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 |
|---|---|---|---|---|---|---|
371,700 | 71,503,974 | Dask where returns NaN on valid array | <p>I'm trying to accelerate my <code>numpy</code> code using <code>dask</code>. Following is a part of my <code>numpy</code> code</p>
<pre><code>arr_1 = np.load('<arr1_path>.npy')
arr_2 = np.load('<arr2_path>.npy')
arr_3 = np.load('<arr3_path>.npy')
arr_1 = np.concatenate((arr_1, arr_2[:,:,np.newaxis... | <p>The array you've constructed with <code>da.where</code> has <a href="https://docs.dask.org/en/stable/array-chunks.html#unknown-chunks" rel="nofollow noreferrer">unknown chunk sizes</a>, which can happen whenever the size of an array depends on lazy computations that haven’t yet been performed. Unknown values within ... | numpy|dask|numpy-ndarray|zarr | 1 |
371,701 | 71,473,154 | How to justify columns in pandas? | <p>The original data frame:</p>
<pre><code> B C D
0 OE EG A
1 CA EG A
2 HA1 EG A
3 HA2 EG A
4 CB EG A
5 HB1 EG A
</code></pre>
<p>The desired data frame:</p>
<pre><code> B C D
0 OE EG A
1 CA EG A
2 HA1 EG A
3 HA2 EG A
4 CB EG ... | <p>Try this:</p>
<pre><code>str_cols = df.select_dtype('object')
df[str_cols.columns] = str_cols.apply(lambda col: col.str.strip())
</code></pre> | python|pandas | 0 |
371,702 | 71,614,795 | Assignment with pandas iloc where RHS length is shorter than LHS | <p>I am trying to perform <code>np.where</code> function on a dataframe starting from row 20 onward. The code that I entered as follow:</p>
<pre><code>df['buy'] = np.where((df.iloc[20:,]['signal']==1), 'buy','no buy')
</code></pre>
<p>It showed the error below:
ValueError: Length of values (226) does not match length o... | <p>This is an assignment error, <code>df</code> has 246 entries, but you are trying to assign a numpy vector with 226 values to it.</p>
<p>What do you intend the result should be?</p>
<p>you could subset the dataframe?</p>
<pre><code>df_sub = df.iloc[20:].copy()
df_sub['buy']=np.where((df_sub.iloc['signal']==1), 'buy',... | python|pandas|conditional-statements|mismatch | 0 |
371,703 | 71,737,396 | How to update Plotly scatter by filtering pandas dataframe | <p>I have created a scatter plot in plotly. Now I would like update the plot by selecting a dropdown element in plotly. This should filter the source dataframe by values in one column. I am almost there but formatting of the axis is wrong:</p>
<p>May dataframe - aircraft for different airlines and dates...</p>
<pre><co... | <p>My solution:</p>
<p>Formatting of the X-Axis seems to be a version issue. With Plotly 5.6.0 it works just fine.</p>
<p>The Y-Axis was due to the wrong size of the array:
I changed it to match the x-values shape with numpy.tile:</p>
<pre><code>np.tile(df[df['airline'] == airline]['id'].T.values, (4,1))
</code></pre> | python|pandas|plotly | 0 |
371,704 | 71,580,184 | pandas mask indexing misshaped DataFrame | <p>Is there a built in way with pandas to accomplish this.</p>
<p>I'd prefer to avoid <code>pd.concat([...],1)</code> <code>.all(1)</code> methods as the dataset I'm working with has missing data points.</p>
<h2>main.py</h2>
<pre class="lang-py prettyprint-override"><code>import pandas as pd
import numpy as np
import n... | <p>I think you need <a href="https://pandas.pydata.org/docs/reference/api/pandas.Index.intersection.html" rel="nofollow noreferrer"><code>pd.Index.intersection</code></a>:</p>
<pre><code>x = frame_b.loc[frame_a.index.intersection(frame_b.index)]
</code></pre>
<p>Output:</p>
<pre><code>>>> x
0 1
C 6 7... | python|python-3.x|pandas|numpy | 1 |
371,705 | 71,572,976 | Why does open cv dilation only work downwards? | <p><strong>Initial Question:</strong></p>
<p>I thought the following code should lead to the initial white pixel expanding, but this does not happen. How can I make dilate work in all directions?</p>
<pre><code>img = np.zeros((11, 11), dtype='uint8')
img[5][5] = 255
dilated = cv2.dilate(img, (5, 5), iterations=3)
cv2... | <p>As stated by @beaker in the comment, the problem I was experiencing was that my kernel was incorrectly defined. The following code yields the desired result:</p>
<pre><code>img = np.zeros((11, 11), dtype='uint8')
img[5][5] = 255
kernel = np.ones((3, 3), dtype='uint8')
dilated = cv2.dilate(img, kernel, iterations=3... | python|numpy|opencv|image-processing|dilation | 0 |
371,706 | 71,654,582 | All binary combinations in a 2d Numpy | <p>I am trying to create a code that could generate all possible combinations of 0 and 1 in a numpy matrix excluding rotations.
What do I mean rotation? I mean that below matrix data are in fact the same because it has the same data but rotated 90º, 180º and 270º.</p>
<p><a href="https://i.stack.imgur.com/GqxDh.png" re... | <p>Here is a vectorized solution that can work for small matrix sizes; comments in the code illustrate a 3 by 3 case:</p>
<pre><code>def get_binary_mats(n):
# all possible n by n binary matrices up to rotation:
bin_mats = (np.bitwise_and(np.arange(2**(n*n))[:,None], 2 ** np.arange(n*n)) > 0)\
.reshape(-1, n... | python|numpy|combinations|permutation | 3 |
371,707 | 42,314,272 | imwrite merged image : writing image after adding alpha channel to it opencv python | <p>I want to change background of image and add alpha channel to it before saving it as a png file. </p>
<p><code>imshow</code> shows the image , but <code>imwrite</code> writes an empty image.
