6 7 8]]
zi = np.argwhere(z % 3 == 0)
[[0 0]
[1 0]
[2 0]]
</code></pre>
<p>I want this array: <code>[0, 3, 6]</code> and did this:</... | <p>Why not simply use masking here:</p>
<pre><code>z<b>[z % 3 == 0]</b></code></pre>
<p>For your sample matrix, this will generate:</p>
<pre><code>>>> z[z % 3 == 0]
array([0, 3, 6])
</code></pre>
<p>If you pass a matrix with the same dimensions with booleans as indices, you get an array with the elements o... | python|numpy | 14 |
1,346 | 44,874,061 | Comparing cell values with a integer in pandas giving typeerror | <p>I have been trying to compare values of each cell in a row with an integer like so:</p>
<pre><code>df.loc[df['A'] <= 14, 'A']
</code></pre>
<p>All rows whose values are less than or equal to 14, but it shows an error like:</p>
<blockquote>
<pre><code>TypeError : '<=' is not supported between instances of st... | <p>You need <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.str.replace.html" rel="nofollow noreferrer"><code>replace</code></a> <code>,</code> to empty space and convert to <code>int</code>:</p>
<pre><code>df = pd.DataFrame({'A':['1,473','1,473','1,4', '1,2'],
'B':[2,4,... | python|pandas | 1 |
1,347 | 56,896,712 | How to represent the Null class in Multilabel Classification with Convolutional Neural Nets? | <p>I'm trying to label images with the various categories that they belong to with a convolutional neural net. For my problem, the image can be in a single category, multiple categories, or zero categories. Is it standard practice to set the zero category as all zeroes or should I add an additional null class neuron to... | <p>Yes, the "null" category should be represented as just zeros. In the end multi-label classification is a set of C binary classification problems, where C is the number of classes, and if all C problems output "no class", then you get a vector of just zeros.</p> | python|tensorflow|machine-learning|keras | 5 |
1,348 | 57,279,754 | What are the Tensorflow qint8, quint8, qint32, qint16, and quint16 datatypes? | <p>I'm looking at the Tensorflow tf.nn.quantized_conv2d function and I'm wondering what exactly the qint8, etc. dataypes are, particularly if they are the datatypes used for the "fake quantization nodes" in tf.contrib.quantize or are actually stored using 8 bits (for qint8) in memory.</p>
<p>I know that they are defin... | <p>These are the data types of the <code>output Tensor</code> of the function, <code>tf.quantization.quantize()</code>. This corresponds to the Argument, <code>T</code> of the function.</p>
<p>Mentioned below is the underlying code, which converts/quantizes a Tensor from one Data Type (e.g. <code>float32</code>) to an... | python|tensorflow|neural-network|tensorflow-lite|quantization | 6 |
1,349 | 57,037,252 | Updating column in for loop using merge | <p>Hi, </p>
<p>I have two dataframes and I want to loop through subsets of my first DF and merge values to my second DF. </p>
<p>My data looks like: </p>
<pre><code> DF1
product survey_id
X1 survey_1
x2 survey_1
x3 survey_2
x4 survey_3
x5 survey_3
... | <p>This is based on assumption: </p>
<p><strong>for all product xi, we require survey_j such that j is maximum.</strong></p>
<pre><code>>>> data = {'product':['x1','x1','x2','x2','x2'], 'survey_id':['survey_1','survey_2','survey_1', 'survey_2', 'survey_3'] }
>>> df = pd.DataFrame(data)
>>>... | python|sql|pandas|numpy|merge | 0 |
1,350 | 56,942,827 | How do I style a subset of a pandas dataframe? | <p>I previously asked <a href="https://stackoverflow.com/questions/56942320/how-do-i-style-only-the-last-row-of-a-pandas-dataframe">How do I style only the last row of a pandas dataframe?</a> and got a perfect answer to the toy problem that I gave. </p>
<p>Turns out I should have made the toy problem a bit closer to m... | <p>Using a <code>tuple</code> for <code>subset</code> worked for me, but not sure if it is the most elegant solution:</p>
<pre><code>df.style.background_gradient(cmap=cm,
subset=(df.index[-1], df.select_dtypes(float).columns))
</code></pre>
<p>Output:</p>
<p><a href="https://i.stack.img... | python|pandas|dataframe | 10 |
1,351 | 57,060,655 | Error com_error: (-2147221005, 'Invalid class string', None, None) while writing dataframe into excel binary notebook | <p>I am trying to write a data frame into excel sheet(xlsb), which is having formulas, using xlwing library:</p>
<pre><code>app = xw.App()
book = xw.Book('ABC.xlsb')
sheet = book.sheets('SL Dump')
sheet.range('A1').values=final_merge_dataframe
</code></pre>
<p>After running the above code, I am getting this error:... | <p>I use to have the same problem,</p>
<pre><code>dispatch = pythoncom.CoCreateInstanceEx(
pywintypes.com_error: (-2147221005, 'Invalid class string', None, None)
</code></pre>
<p>After installing MS Office into my machine the error was resolved, it seems <em>xlwings</em> requires some packages or files from the MS Off... | python|pandas | 0 |
1,352 | 57,255,266 | Search excel columns for matching text value, print row #s | <p>Here I grab the name and zip values from a different document; and store in variables: (works fine)</p>
<pre><code> Name = find_name.group(0)
</code></pre>
<p><strong>Then I simply want to search my excel file to find a match; where the <code>Name</code> text value is found, get row number(s):</strong>... | <p>I do not have your excel file, so I setup the following code:</p>
<pre><code>import pandas as pd
names = ["RHONDA GILBERT", "FRED FLINTSTONE", "FRED FLINTSTONE", "BARNEY RUBLE", "RHONDA GILBERT"]
add1 = ["123 Elm St", "254 Pine Ave", "254 Pin... | python|regex|excel|pandas|python-3.7 | 1 |
1,353 | 45,978,058 | Return NaN from indexing operation on a pandas series | <pre><code>a = pd.Series([0.1,0.2,0.3,0.4])
>>> a.loc[[0,1,2]]
0 0.1
1 0.2
2 0.3
dtype: float64
</code></pre>
<p>When a non existent index is added to the request along with existing ones, it returns NaN (which is what I need).</p>
<pre><code>>>> a.loc[[0,1,2, 5]]
0 0.1
1 0.2
2 0.3... | <p>Try <code>pd.Series.reindex</code> instead.</p>
<pre><code>out = a.reindex([0,1,2, 5])
print(out)
0 0.1
1 0.2
2 0.3
5 NaN
dtype: float64
</code></pre>
<hr>
<pre><code>out = a.reindex([5])
print(out)
5 NaN
dtype: float64
</code></pre> | python|pandas|indexing|nan|series | 5 |
1,354 | 45,927,399 | Memory error when initializing Xception using Keras | <p>I am having difficulty implementing the pre-trained Xception model for binary classification over new set of classes. The model is successfully returned from the following function:</p>
<pre><code>#adapted from:
#https://github.com/fchollet/keras/issues/4465
from keras.applications.xception import Xception
from ke... | <p>Per Yu-Yang, the simplest solution was to reduce the batch size, everything ran fine after that!</p> | tensorflow|out-of-memory|keras|gpu | 0 |
1,355 | 46,045,512 | h5py - HDF5 database randomly returning NaNs and near very small data with multiple readers? | <p>I have an HDF5 dataset and I'm using a framework which is creating multiple processes to read from it (PyTorch's DataLoader, but this framework shouldn't be important). I'm indexing the first dimension of a 3D float array randomly, and to debug what was going on, I have been summing the slice from the indexing. Ever... | <p>I encountered the very same issue, and after spending a day trying to marry PyTorch DataParallel loader wrapper with HDF5 via h5py, I discovered that it is crucial to open <code>h5py.File</code> inside the new process, rather than having it opened in the main process and hope it gets inherited by the underlying mult... | python|numpy|hdf5|h5py | 7 |
1,356 | 35,700,389 | How to use numpy where for several possible values? | <p>Consider a numpy ndarray called <code>picks_user</code> with shape <code>picks_user.shape = (2016,3)</code>.
The 'columns' represent the variables user, item and count in that order. The 'rows' represent observations.</p>
<p>When performing:</p>
<p><code>target_users = picks_user[np.where(picks_user[:,1]== 2711)][... | <p>You can use <code>np.in1d</code> as:</p>
<pre><code>>>> picks_user = np.random.randint(0,10, (10,3))
>>> picks_user
array([[7, 8, 7],
[6, 0, 9],
[5, 6, 7],
[6, 7, 3],
[0, 1, 3],
[8, 7, 5],
[2, 6, 6],
[7, 9, 8],
[1, 7, 1],
[9, 8, 4]])
&... | python|numpy|vectorization | 3 |
1,357 | 35,495,543 | pandas DataFrame cumulative value | <p>I have the following pandas dataframe:</p>
<pre><code>>>> df
Category Year Costs
0 A 1 20.00
1 A 2 30.00
2 A 3 40.00
3 B 1 15.00
4 B 2 25.00
5 B 3 35.00
</code></pre>
... | <p>This works in pandas 0.17.0 Thanks to @DSM in the comments for the terser solution.</p>
<pre><code>df['Cumulative Costs'] = df.groupby(['Category'])['Costs'].cumsum()
>>> df
Category Year Costs Cumulative Costs
0 A 1 20 20
1 A 2 30 50
2 ... | python|pandas|dataframe | 2 |
1,358 | 35,695,259 | pandas read_sql convers column names to lower case - is there a workaroud? | <p>related: <a href="https://stackoverflow.com/questions/28318722/pandas-read-sql-drops-dot-in-column-names">pandas read_sql drops dot in column names</a></p>
<p>I use pandas.read_sql to create a data frame from an sql query from a postgres database.
some column aliases\names use mixed case, and I want it to propagat... | <p>Postgres normalizes unquoted column names to lower case. If you have such a table:</p>
<pre><code>create table foo ("Id" integer, "PointInTime" timestamp);
</code></pre>
<p>PostgreSQL will obey the case, but you will <strong>have to</strong> specify table names quoted as such:</p>
<pre><code>select "Id", "PointIn... | postgresql|pandas|sqlalchemy | 4 |
1,359 | 11,718,852 | sum over values in python dict except one | <p>Is there a way to sum over all values in a python dict except one by using a selector in </p>
<pre><code>>>> x = dict(a=1, b=2, c=3)
>>> np.sum(x.values())
6
</code></pre>
<p>?