Dimension after merging is also correct, i.e. the merged image has <code>(x,y,4)</code> when I print <code>img_a.shape</code... | <p>The problem is with your alpha channel, The reason why the image is shown in <code>imshow</code> but not shown with <code>imwerite</code> lies in the face that <em>cv2.imshow() rejects the alpha channel whereas imwrite takes into account the alpha channel.</em></p>
<p>As per your code you are defining the alpha cha... | image|python-2.7|opencv|numpy | 2 |
371,708 | 42,262,764 | Pandas OLS - pulling params not working | <p>I have Pandas OLS lines of code that are working correctly, but am unable to pull the params to use in another related function:</p>
<pre><code>ES_15M_LR = pd.ols(y = ES_15M_Last_300_Periods['Close'], x = ES_15M_Last_300_Periods['Date'])
</code></pre>
<p>The above code works great, but when I try to pull params fr... | <p>First of all, it's highly recommended that you use statsmodels because...</p>
<blockquote>
<p><code>pandas.stats.ols</code>, <code>pandas.stats.plm</code> and <code>pandas.stats.var</code> routines are
deprecated and will be removed in a future version (<a href="https://github.com/pandas-dev/pandas/issues/6077"... | python|pandas|dataframe|linear-regression | 5 |
371,709 | 42,503,783 | Calculate various differences in days using DateOffSet using loops | <p>given a fame with the next five Saturdays:</p>
<pre><code>start_date =
0 04.03.2017
1 11.03.2017
2 18.03.2017
3 25.03.2017
4 01.04.2017
</code></pre>
<p>and a list which I use for calculating end_dates </p>
<pre><code>duration = [4, 8, 15, 22].
</code></pre>
<p>I would like to create a new frame w... | <p>The problem is not with <code>DateOffset()</code>. The problem is you do not specify the date format when you pass your date string to pandas.</p>
<p>So pandas is guessing the date format. What you should do is passing the <code>format</code> parameter when using <code>to_datetime()</code>.</p>
<pre><code>df
... | python|python-2.7|date|pandas|datetime | 0 |
371,710 | 42,276,811 | TensorFlow computation results slightly vary | <p>Since this <a href="https://stackoverflow.com/questions/39429313/why-do-tensorflow-tf-learn-classification-results-vary-a-lot">link</a> only describes the variety of predictions based on learning approaches, I want to find out of curiosity why TensorFlow computations <strong>slightly vary</strong>. </p>
<pre><code>... | <p>This is normal for 32-bit floating point values. Those 1.1, 2,2 and 3.3 values are not exactly represented in 32-bit floating point.</p>
<pre><code>import numpy as np
x = np.array([1.1, 2.2, 3.3], dtype=np.float32)
y = np.array([1, 1, 1], dtype=np.float32)
x+y
>>> array([ 2.0999999 , 3.20000005, 4.3000... | python|machine-learning|tensorflow | 1 |
371,711 | 42,498,358 | Dataframes, csv, and CNTK | <p>I have been playing around with CNTK and am finding that models can only be trained using numpy arrays. Is this correct?</p>
<p>This makes sense for image recognition etc. </p>
<p>How would I turn my tidy dataset (read in as a dataframe using pandas) into a format that can train a logistic regression with? I have ... | <p>CNTK 104 shows how to use pandas dataframes and numpy.</p>
<p><a href="https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb" rel="nofollow noreferrer">https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Nu... | pandas|dataframe|cntk | 1 |
371,712 | 42,188,816 | I get an TypeError in python3 when I use Pandas and sep function | <p>I try to manipulate a real big dataset in python using pandas. The code I am using is the following</p>
<pre><code>import numpy as np
import pandas as pd
from pandas import DataFrame
from pandas import Series
pd.set_option('display.max_columns', None)
df = pd.read_csv('Medicare.txt', 'r', sep='\t', na_values=['.'])... | <p>The second positional argument to read_csv is <code>sep</code>. For some reason you are passing an 'r' there, as well as an explicit <code>sep</code> kwarg.</p> | python|pandas | 1 |
371,713 | 42,434,788 | Efficient feeding of data for reinforcement learning algorithms | <p>I am currently implementing the deep double Q learning algorithm in TensorFlow. I have an experience replay buffer implemented based on NumPy arrays. However, some performance analysis shows that the feeding of the data from NumPy arrays to the graph using feed_dict is very inefficient. This is also pointed out in t... | <p>The short answer is <strong>there is no way to speed it up</strong> if your data is a minimum set and is unique. If your data has some redundancies or unneeded decoration that you can remove, then remove it prior to feeding it into your model, but assuming you've already done that and your data is unique in each ba... | tensorflow|reinforcement-learning | 0 |
371,714 | 42,281,851 | How to add padding to a plot in python? | <p>I am trying to add padding to the left and the right side of my plot.
But when I change xlim and ylim; the image becomes smaller.</p>
<p>what am I doing wrong?</p>
<pre><code>import matplotlib.pyplot as plt
plt.rcParams['text.usetex'] = False
from matplotlib.font_manager import FontProperties
import seaborn as sns... | <p>If your matplotlib figure is getting trimmed, you can use <code>Tight Layout</code>. Details provided <a href="http://matplotlib.org/users/tight_layout_guide.html" rel="noreferrer">here</a></p>
<p>In your code, try adding </p>
<pre><code>plt.tight_layout()
</code></pre>
<p>Another option that you can try is to us... | python|python-3.x|pandas|matplotlib|seaborn | 17 |
371,715 | 42,161,884 | Python: how to find all connected pixels if I know an origin pixel's position? | <p>I have a binary image: <code>numpy.ndarray(dtype=bool)</code>. It has a few hundreds of connected regions filled with <code>True</code> value.</p>
<p>But I'm interested only in one region. I know the posision of one of its elements and want to find out the bounding box of this region-of-interest (and maybe the posi... | <p>Depending on the size of your image it might be simplest to label the image to get <strong>all</strong> the connected components. Use the label of the known pixel to get the connected pixels as well. <code>skimage</code> makes this really simple using <a href="http://scikit-image.org/docs/dev/api/skimage.measure.htm... | python|numpy|image-processing | 5 |
371,716 | 42,553,919 | Matplotlib Scatter plot with numpy row index as marker | <p>I have a numpy array and i'm trying to plot it with a scatter plot using matplotlib.</p>
<pre><code>from matplotlib import pyplot as plt
from matplotlib import pylab as pl
pl.plot(matrix[:,0],matrix[:,1], 'ro')
</code></pre>
<p>This gives me something like :
<img src="https://i.stack.imgur.com/PgDs6.png" alt="pl... | <p>You can do this using <code>plt.text</code>:</p>
<pre><code>from matplotlib import pyplot as plt
import numpy as np
N = 100
matrix = np.random.rand(N,2)
plt.plot(matrix[:,0],matrix[:,1], 'ro', alpha = 0.5)
for i in range(matrix.shape[0]):
plt.text(matrix[i,0], matrix[i,1], str(i))
plt.show()
</code></pre>
<... | python|numpy|matplotlib | 14 |
371,717 | 42,493,978 | Returning corresponding row based on fuzzywuzzy ratio | <p>I'm using fuzzy wuzzy to compare two columns in two different dataframes. I'd like to retrieve a corresponding value in the same row but different column within df2. For example:</p>
<p>If i in df1 column A has a match ratio of more than 50 with df2 column A, I'd like to retrieve the corresponding value in df2 colu... | <p>Replace <code>while</code> with <code>if</code></p>
<p>You have already run the partial_ratio function and obtained your static result named <code>test</code>. if it is > 50 it will forever be > 50 in that part of your code causing infinite looping.</p> | python|pandas|fuzzy|fuzzywuzzy | 1 |
371,718 | 42,216,865 | Python Matplotlib x-axis improperly labels timedelta64 object | <p>I am trying to generate a plot from a Pandas dataframe in Python with Matplotlib. Here is a summary of the dataframe. </p>