My current solution is a loop based one:</p>
<pre><code>>>> x = dict(a=1, b=2, c=3)
>>> y = 0
>>... | <p>You could loop over the dict to create a generator for the <code>sum</code> method:</p>
<pre><code>np.sum(value for key, value in x.iteritems() if key != 'a')
</code></pre> | python|dictionary|numpy|sum | 8 |
1,360 | 11,687,953 | Operations on rows in scipy sparse matrix of csr format | <p>I would like to multiply single rows of a csr matrix with a scalar. In numpy I would do</p>
<pre><code>matrix[indices,:] = x * matrix[indices,:]
</code></pre>
<p>For csr this raises an exception in scipy.</p>
<p>Is there a way to do this similarily with csr matrixes?</p> | <p>No, there's no way to this directly, because although you can compute <code>row * x</code>, you can't assign to a row in a CSR matrix. You can either convert to DOK format and back, or work on the innards of the CSR matrix directly. The <code>i</code>'th row of a CSR matrix <code>X</code> is the slice</p>
<pre><cod... | python|numpy|scipy | 12 |
1,361 | 28,863,853 | Minimum element from the matrix column | <p>I need to find minimum over all elements from the column which has the maximum column sum.
I do the following things:</p>
<p>Create random matrix</p>
<pre><code>from numpy import *
a = random.rand(5,4)
</code></pre>
<p>Then calculate sum of each column and find index of the maximum element</p>
<pre><code>c = a.s... | <p>You can extract the minimum value of a column as follows (using the variables you have indicated):</p>
<pre><code>e=a[:,d].min()
</code></pre>
<p>Note that using</p>
<pre><code>a=min(a[:,d])
</code></pre>
<p>will break you out of Numpy, slowing things down (thanks for pointing this out @SaulloCastro).</p> | python|numpy|matrix|minimum | 5 |
1,362 | 50,905,520 | Using pd.ExcelWriter to write many dataframes to a single Excel workbook for VBA manipulation | <p>I have over 60 dataframes to write to an excel template. My intention is to paste all these to various named ranges via vba once all df's are exported. </p>
<pre><code>excel_writer = pd.ExcelWriter('test.xlsx')
df['state'].value_counts().to_excel(excel_writer, sheet_name='Sheet1', startrow=5, startcol=0, na_rep=0, ... | <p><code>df_Shape</code> is a shape of <code>df</code><br>
<code>df_Start_Date</code> is a time</p>
<p>They're not <code>pd.DataFrame</code> objects, thus they don't have method <code>.to_excel</code></p>
<p><strong>EDIT</strong>:<br>
You can create new dataframe with necessary statistics and write it to the same she... | python|pandas|dataframe | 2 |
1,363 | 50,932,129 | Pandas: groupby column and set it as index | <p>If I have a dataframe such as this:</p>
<pre><code>df1 = pd.DataFrame({'A':[1,2,3,4,5,6,7,8],
'B':['a','b','c','d','e','f','g','h'],
'C':['u1','u2','u4','u3','u1','u1','u2','u4']})
</code></pre>
<p>And would like it to be as such, with u1 to u4 as the indices.</p>
<pre><code> A ... | <p>You can set C as an index and then sort it : </p>
<pre><code>df1.set_index('C').sort_index(axis=0)
</code></pre> | python|pandas | 2 |
1,364 | 50,955,960 | How can I map 2 numpy arrays with same indices | <p>I am trying to map 2 numpy arrays as [x, y] similar to what zip does for lists and tuples.</p>
<p>I have 2 numpy arrays as follows:</p>
<pre><code>arr1 = [1, 2, 3, 4]
arr2 = [5, 6, 7, 8]
</code></pre>
<p>I am looking for an <code>output as np.array([[[1, 5], [2, 6], [3, 7], [4, 8]]])</code></p>
<p>I tried this b... | <p>You are looking for <a href="https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.dstack.html" rel="nofollow noreferrer"><strong><code>np.dstack</code></strong></a></p>
<blockquote>
<p>Stack arrays in sequence depth wise (along third axis).</p>
</blockquote>
<pre><code>np.dstack([arr1, arr2])
array... | python|numpy | 2 |
1,365 | 50,941,409 | Quote marks in bash file created in Python | <p>I'm using Python and Pandas to write multiple bash scripts. I have a pandas.Series containing the script. Simplified::</p>
<pre><code>script = pd.Series([
'#!/bin/bash',
'#SBATCH --output "/home/path/output_filename.out"'
])
</code></pre>
<p>I then use <code>script.to_csv('script_file.bat',index=False)</code> to c... | <p>It's possible to explicitly specify quoting for <a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_csv.html" rel="nofollow noreferrer"><code>df.to_csv</code></a>:</p>
<pre><code>import csv
pd.DataFrame(script).to_csv("test.sh", index=False, header=False,
... | python|string|bash|pandas|quotes | 1 |
1,366 | 50,698,834 | How can I remove sharp jumps in data? | <p>I have some skin temperature data (collected at 1Hz) which I intend to analyse. </p>
<p>However, the sensors were not always in contact with the skin. So I have a challenge of removing this non-skin temperature data, whilst preserving the actual skin temperature data. I have about 100 files to analyse, so I need to... | <p>Try the code below (I used a tangent function to generate data). I used the second order difference idea from Mad Physicist in the comments.</p>
<pre><code>import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.DataFrame()
df[0] = np.arange(0,10,0.005)
df[1] = np.tan(df[0])
#the following... | python|python-3.x|pandas|dataframe|filtering | 3 |
1,367 | 33,226,017 | Pandas Aggregating a GroupBy object using UDF | <p>Suppose that I have the following. I group by "happy" and then sum over each group. It works great.</p>
<pre><code>import pandas as pd
testdf = pd.DataFrame({"happy": [1, 2, 1, 3], "sad": [4, 5, 6, 7], \
"cool":[1, 99, 0, -5]})
testgb = testdf.groupby(["happy"])
testgb.sum()
</code></pre>
<p>... | <p>I'm assuming that you want to pass the list of values from other column, e.g. <code>sad</code>. You can use the <code>agg</code> function</p>
<pre><code>testdf = pd.DataFrame({"happy": [1, 2, 1, 3], "sad": [4, 5, 6, 7], "cool":[1, 99, 0, -5]})
testgb = testdf.groupby(["happy"]).agg({'sad': lambda x: max(x)})
</code... | python|pandas | 1 |
1,368 | 33,460,209 | How to get the union of two MultiIndex DataFrames? | <p>How do I merge two MultiIndexed DataFrames? </p>
<p>For example, let's say I have:</p>
<pre><code>index1 = pd.MultiIndex.from_tuples([('2010-01-01', 'Jim'),
('2010-01-01', 'Mike'),
('2010-01-02', 'Sam')])
index2 = pd.MultiIndex.from_tuples([(... | <p>Try:</p>
<pre><code>df3 = df1.join(df2, how='outer', lsuffix='1', rsuffix='2')
</code></pre>
<p>Or</p>
<pre><code>df3 = pd.merge(df1, df2, how='outer', left_index=True, right_index=True)
</code></pre> | python|pandas | 1 |
1,369 | 66,569,033 | How to fill one column in a csv by comparing values to three different columns in another csv file? | <p>As I am completely new to pandas, I would like to ask.</p>
<p>I have two CSV files.</p>
<p>One of them has all the colors of different languages in one column:</p>
<p>I have color blue in three rows here: azul, bleu all means blue in different languages, so they should be in the same group 1. rouge and rojo means re... | <p>You can use <code>map</code></p>
<p>Using <code>df2</code> you can create a dict <code>d</code> and then map the name values to their corresponding group.</p>
<pre><code>d = dict(zip(df2.T.values[3], df2.values[:,:3].tolist()))
df1['group'] = df1.name.map(lambda x: [k for k in d if x in d[k]][0])
</code></pre>
<hr /... | python-3.x|pandas|dataframe|csv | 1 |
1,370 | 66,721,047 | Pandas: compute average and standard deviation by clock time | <p>I have a DataFrame like this:</p>
<pre><code> date time value
0 2019-04-18 07:00:10 100.8
1 2019-04-18 07:00:20 95.6
2 2019-04-18 07:00:30 87.6
3 2019-04-18 07:00:40 94.2
</code></pre>
<p>The DataFrame contains value recor... | <p>We can <code>split</code> the <code>time</code> column around the delimiter <code>:</code>, then slice the <code>hour</code> component using <code>str[0]</code>, finally <code>group</code> the dataframe on <code>date</code> along with <code>hour</code> component and aggregate column <code>value</code> with <code>mea... | python|pandas|dataframe | 4 |
1,371 | 16,574,470 | Out of memory when using numpy's multivariate_normal random sampliing | <p>I tried to use numpy.random.multivariate_normal to do random samplings on some 30000+ variables, while it always took all of my memory (32G) and then terminated. Actually, the correlation is spherical and every variable is correlated to about only 2500 other variables. Is there another way to specify the spherical c... | <p>If your correlation is spherical, that is the same as saying that the value along each dimension is uncorrelated to the other dimensions, and that the variance along every dimension is the same. You don't need to build the covariance matrix at all, drawing one sample from your 30,000-D multivariate normal is the sam... | python|memory|numpy | 1 |
1,372 | 57,403,472 | How do I add a new feature column to a tf.data.Dataset object? | <p>I am building an input pipeline for proprietary data using Tensorflow 2.0's data module and using the tf.data.Dataset object to store my features. Here is my issue - the data source is a CSV file that has only 3 columns, a label column and then two columns which just hold strings referring to JSON files where that d... | <p>Wow, this is embarassing, but I have found the solution and it's simplicity literally makes me feel like an idiot for asking this. But I will leave the answer up just in case anyone else is ever facing this issue.</p>
<p>You first create a new tf.data.Dataset object using any function that returns a Dataset, such a... | tensorflow|dataset|tensorflow-datasets | 15 |
1,373 | 57,478,251 | Maximum of an array constituting a pandas dataframe cell | <p>I have a pandas dataframe in which a column is formed by arrays. So every cell is an array.</p>