<pre><code>import pandas as pd
import datetime
import matplotlib.pyplot as plt
# Summarize data frame.
>>> df.shape
(40, 4)
>>> df.dtypes
ID object
r... | <p>The units of your x-axis are nanoseconds, as shown in your output</p>
<pre><code>>>> df.dtypes
ID object
relative_time timedelta64[ns] <----- [ns] == nanoseconds
value float64
relative_value float64
dtype: object
</code></pre>
<p>Looks like ma... | python|pandas|matplotlib | 2 |
371,719 | 42,476,344 | Pandas: extract columns from multiple dataframes to a new dataframe based on common column name | <p>I have 4 datasets imported from Excel containing total_budget for schools for 2013, 2014, 2015 and 2016. All dataset have a common column with the ID code for each school (Column LAESTAB).</p>
<p>I want a new dataset with the common column LAESTAB (same values across the 4 datasets) on the left and the columns tota... | <p>Merge the dataframes SQL style using the LAESTAB column and then delete columns from <code>data_merged</code> as necessary.</p>
<pre><code>import pandas as pd
data_merged = pd.merge(cuts2016,cuts2015,on = "LAESTAB")
</code></pre>
<p>For more on merging you can check the following links:</p>
<p><a href="http://chr... | python|excel|pandas|dataframe | 0 |
371,720 | 42,166,629 | Python pandas 'Index' object has no attribute 'str' | <p>I'm trying to run this code, but it returns this error</p>
<pre><code>import pandas as pd
df = pd.read_csv('olympics.csv', index_col=0, skiprows=1)
for col in df.columns:
if col[:2]=='01':
df.rename(columns={col:'Gold'+col[4:]}, inplace=True)
if col[:2]=='02':
df.rename(columns={col:'Silve... | <p>It solves with (posted in a comment by @Shijo)</p>
<pre><code>df.index.to_series().str.split('\s\(')
</code></pre> | python|pandas | 2 |
371,721 | 42,563,895 | Python, solver method or optimization of current code? | <p>I am trying to add more data to the matrices to analyze and solve for the , but as it stands currently it is performing the brute operation, it exceeds python's limits if I add another column to the analysis. Is there a solver method availalbe that would find a similar result rather than having to brute through comb... | <p>This answer is treating the question more like <a href="http://codereview.stackexchange.com">codereview</a> then helping with algorithm.</p>
<p>First you can <a href="https://stackoverflow.com/questions/10080379/better-way-to-iterate-over-two-or-multiple-lists-at-once">iterate over all three <code>plines1</code> at... | python|numpy|matrix|iterator|solver | 0 |
371,722 | 42,375,680 | Tensorflow Count Objects in Image | <p>New to machine learning so looking for some direction how to get started. The end goal is to be able to train a model to count the number of objects in an image using Tensorflow. My initial focus will be to train the model to count one specific type of object. So lets say I take coins. I will only train the model to... | <p>Probably the best performing solution for the coin problem would be to use a regression to solve this. Annotate 5k images with the amount of objects in the scene and run your model on it. Then your model just outputs the correct number. (Hopefully)</p>
<p>Another way is to classify if an image shows a coin and use ... | machine-learning|tensorflow|computer-vision|artificial-intelligence|deep-learning | 5 |
371,723 | 42,144,915 | Convert TensorFlow string to python string | <p>I am aware that in TensorFlow, a tf.string tensor is basically a byte string. I need to do some operation with a filename which is stored in a queue using <a href="https://www.tensorflow.org/api_docs/python/tf/train/string_input_producer" rel="noreferrer">tf.train.string_input_producer()</a>.</p>
<p>A small snippet... | <p>In tensorflow 2.0.0, it can be done in the following way:</p>
<pre><code>import tensorflow as tf
my_str = tf.constant('Hello World')
my_str_npy = my_str.numpy()
print(my_str_npy)
type(my_str_npy)
</code></pre>
<p>This converts a string tensor into a string of 'bytes' class</p> | python|tensorflow | 6 |
371,724 | 42,579,407 | What does numpy's vectorize do? | <p>I have a function for cleaning text of a set stopwords:</p>
<pre><code>def clean_text(raw_text, stopwords_set):
# removing everything which is not a letter
letters_only = re.sub("[^a-zA-Z]", " ", raw_text)
# lower case + split --> list of words
words = letters_only.lower().split()
... | <p>I wondered how <code>vectorize</code> handled these inputs. It is designed to take array inputs, broadcast them against each other, and feed all elements, as scalars, to your function. In particular I wondered how it handled the <code>set</code>. </p>
<p>With your function, and a <code>print(stop_words)</code> ad... | pandas|numpy|vectorization|stop-words | 2 |
371,725 | 42,189,312 | pandas.nlargest() - Confused with repeated index values | <p>I have following sample DataFrame defined as following:</p>
<pre><code>df1 = pandas.DataFrame(data = {"Age":[28, 3, 32, 18], "Student":[False, True, False, False]}, index = ["Jack", "Sam", "Nate", "Jack"])
</code></pre>
<p>and the output is following.</p>
<p><a href="https://i.stack.imgur.com/awvJut.png" rel="nof... | <p>The issue is resolved. Pandas version I was using was 0.19.1 and after <a href="https://stackoverflow.com/questions/42189312/pandas-nlargest-confused-with-repeated-index-values#comment71541579_42189312">suggestion of @user35603</a> I updated it to 0.19.2 and re-executed the code and it works!</p>