<p>Say there is a column A in dataframe df, such that</p>
<pre><code>A = [ [1, 2, 3],
[4, 5, 6],
[7, 8, 9],
... ]
</code></pre>
<p>I want to operate in each array and get, e.g. the maximum of each a... | <p>Here is one way without apply:</p>
<pre><code>df['B']=np.max(df['A'].values.tolist(),axis=1)
</code></pre>
<hr>
<pre><code> A B
0 [1, 2, 3] 3
1 [4, 5, 6] 6
2 [7, 8, 9] 9
</code></pre> | python|arrays|pandas|data-analysis | 1 |
1,374 | 43,549,885 | Trying to replace all values matching a pattern in a pandas dataframe with the matched capture groups reversed | <p>Been trying to replace all values matching a pattern in a pandas dataframe with the matched capture groups reversed. So <code>Mouse, Mickey</code> would be replaced with <code>Mickey Mouse</code></p>
<p>Dataframe looks like:</p>
<pre><code>+---+---------------+------+------+------+------+------+------+------+-----... | <p>You need to specify <code>regex=True</code>; By default, <em>DataFrame.replace</em> method replaces values literally:</p>
<pre><code>df = pd.DataFrame({"A": ["Mouse, Mickey", "Duck, Donald"]})
df.replace(r'(.*),\s+(.*)', r'\2 \1', inplace=True, regex=True)
df
# A
#0 Mickey Mouse
#1 Donald Duck
</co... | python|regex|python-3.x|pandas|dataframe | 2 |
1,375 | 43,524,953 | Find index where elements change value pandas dataframe | <p>Regaring to <a href="https://stackoverflow.com/questions/19125661/find-index-where-elements-change-value-numpy">this question/answer</a>, is there a way to accomplish the same function for a pandas dataframe structure without casting it as a numpy array?</p> | <h3>Update: we can use this per @LorenzoMeneghetti</h3>
<pre><code>s[s.diff() != 0].index.tolist()
</code></pre>
<p>Output:</p>
<pre><code>[0, 5, 8, 9, 10, 11, 12, 13, 14, 15, 16]
</code></pre>
<hr />
<pre><code>s = pd.Series([1, 1, 1, 1, 1, 2, 2, 2, 3, 4, 3, 4, 3, 4, 3, 4, 5, 5, 5])
print(s.diff()[s.diff() != 0].inde... | python|pandas | 13 |
1,376 | 43,547,754 | How to Import data through Panda for several years but specifc months? | <p>I want to import data from panda for 10 years. But I need each season to be imported separately. for example all the data during Spring from 2000 to 2010.</p>
<p>I have following code but this does not separate the season. </p>
<pre><code>import pandas_datareader.data as web
import datetime
start = datetime.dateti... | <p>Assuming that you are targeting Spring months (maybe January 1st through April 30th, subject to change), you can create a <code>list</code> of date <code>tuples</code> where each <code>tuple</code> holds the <code>start</code> and <code>end</code> dates for a given <code>year</code>. For instance, the first element ... | python|python-2.7|python-3.x|pandas-datareader | 2 |
1,377 | 43,668,993 | How to select rows that not consist of only NaN values and 0s | <p>This is my dataframe:</p>
<pre><code>cols = ['Country', 'Year', 'Orange', 'Apple', 'Plump']
data = [['US', 2008, 17, 29, 19],
['US', 2009, 11, 12, 16],
['US', 2010, 14, 16, 38],
['Spain', 2008, 11, None, 33],
['Spain', 2009, 12, 19, 17],
['France', 2008, 17, 19, 21],
... | <pre><code>In [307]: df[~df[['Orange','Apple','Plump']].fillna(0).eq(0).all(1)]
Out[307]:
Country Year Orange Apple Plump
0 US 2008 17.0 29.0 19.0
1 US 2009 11.0 12.0 16.0
2 US 2010 14.0 16.0 38.0
3 Spain 2008 11.0 NaN 33.0
4 Spain 2009 12.0 19.0 1... | python|pandas | 6 |
1,378 | 43,643,663 | Merge serval models (LSTMs) in TensorFlow | <p>I know how to merge different models into one in Keras.</p>
<pre><code>first_model = Sequential()
first_model.add(LSTM(output_dim, input_shape=(m, input_dim)))
second_model = Sequential()
second_model.add(LSTM(output_dim, input_shape=(n-m, input_dim)))
model = Sequential()
model.add(Merge([first_model, second_mod... | <p>As was said in the comment, I believe the simplest way to do this is just to concatenate the outputs. The only complication that I've found is that, at least how I made my LSTM layers, they ended up with the exact same names for their weight tensors. This led to an error because TensorFlow thought the weights were a... | deep-learning|tensorflow|keras | 3 |
1,379 | 1,664,917 | Automatic string length in recarray | <p>If I create a recarray in this way:</p>
<pre><code>In [29]: np.rec.fromrecords([(1,'hello'),(2,'world')],names=['a','b'])
</code></pre>
<p>The result looks fine:</p>
<pre><code>Out[29]:
rec.array([(1, 'hello'), (2, 'world')],
dtype=[('a', '<i8'), ('b', '|S5')])
</code></pre>
<p>But if I want to specif... | <p>If you don't need to manipulate the strings as bytes, you may use the object data-type to represent them. This essentially stores a pointer instead of the actual bytes:</p>
<pre><code>In [38]: np.array(data, dtype=[('a', np.uint8), ('b', np.object)])
Out[38]:
array([(1, 'hello'), (2, 'world')],
dtype=[('a'... | python|numpy | 2 |
1,380 | 72,907,661 | Creating a Multiple Dictionaries from a CSV File | <p>I am currently importing a file as so:</p>
<pre><code>df= pd.read_csv(r"Test.csv")
</code></pre>
<p>And the output looks like</p>
<pre><code> Type Value
0 Food_Place_1 1
1 Food_Place_2 2
2 Car_Type_1 3
3 Car_Type_2 4
</code></pre>
<p>I would like to iterate through this df and dep... | <p>Create category for possible aggregate lists for nested dictionary:</p>
<pre><code>#If category is set by remove digits
cat = df['Type'].str.replace('\d','')
#If category is set by first letter
#cat = df['Type'].str[0]
d = df.rename(columns={'Type':'Component'}).groupby(cat).agg(list).to_dict('index')
print (d)
{'... | python|pandas | 2 |
1,381 | 73,022,079 | Turning Python Keras Machine Learning model into repeatable function that can take as input multiple X and y data sets | <p>I am currently building various machine learning models, each of the models takes in X and Y data that represent different stock prices e.g. there's an X and y data frame for each stock e.g. Apple, Microsoft.</p>
<p>I am trying to produce these models so that they are repeatable, modular, functions that I can quickl... | <p>Try providing default values for your <code>LSTM_regressor</code> function.</p>
<p><code>def LSTM_regressor(X_train=X_tr, X_test=X_te, y_train=y_tr, y_test=y_te):</code></p>
<p>From the docs:</p>
<blockquote>
<p>sk_params takes both model parameters and fitting parameters. Legal
model parameters are the arguments of... | python|function|tensorflow|machine-learning|keras | 1 |
1,382 | 72,952,488 | Pandas Scipy mannwhitneyu in this type of data table | <p>I have a data table similar to this one (but huge), many types and more "Spot" cells for each "Color":</p>
<pre><code>Type Color Spots
A Blue 792
A Blue 56
A Blue 2726
A Blue 780
A Blue 591
A Blue 2867
A Blue 193
A Green 134
A Green 631
A Green ... | <p>Your questions might be leaving a lot that is obvious to you implied for people who are not as familiar with the sort of statistical analysis you are interested in. That might be making it difficult to help you along, but by trying to cover all my bases, I think I might be able to help you regardless.</p>
<p>The fir... | python|pandas|scipy|statistics | -1 |
1,383 | 72,990,835 | How to send emails for each person in a excel file | <p>So i have a file em.xlsx where i have Name & Email columns, i want to send email when Name matchs the the filename in a directory</p>
<p>How can i do that ? so far i have this code below, but it actualy return nothing</p>
<pre><code>import glob
import pandas as pd
import smtplib
from email.mime.text import MIMET... | <p>Following code passed my test. Hope it can help you. I'm not sure what's <code>while name == os.path:</code> for, so I just ignore it.</p>
<pre><code>import glob
import pandas as pd
import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
from email.mime.application import M... | python|pandas|email|path|smtplib | 0 |
1,384 | 72,845,550 | Python Pandas. Endless cycle | <p>Why does this part of the code have an infinite loop? It can't be so, because where I stop this part of code (in Jupyter Notebook), all 99999 values have changed to oil_mean_by_year[data.loc[i]['year']]</p>
<pre><code>for i in data.index:
if data.loc[i]['dcoilwtico'] == 99999:
data.loc[i, 'dcoilwtico'] ... | <p>Use merge to align the oil mean of a year with the given row:</p>
<p>Merge on <code>data['year']</code> vs <code>oil_mean_by_year</code>'s index</p>
<pre><code>data_with_oil_mean = pd.merge(data, oil_mean_by_year.rename("oil_mean"),
left_on="year", right_index=True, ... | python|pandas | 0 |
1,385 | 70,622,976 | pytest: use fixture with pandas dataframe for parametrization | <p>I have a fixture, which returns a <code>pd.DataFrame</code>. I need to insert the individual columns (<code>pd.Series</code>) into a unit test and I would like to use <code>parametrize</code>.</p>
<p>Here's a toy example without <code>parametrize</code>. Every column of the dataframe will be tested individually. How... | <p>If you try to generate multiple data from a fixture based on another fixture you will get the <code>yield_fixture function has more than one 'yield'</code> error message.</p>
<p>One solution is to use <a href="https://docs.pytest.org/en/6.2.x/fixture.html#parametrizing-fixtures" rel="nofollow noreferrer">fixture par... | python|pandas|pytest|fixtures|parametrized-testing | 1 |
1,386 | 70,688,556 | What is the equivalent of PyTorch's BoolTensor in Tensorflow 2.x? | <p>Is there an equivalent of BoolTensor from Pytorch in Tensorflow assuming I have the below usage in Pytorch that I want to migrate to Tensorflow</p>
<pre><code>done_mask = torch.BoolTensor(dones.values).to(device)
next_state_values[done_mask] = 0.0
</code></pre> | <p>What is <code>dones</code>?