<p>Thank you <a hr... | python|pandas|dataframe | 2 |
371,726 | 69,841,895 | Pivoting first level of a multilevel index to be the first level of a multilevel column | <p>I have a multilevel index dataframe like this:</p>
<pre><code>indx = [('location', 'a'), ('location', 'b'), ('location', 'c'), ('location2', 'a'), ('location2', 'b'), ('location2', 'c')]
indx = pd.MultiIndex.from_tuples(indx)
col = ['S1','S2','S3']
df = pd.DataFrame(np.random.randn(6, 3), index=indx, columns=col)
d... | <p>You want <code>unstack</code>:</p>
<pre><code>df.unstack(level=0).swaplevel(0,1, axis=1).sort_index(axis=1)
</code></pre>
<p>Output:</p>
<pre><code> location location2
S1 S2 S3 S1 S2 S3
a 0.022553 0.485896 -0.421144 1.836187 -0.... | python|pandas|indexing|pivot|multi-level | 2 |
371,727 | 69,805,243 | How to assign variable to a function's return object and ignore displays | <p>Currently my function is something like this</p>
<pre><code>def create_dataframe():
df1 = pd.DataFrame(x)
display(df1)
df2 = pd.DataFrame(x_2)
display(df2)
df3 = pd.DataFrame(x_3)
return df3
</code></pre>
<p>I want to later on in a future cell set a variable to equal only df3, but if I run th... | <p>It is tricky and error prone to cleanly determine whether a variable exists. The most explicit would be to use a flag:</p>
<pre><code>def create_dataframe(show=False):
if show:
df1 = pd.DataFrame(x)
display(df1)
df2 = pd.DataFrame(x_2)
display(df2)
df3 = pd.DataFrame(x_3)
... | python|pandas|dataframe | 1 |
371,728 | 69,692,906 | Fill rows of an (10000,2) array using another array's values as indices | <p>I have an array, idnl, that contains non-consecutive numbers in order from 0-10000</p>
<pre><code>u = rng.integers(0, 100, size = N)/100.0
temp = (np.cumsum(priors))
thresholds = [temp[0], temp[1],1]
indl = (np.argwhere(u <= thresholds[l]))
Nl = np.size(indl)
</code></pre>
<p>Array x is [10000, 2] filled with zer... | <p>Take a look at numpy.reshape></p>
<p>I would use</p>
<pre><code>np.reshape(x, (-1:2))
</code></pre>
<p>this will create a 2d list.</p>
<pre><code>[[0,0], [0,0], [0.53, 0.98] ....]
</code></pre>
<p>you can then use a for loop to loop through the list.</p>
<pre><code>for arr_element in x:
print(arr_element[0], ... | python|arrays|numpy|matlab|data-science | 0 |
371,729 | 69,882,464 | How to extract a new array after skipping a certain number of items in the array repeatly using numpy | <pre><code>num_pixels_per_cell_one_axis = 4
num_cells_per_module_one_axis = 4
inter_cell_sep = 2
max_items_in_list = num_cells_per_module_one_axis * num_pixels_per_cell_one_axis + (num_cells_per_module_one_axis-1) * inter_cell_sep
print(max_items_in_list)
indices_to_retain = list(range(max_items_in_list))
indices_to... | <p>IIUC you want to keep 4 items, then skip 2?</p>
<p>You could use:</p>
<pre><code>keep, skip = 4,2
indices_to_retain = [i for i in range(max_items_in_list) if i%(skip+keep)<keep]
</code></pre>
<p>output:</p>
<pre><code>>>> indices_to_retain
[0, 1, 2, 3, 6, 7, 8, 9, 12, 13, 14, 15, 18, 19, 20, 21]
</code><... | python|list|numpy|slice|numpy-slicing | 0 |
371,730 | 69,918,003 | Extracting values from existing dataset | <p>I need to gather information from an existing dataset.
The dataset looks as follows:</p>
<pre><code>Source Target Label_S Weight Prop_1 Prop_2 Mer_1 Mer_2
car airplane 0.5 0.2 1 0 0 0
car train 0.5 0.5 1 1 0 1
car ... | <p>I'm not sure I understand correctly but you can first create two distinct dataframes for source and target and aggregate the properties in lists:</p>
<pre><code>df_s = df[["Source", "Label_S", "Prop_1", "Mer_1"]].groupby("Source").agg(list)
df_t = df[["Target&qu... | python|pandas | 1 |
371,731 | 69,678,967 | Is there a way to set the indices of a multi index DataFrame? | <p>I am attempting to create a multi index dataframe which contains every possible index even ones where it does not currently contain values. I wish to set these non-existent values to 0. To achieve this, I used the following:</p>
<pre><code>index_levels = ['Channel', 'Duration', 'Designation', 'Manufacturing Class']
... | <p>You should change the reindex part, as <code>pd.MultiIndex.from_product()</code> should take as input the original dataframe indexes (by giving <code>grouped_df.index.levels</code> as input you pass only the indexes resulting after the groupby).</p>
<p>This is a solution that wuold work:</p>
<pre><code>full_idx = [d... | python|pandas|multi-index | 0 |
371,732 | 69,917,744 | Get row of maximum value in pandas.groupBy without apply in dataframe with DatetimeIndex | <p>I have a <code>pandas.Dataframe</code> with a <code>DatetimeIndex</code>. The data are sampled every 30 minutes (can be different). I want to resample the data every hour and, for each group, I want to extract the row with the highest value on a specific column.</p>
<p>E.g.</p>
<pre class="lang-none prettyprint-over... | <pre><code>df.groupby(df.index.ceil(freq="H")).apply(lambda x: x.loc[x.WindDir.idxmax(), :])
# WindDir WindSpeed Temperature CloudHgt
# Date
# 2020-01-01 01:00:00 150.0 3.6 5.0 213.0
# 2020-01-01 02:00:00 ... | python|pandas|pandas-groupby | 0 |
371,733 | 69,975,335 | give more importance to smaller value in Numpy weightage average | <p>I am trying to weight average the output of different regression models. I have mean absolute error of each model. While averaging i want to give more weight to lower mae model.