Assuming it's a 0/1 tensor, you can convert it to a Bool tensor like this:</p>
<pre><code>tf.cast(dones,tf.bool)
</code></pre>
<p>However, if you want to assign values to a tensor, you can't do it that way.</p>
<p>A way, which I recommend, is to multiply by a matrix of 1/0:</p>
<pre><code... | python-3.x|tensorflow|pytorch|tensorflow2.0|tf.keras | 1 |
1,387 | 70,498,489 | Create multiple dataframes with for loop in python | <p>I need to compile grades from 10 files named quiz2, quiz3 [...], quiz11.</p>
<p>I have the following transformation:</p>
<ul>
<li>Import the xls to df with pandas</li>
<li>Get only the 4 renamed columns</li>
<li>Keep only the highest grade if there is multiple values for the same ID</li>
</ul>
<p>The code for one da... | <p>You could generate the file name dynamically by looping through a range of numbers from 1 to 11 and concatenating the number to the file name and suffix.</p>
<pre><code>#create an empty dataframe for collecting loop results
cumulative_df = pd.DataFrame(columns = ['a'])
#loop through a range of numbers from 1 to 11
... | python|pandas|dataframe|loops|file | -1 |
1,388 | 70,415,426 | Allocator ran out of memory - how to clear GPU memory from TensorFlow dataset? | <p>Assuming a Numpy array <code>X_train</code> of shape <code>(4559552, 13, 22)</code>, the following code:</p>
<pre class="lang-py prettyprint-override"><code>train_dataset = tf.data.Dataset \
.from_tensor_slices((X_train, y_train)) \
.shuffle(buffer_size=len(X_train) // 10) \
.batch(batch_size)
</code></p... | <p>Try setting a hard limit on the total GPU memory as shown in <a href="https://www.tensorflow.org/guide/gpu#limiting_gpu_memory_growth" rel="nofollow noreferrer">here</a></p>
<pre><code>import tensorflow as tf
gpus = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(gpus[0],... | python|tensorflow|gpu|out-of-memory | 1 |
1,389 | 70,407,722 | Using one excel column on two places JSON | <p>I have Excel file which I am converting to JSON and merge with existing JSON file.
The case is that In Excel I have column "ja" and values in it. IS there a way to add values from that column in JSON, on two places: "ja" and "ja-jpn"
Expected output:</p>
<pre><code>"ja":{
... | <p>Before you convert your dataframe to JSON, just duplicate the column, like this:</p>
<pre><code>if 'ja' in new_data.columns:
new_data['ja-jpn'] = new_data['ja']
</code></pre> | python|json|excel|pandas|localization | 0 |
1,390 | 70,567,173 | Pandas - how to convert objects to float values? | <p>I am working with a pandas dataframe of football players. There is a column with the value of each player. The problem is the type of this column is an object and I want to convert it to float64. How can I do it? The variable is <code>Release clause</code>.</p>
<pre><code>df_fifa['Release Clause']
0 €226.5M
1... | <p>Convert your symbol <code>€</code>, <code>K</code> and <code>M</code> to <code>''</code>, <code> * 1e3</code> and <code> * 1e6</code> and evaluate your expression with <code>pd.eval</code>:</p>
<pre><code>mapping = {'€': '', 'K': ' * 1e3', 'M': ' * 1e6'}
df_fifa['Release Clause'] = \
pd.eval(df_fifa['Release Cl... | python|pandas|dataframe|variables | 2 |
1,391 | 70,527,526 | When using df1[~df1.isin(df2)].dropna() Question | <p>I have a data set filled with invoices columns including:</p>
<ul>
<li>CaseID</li>
<li>Customer</li>
<li>Supplier</li>
<li>Part Number</li>
<li>Cost.</li>
</ul>
<p>This data set includes Charges and Credits. I want to remove the Credits and the Charges they credited from DataFrame. I'd like to remove the rows in ori... | <p>Try this:</p>
<pre><code>invoices = pd.DataFrame([['111', '2g', 53],
['112', '7g', 25],
['112', '7g', 25],
['113', '8g', 20],
['113', '8g', -20],
['114', '9g', 15],
['... | python|pandas|dataframe | 1 |
1,392 | 70,441,819 | In a pandas string column, eliminate the text preceding a substring | <p>For example I have a Pandas DataFrame with a string column in which I would like to delete the <code>**bold**</code> text before a substring:</p>
<pre><code>Column1
**Yon-RM-**CT 500M
**Abib-RM-**CT 500M
**Wal-RM-**CT 500M
**Sopxc-RM-**CT 1000M
</code></pre>
<p>Notice that the bold text could have different length b... | <p>Assuming all you want is CT 500M, and all follow the same format, apply a lambda function that splits by "-", and get the third index</p>
<pre><code> df["Column1"] = df.apply(lambda x: x["Column1"].split("-")[2], axis=1)
</code></pre>
<p>You could also split by "RM"<... | python|pandas|substring | 0 |
1,393 | 42,914,747 | Setting value to a copy of a slice of a DataFrame | <p>I am setting up the following example which is similar to my situation and data:</p>
<p>Say, I have the following DataFrame: </p>
<pre><code>df = pd.DataFrame ({'ID' : [1,2,3,4],
'price' : [25,30,34,40],
'Category' : ['small', 'medium','medium','small']})
</code></pre>
<p><br></p>
<pre>... | <p>One approach with <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html" rel="nofollow noreferrer"><code>np.where</code></a> -</p>
<pre><code>mask = df.Category.values=='small'
df['Discount'] = np.where(mask,df.price*0.01, df.price*0.02)
</code></pre>
<p>Another way to put things a bit dif... | python|pandas|numpy|dataframe | 8 |
1,394 | 42,956,997 | Stripping and testing against Month component of a date | <p>I have a dataset that looks like this:</p>
<pre><code>import numpy as np
import pandas as pd
raw_data = {'Series_Date':['2017-03-10','2017-04-13','2017-05-14','2017-05-15','2017-06-01']}
df = pd.DataFrame(raw_data,columns=['Series_Date'])
print df
</code></pre>
<p>I would like to pass in a date parameter as a stri... | <p>convert your column to <code>Timestamp</code></p>
<pre><code>df.Series_Date = pd.to_datetime(df.Series_Date)
date = pd.to_datetime('2017-03-01')
</code></pre>
<p>Then</p>
<pre><code>df[
(df.Series_Date.dt.year - date.year) * 12 +
df.Series_Date.dt.month - date.month == 3
]
Series_Date
4 2017-06-01
<... | python|python-2.7|pandas | 0 |
1,395 | 42,605,840 | Automatic extraction of recent nine months data in python | <p>I have a data frame which consists of data aggregated over certain time span with 'date'as one of the columns. Now every day a new data with exactly same columns is added to this aggregated data. Now I want to apply a filter on this aggregated data, that post appending new daily data I want only recent nine months d... | <p>You can dynamically generate 6 months old date</p>
<pre><code>from datetime import date
from dateutil.relativedelta import relativedelta
six_months_old = date.today() + relativedelta(months=-6)
six_months_old
#datetime.date(2016, 9, 5)
</code></pre>
<p>now use this value to filter your dataframe</p>
<pre><code>... | python|pandas|datetime | 2 |
1,396 | 42,955,746 | Process to build our own model for image detection | <p>Currently, I am working on deep neural network for image detection and I founded a model called YOLO Network, and it's very powerful to make objects detections, but I have a question:</p>
<ul>
<li>How can we design and concept our own model? Do we use a brut force for that, for example "I use 2 convolutional and 1... | <p>There are a couple of papers addressing this issue. For example in <a href="http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Szegedy_Rethinking_the_Inception_CVPR_2016_paper.pdf" rel="nofollow noreferrer">http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Szegedy_Rethinking_the_Inceptio... | tensorflow|deep-learning|object-detection | 2 |
1,397 | 27,100,799 | how to do 3d matrix to 3d multiplication in python without loops? | <p>I am doing segmentation part in my project, where I need to multiply a 3 dimensional mask to a color image. </p>
<p>What am doing now:</p>
<pre><code>maskedFrame=np.zeros((rgbFrame.shape))
maskedHsvFrame=np.zeros((rgbFrame.shape))
for color in range(0,3):
maskedFrame[:,:,color]=rgbFrame[:,:,color]*biscuitMask
... | <p>AFAIK, numpy can <a href="http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html" rel="nofollow">braodcast</a> <code>biscuitMask</code> along the color dimension to fit the shape of the frame.<br>
This broadcasting is done automatically. Thus,</p>
<pre><code>maskedFrame = rgbFrame * biscuitMask
maskedHsvFram... | python|image-processing|numpy|image-segmentation | 0 |
1,398 | 14,398,188 | Numpy import in PyCharm yields "Cannot locate working compiler" | <p>I am new to Python and PyCharm, installed PyCharm 2.6 (on Mac OSX) and tried to import NumPy for Python 3.3. JetBrains support file tells me to install Cython which also yields "Cannot locate working compiler"</p>
<p>How and which compiler do I need to install?</p>
<p>Thanks!</p> | <p>Check whether the 'gcc' command works. If it's there, try setting the CC environment variable to 'gcc'.</p> | compiler-construction|numpy|python-3.x|pycharm | 0 |
1,399 | 25,275,009 | Pandas Series.filter.values returning different type than numpy array | <p>I am trying to run the <code>scipy.stats.entropy</code> function on two arrays. It is being run on each row of a Pandas DataFrame via the apply function:</p>
<pre><code>def calculate_H(row):
pk = np.histogram(row.filter(regex='stuff'), bins=16)[0]
qk = row.filter(regex='other').values
stats.entropy(pk, ... | <p>I suspect <code>qk</code> is an <code>object</code> array and not an array of integers. In <code>calculate_H</code>, try this:</p>
<pre><code>qk = row.filter(regex='other').values.astype(int)
</code></pre>
<p>(i.e. cast the values to an array of integers).</p> | python|numpy|pandas|scipy | 2 |
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