The following code give more importance to higher mae value.</p>
<pre><code>weights_mae=[1.640,1.675,1.514,1.563,1.667]
mean=np.average(val... | <p>Simplest idea - make inverse of mae weights, small weights will become large and vice versa.</p>
<pre><code>weights_mae=[1.640,1.675,1.514,1.563,1.667]
mean=np.average(val_list,axis=0, weights= 1 / weights_mae)
</code></pre> | python|numpy | 0 |
371,734 | 69,852,530 | Adding a calculated metric in multiindex pandas dataframe | <p>I have a <code>df</code>:</p>
<pre><code>date category subcategory order_id product_id
2021-05-04 A aa 10 5
2021-06-04 A dd 10 2
2021-05-06 B aa ... | <p>Remove list after <code>groupby</code>, then add new column with division by <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.assign.html" rel="nofollow noreferrer"><code>DataFrame.assign</code></a> and last reshape with <code>unstack</code> - if necessary sorting pre datetimes:</p... | python|pandas|group-by | 1 |
371,735 | 69,789,418 | How to compare two identically sized images in python, replacing pixels that match between the two images with black pixels | <p>I have two images for example</p>
<pre><code>import numpy as np
img1 = np.array([[[1,1,1],[2,2,2]],[[3,3,3],[4,4,4]]])
img2 = np.array([[[1,1,1],[1,1,1]],[[3,3,3],[1,1,1]]])
</code></pre>
<p>I'd like to compare the two and, where the pixels are matching, and where they don't match, use the pixels from img1, and whe... | <p>Use <code>.all(-1)</code> on <code>img1==img2</code> to check for equality on all channels. Then <code>np.where</code> with broadcasting:</p>
<pre><code>out = np.where((img1==img2).all(axis=-1)[...,None], (0,0,0), img1)
</code></pre>
<p><strong>Or</strong>, since you are masking with <code>(0,0,0)</code>, you can us... | python|image|numpy|opencv|python-imaging-library | 1 |
371,736 | 69,764,808 | Duplicate Rows in a Pandas DataFrame and replacing values by multiple other values | <p>First of all, hi everyone! This is the first time I am actually posting a question on StackOverflow, so if I am too specific / too general, I would appreciate receiving advise :).</p>
<p>I have a Pandas DataFrame containing some SAP Authorization Data, in which a column can contain something like "placeholder v... | <p>If possible use outer join by column <code>LOW</code> from <code>df1</code> with <code>ROLE</code> first copy <code>LOW</code> to <code>VARBL</code> in <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.assign.html" rel="nofollow noreferrer"><code>DataFrame.assign</code></a>, then re... | python|pandas|dataframe | 0 |
371,737 | 69,805,981 | making a multi index data frame from list of data sets | <p>I'm trying to window my data frame so I made a for loop as the code below:</p>
<pre><code>m=6
p=0
Window=[]
for i in list2:
l=df3S.iloc[i:i+k,:]
j=df3S.iloc[i+m:i+(3*m),:]
Window.append(l)
Window.append(j)
i+=k
</code></pre>
<p>so I have a list of data sets right now but I need a multi-index data... | <p>Use:</p>
<pre><code>df = pd.concat(Window, ignore_index=True)
</code></pre> | python|pandas|dataframe|for-loop | 0 |
371,738 | 69,956,017 | How do I set a calculated column in apps script similar to pandas? | <p>I am trying to set a new calculated column based on values of two other columns. The condition is: whenever I insert new data in columns C and D, the column E should be calculated as "=C/D".</p>
<p>This would be the equivalent of this in pandas:</p>
<pre><code>df['new']= df['C']/df['D']
</code></pre>
<p>bu... | <p>You can <code>setFormula</code> over the entire column like this:</p>
<pre><code>var range=sheet.getRange('E2:E20000')
range.setFormula("C2/D2")
</code></pre>
<p>This is similar to setting conditional formatting. The top left of the range is all that matters. Everything else is relative. See <a href="https... | javascript|pandas|google-apps-script|google-sheets | 1 |
371,739 | 69,941,156 | The `GLIBC_2.29 not found` problem of the installation of transformers? | <p>To run transformers I installed it on CentOS 8 by</p>
<pre><code>conda install -c conda-forge transformers=4.12.2
</code></pre>
<p>following the method on <a href="https://discuss.huggingface.co/t/problem-installing-using-conda/5518/2" rel="nofollow noreferrer">this page</a>, but I still encountered the same error:<... | <p>I had the same issues, and I downgraded to the following version:</p>
<pre><code>tokenizers=0.10.1
transformers=4.6.1
</code></pre> | python|deep-learning|nlp|glibc|huggingface-transformers | 1 |
371,740 | 70,000,931 | Add column in pandas vectorized | <p>i have a Term-Frequency matrix saved as a pandas dataframe.</p>
<pre><code> 1000 Merkwürdig Mindestens Error ... Periode bildet 30 Button
0 0 0 0 0 ... 0 0 0 0
1 0 1 0 2 ... 0 0 0 0
2 0 ... | <p>You can use <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sum.html?highlight=sum#pandas.DataFrame.sum" rel="nofollow noreferrer"><code>.sum</code></a> method</p>
<pre><code>df['count'] = df.sum(axis=1)
</code></pre> | python|pandas|dataframe|tf-idf | 1 |
371,741 | 69,673,427 | How to check value in a series with any? | <p>I'm working with Pandas. I need to create a new column in a dataframe according to conditions in other columns. I try to look for each value in a series if it contains a value (a condition to return text).This works when the values are exactly the same but not when the value is only a part of the value of the series... | <p>Use <a href="https://numpy.org/doc/stable/reference/generated/numpy.where.html" rel="nofollow noreferrer"><code>numpy.where</code></a> with <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.contains.html" rel="nofollow noreferrer"><code>Series.str.contains</code></a>:</p>
<pre><cod... | python|pandas|dataframe|if-statement|any | 2 |
371,742 | 69,773,871 | Calculating curvature with numpy, sharp incorrect jumps in curvature at beginning and end of curve? | <p>I'm attempting to calculate curvature values using Numpy, and for the most part using the standard math seems to work well. However, I seem to be running into an issue with the ends of my curve having their curvature calculated incorrectly. Here's an example of what I mean below:</p>
<p>The curve:</p>
<p><a href="ht... | <p>You can always expect this kind of artifacts.</p>
<p>If you calculate the gradient along a vector using the difference between neighbors, you estimate the gradient in each interval, and the result necessarily has one value less then the original vector:</p>
<pre><code>>>> v = np.array([2,1,3,3,4,5,3,4], dty... | python|numpy | 2 |
371,743 | 69,957,788 | How can I set the points outside of the certain range to 0 | <p>I have interpolated a plot and extended the x and y which gave me the following contour plot. How can I rearrange the Z matrix so that the points outside the red box are 0.</p>
<pre><code>x=data.columns.astype('float64')
y=data.index.to_numpy()
z=data.to_numpy()
X,Y = np.meshgrid(x, y)
ratio = 4
x_new = x*ratio
y_... | <p>Here is a minimal example, which should be easy to adapt to your case.
The <code>np.where</code> function is your friend here:</p>
<pre><code>import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-15, 15, 301)
y = np.linspace(-15, 5, 201)
X_new, Y_new = np.meshgrid(x, y)
znew = np.random.randn(*X_new.... | python|numpy|matplotlib|scipy|interpolation | 1 |
371,744 | 69,759,263 | python Pandas: VLOOKUP multiple cells on column | <p>I'm struggling with next task: I would like to identify using pandas (or any other tool on python) if any of multiple cells (Fruit 1 through Fruit 3) in each row from Table 2 contains in column Fruits of Table1. And at the end obtain "Contains Fruits Table 2?" table.</p>
<div class="s-table-container">
<ta... | <p>Try:</p>
<ol>
<li><code>filter</code> DataFrame to include columns that contain the word "Fruit"</li>
<li>Use <code>isin</code> to check if the values are in <code>table1["Fruits"]</code></li>
<li>Return True if <code>any</code> of fruits are found</li>
<li><code>map</code> True/False to "Ye... | python|pandas | 1 |
371,745 | 69,865,732 | problem using convolutional autoencoder for 2d data | <p>I want to train an autoencoder for the purpose of gpr investigations.
The input data dimension is 149x8.However, While i am trying deep autoencoder it works fine</p>
<pre><code>input_img = Input(shape=(8,))
encoded1 = Dense(8, activation='relu')(input_img)
encoded2 = Dense(4, activation='relu')(encoded1)
encoded3... | <p>Wrong Input Shape:</p>
<p>This is because we are passing the input shape of (8,) and 1 extra dimension added by TensorFlow for Batch size, so the error message says that it found ndim=3, but the CNN has expected min_ndim=4, 3 for the image size and 1 for the batch size. e.g.</p>
<pre><code>input_shape=(number_of_row... | tensorflow|keras|deep-learning|autoencoder|encoder | 0 |
371,746 | 69,717,566 | How do I get the count per category aggregated by month using python's pandas? | <p>I have a dataset that looks like this (link for sample data is below)</p>
<p>I was hoping to get the count of each 'frame' per type and per month. So it would give this kind of result: I tried it with groupby and value_counts and can only do it with one layer such as origin_type and frame only</p> | <p>Can you try this:</p>
<pre><code>df = pd.read_excel("Sample_Data.xlsx")
df.Publication_Date = pd.to_datetime(df.Publication_Date)
df['yr'] = df.Publication_Date.dt.year
df['mon'] = df.Publication_Date.dt.month
df.groupby(['yr', 'mon', 'Origin_Type', 'frames'])['frames'].count()
</code></pre> | python|pandas|aggregate | 3 |
371,747 | 69,874,872 | How to cut data from one column and paste into a new column in Python -> Pandas? | <p>For example, I have the DataFrame:</p>
<pre><code>import pandas as pd
a = [{'name': 'RealMadrid_RT'}, {'name': 'Bavaria_FD'}, {'name': 'Lion_NS'}]
df = pd.DataFrame(a)
</code></pre>
<p>I need to create new column -> df['name_2'], next, cut the data from column df['name'] and paste to column df['name_2']. I requi... | <p>If you don't want the underscore in the second column, this will work:</p>
<pre class="lang-py prettyprint-override"><code>df = df['name'].str.split('_', expand=True)
df.columns = ['name', 'name2']
</code></pre>
<p>If you <em>do</em> want the underscore in the second column, this will work for that:</p>
<pre class="... | python|pandas|dataframe | 0 |
371,748 | 70,012,176 | Is there any way to use the Teachable Machine pose project on ml5？ | <p>I trained a pose model on teachablemachine.with google.com. I tried to use
that model as a web-application, so I applied it to the ml5.js on JavaScript like below.</p>
<pre class="lang-js prettyprint-override"><code>
const imageModelURL = 'teachable machine model URL';
async function start() {
const stream = awai... | <p>At this moment, we can not use the teachable machine's pose project on ml5. If you want, you can use Image Project instead.</p> | javascript|tensorflow|web-applications|codepen|ml5.js | 0 |
371,749 | 69,783,161 | Converting Pandas Datetime to Postgres Date | <p>I am working with Pandas to add a field that is a string to a date and add another field that would be the date of yesterday.</p>
<p>I am using <code>pytz</code> to set the timezone on the <code>stat_date</code> field, but I am still getting an error on the <code>to_sql</code> commmand.</p>
<pre><code>tz = pytz.time... | <p>add <code>format='%Y-%m-%d'</code> to <code>df.insert</code></p>
<pre><code>df.insert(1, 'feed_date',format='%Y-%m-%d' ,pd.to_datetime(df['feed_id'],
errors='coerce'))
df.insert(1, 'stat_date', format='%Y-%m-
%d',pd.to_datetime(datetime.now(tz).date()) - timedelta(days=1))
</code></pre> | python|pandas|postgresql | 0 |
371,750 | 69,915,944 | How to create a stacked bar plot from a wide dataframe | <p>can you help me figure out what is wrong with this code? I am getting the same error message " "ufunc 'add' did not contain a loop with signature matching types (dtype('<U32'), dtype('<U32')) -> dtype('<U32')""</p>
<pre class="lang-py prettyprint-override"><code>import numpy as np
imp... | <h2>Pandas Stacked Bars</h2>
<ul>
<li>Use <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html" rel="nofollow noreferrer"><code>pandas.DataFrame.plot</code></a> with <code>kind='bar'</code> and <code>stacked=True</code></li>
<li>This reduces the implementation from 26 to 9 lines of code</li>... | python|pandas|matplotlib|bar-chart | 1 |
371,751 | 70,003,990 | Melt pandas dataframe based on condition | <p>I have a dataframe with the following format</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th>timestamp</th>
<th>ID</th>
<th>Col1</th>
<th>Col2</th>
<th>Col3</th>
<th>Col4</th>
<th>UsefulCol</th>
</tr>
</thead>
<tbody>
<tr>
<td>16/11/2021</td>
<td>1</td>
<td>0.2</td>
<td></td>
<td>0.1</td... | <p>Try making a column with the useful values first:</p>
<p><code>df['Value'] = df.apply(lambda x: x[x.UsefulCol], axis=1)</code></p>
<pre><code>timestamp ID Col1 Col2 Col3 Col4 UsefulCol Value
16/11/2021 1 0.2 0.1 Col3 0.1
17/11/2021 1 0.3 0.8 ... | python|pandas|dataframe|melt | 1 |
371,752 | 69,962,726 | Assigning a value on remaining blanks in a category without impacting others | <p>I am trying to assign the remaining location value in <code>apple</code> category into <code>others</code>, but I don't want the location for <code>banana</code> and <code>waterloon</code> to get touched through the assignment. This means that I cannot just convert all the blanks to <code>others</code>. What's the s... | <p><code>DataFrame.loc</code> with the good condition :</p>
<ul>
<li>fruit is <code>apple</code></li>
<li><code>location</code> is empty</li>
</ul>
<pre><code>df.loc[(df['fruit_tag'] == 'apple') & (df['location'] == ""), 'location'] = 'others'
</code></pre> | python|pandas | 1 |
371,753 | 69,674,706 | Maximum value in a given group, increasing row by row | <p>The goal is to put the current highest digit in the new column increasing row by row in a given group of letters. The expected, correct value, as a result formula, was entered by me manually in the column "col_ok". The only thing I have achieved so far is assigning the highest value to a given group and th... | <p>IIUC use:</p>
<pre><code>df = df.assign(cumulatively = df.groupby('group_letter')['col_ok'].cummax())
</code></pre> | python|pandas | 0 |
371,754 | 69,702,511 | Attribute error: 'numpy.ndarray' object has no attribute 'value_counts' | <p>I am getting the following attribute error when I tried to rebalance the biased data:</p>
<pre class="lang-none prettyprint-override"><code>'numpy.ndarray' object has no attribute 'value_counts';
</code></pre>
<p>it seems that the line <code>y.value_counts()</code> gives the attribute error</p>
<p><strong>code:</st... | <p>An <code>ndarray</code> has no such attribute. Also, attributes don't need the <code>()</code>.</p> | python|pandas|imblearn | 0 |
371,755 | 69,978,101 | How to merge 5 columns of a dataframe into one long column of new dataframe? | <p>I have a dataframe, <code>BaseResult</code> that contains 41 years of daily temperature data (41 rows x 365 columns) where rows represent years and columns represent days. I want to merge every 5 columns centered on each day into one long column so that I have a new dataframe, <code>RollingPercentile</code> that is ... | <p>use windows to perform your calculation</p>
<p>rolling percentiles aggregate over multiple rows of data to get a value.</p>
<pre><code>df.rolling(window=window, min_periods=min_periods).quantile(perc)
df.rolling(window=window, min_periods=min_periods).mean()
df.rolling(window=window, min_periods=min_periods).std()... | python|pandas|dataframe|numpy|weather | 0 |
371,756 | 69,841,193 | ValueError: Error when checking input: expected dense_input to have 2 dimensions, but got array with shape (1, 1, 2) | <p>Edit: Problem Solved. Solution below.</p>
<p>Attempting to build a RL model to handle a task.
There are two inputs: x and y, both are measured on an int scale of 1 to 100.
Based on these two inputs there should be an output (action to take on, discrete(5)) and confidence.</p>
<p>Also, I'm very new to this territory.... | <p>Your model expects a 2D input but you defined it as 1D. Here is a working example:</p>
<pre class="lang-py prettyprint-override"><code>from abc import ABC
import gym
from tensorflow import keras
from gym import Env
from gym.spaces import Discrete, Box
import random
import numpy as np
from tensorflow.keras.models imp... | python|tensorflow|keras|neural-network|openai-gym | 0 |
371,757 | 69,994,348 | Extract Rows with Year(s) Specific in Pandas DF | <p>I have a df "cdata" that is (4743816,7) in shape and looks like this:</p>
<pre><code> plant_name business_name maint_region_name wind_speed_ms \
0 RIO DO FOGO BRAZIL BRAZIL 8.72
1 RIO DO FOGO BRAZIL BRAZIL 8.66
2 RIO DO FOGO BRAZIL ... | <p>You can do this:</p>
<pre><code>import datetime
curr_year = datetime.datetime.now().year
df1 = cdata[cdata['mos_time'].dt.year.eq(curr_year)]
df2 = cdata[cdata['mos_time'].dt.year.ne(curr_year)]
</code></pre> | python|pandas|isin | 1 |
371,758 | 69,905,044 | Don't understand why these two Python functions give different results | <p>I have these two functions here giving different results, but I do not understand why, and assuming the second one is the correct one for Parkinson volatility, how to modify the first one to obtain the same results for the second one. This is the original formula:
<a href="https://derivvaluation.medium.com/parkinson... | <p>It seems that you are using <code>price</code> in <code>Parkinson1</code>.</p>
<p>While you're using <code>price_data</code> in <code>Parkinson2</code>.</p> | python|pandas | 0 |
371,759 | 69,995,874 | pandas dataframe from nested dictionary with uneven depth | <p>I have a dictionary of the following form:</p>
<pre><code>{'x': 1, 'y': 2, 'z':{'a': 3, 'b': 4}, 'w': {'a': 5, 'b': 6}}
</code></pre>
<p>I want to create a df like this:</p>
<p><a href="https://i.stack.imgur.com/BOODx.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/BOODx.png" alt="enter image desc... | <p>If you have only 2 levels:</p>
<pre><code>import pandas as pd
d = {'x': 1, 'y': 2, 'z':{'a': 3, 'b': 4}, 'w': {'a': 5, 'b': 6}}
data = {}
for k1, v1 in d.items():
if isinstance(v1, dict):
for k2, v2 in v1.items():
data[(k1, k2)] = v2
else:
data[(k1, '')] = v1
df = pd.DataFrame(... | python|pandas|dataframe | 2 |
371,760 | 69,676,661 | Annoying yfinance error and incompatibility with numpy/pandas | <p>I have the latest numpy and pandas installed and yfinance too, since I just installed them recently. (like a couple of days ago)
Now right now I am getting the error:</p>
<p>TypeError: Cannot interpret '<attribute 'dtype' of 'numpy.generic' objects>' as a data type</p>
<p>My Code literally is:</p>
<pre><code> ... | <p>For this versions:</p>
<pre><code>import pandas as pd
import numpy as np
import yfinance as yf
print(pd.__version__)
# Output: 1.3.3
print(np.__version__)
# Output: 1.21.2
print(yf.__version__)
# Output: 0.1.63
</code></pre>
<p>The code below works:</p>
<pre><code>stock = yf.Ticker('MSFT')
print(stock.actions)
#... | python|numpy|yfinance | 1 |
371,761 | 69,854,533 | Tensorflow on M1 | <p>Importing Tensorflow gives this error</p>
<p>This is my "code":</p>
<pre><code>import tensorflow as tf
</code></pre>
<p>My versions:
protoc --version</p>
<blockquote>
<p>libprotoc 3.15.8</p>
</blockquote>
<p>pip show protobuf</p>
<blockquote>
<p>Name: protobuf
Version: 3.19.1</p>
</blockquote>
<p>Tensorflo... | <p>You can now use conda to get tensorflow to work natively. Follow the sequence below</p>
<p>Create Conda environment for osx-arm64</p>
<pre><code>CONDA_SUBDIR=osx-arm64 conda create -n <env name> python=3.8 -c conda-forge
</code></pre>
<p>You can also use 3.9 which are the two currently supported versions</p>
<... | python|tensorflow | 0 |
371,762 | 69,872,024 | Understanding principal component analysis with k-means clustering | <p>I have a visualization that plot k means clusters with Principal Component Analysis (PCA) values.
From my understanding. PCA is an algorithm to reduce the large set of data to smaller one so that we can visualize better.</p>
<p>Can i interprete my visualization as most of the data are similar to each other which is ... | <p>PCA projects the initial feature space into a lower dimensional one. Be careful though, after applying PCA the features are not the initial ones, but the eigenvectors. That is why PCA falls into the category of <em>Feature Transformation</em>.</p>
<p>Concerning your question, it seems that taking the first two eigen... | python|pandas|k-means|pca | 0 |
371,763 | 69,746,797 | Numpy: Create new array from 1d integer array where each element in integer array represents the number of elements in the new array | <p>This seems like it should be straightforward, but I'm stumped (also fairly new to numpy.)</p>
<p>I have a 1d array of integers <em>a</em>.</p>
<p>I want to generate a new 1d array <em>b</em> such that:</p>
<ul>
<li>the number of elements in <em>b</em> equals the sum of the elements in <em>a</em></li>
<li>The values ... | <p>I think a pretty clear way to do this is</p>
<pre><code>import numpy as np
a = np.array([2,3,3,4])
constant = 120
#numpy.repeat(x,t) repeats the val x t times you can use x and t as vectors of same len
b = np.repeat(constant/a , a)
</code></pre> | python|arrays|numpy | 3 |
371,764 | 69,859,226 | More efficient way to rank columns in a dataframe | <p>Currently I have a dataframe that I rank the values of each column and output them into a new dataframe. Example code below:</p>
<pre><code>df = pd.DataFrame(np.random.randint(0, 500, size=(500, 1000)), columns=list(range(0, 1000)))
ranking = pd.DataFrame(range(0, 500), columns=['Lineup'])
ranking = pd.concat([ran... | <p>You can use the <strong>Numba</strong> JIT to compute this more efficiently and in parallel. The idea is to compute the rank of each column in parallel. Here is the resulting code:</p>
<pre class="lang-py prettyprint-override"><code># Equivalent of df.rank(ascending=False, method='min')
@nb.njit('int32[:,:](int32[:,... | python|pandas|dataframe|performance|rank | 1 |
371,765 | 69,988,440 | Moving dummy counts from each row to a single row | <p>I am trying this problem but not getting the right solution.</p>
<p>So, I have a data which has City and Months mapped to them</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th>City</th>
<th>Month</th>
</tr>
</thead>
<tbody>
<tr>
<td>A</td>
<td>M1</td>
</tr>
<tr>
<td>A</td>
<td>M2</td>
</t... | <p>I think this can be solved by using <code>pivot_table()</code>. The trick here is to use <code>len</code> as aggfunc.</p>
<pre><code>df.pivot_table(index='City',columns='Month',aggfunc=len,fill_value=0).clip(1,0)
</code></pre>
<p>Outputs:</p>
<pre><code>Month M1 M2 M3 M4 M5 M8
City
A ... | python|pandas|dataframe|pivot|rows | 0 |
371,766 | 69,995,419 | Cleaning Large Volume Of String Values | <p>I am working on a project where I have a large list of raw retailer tenant names with some basic stats like Total Gross Leasing Area (GLA). But there are variations in some of the tenant names so a clean up exercise is required. I know I have seen different solutions online with text comparison using levensthein. I ... | <p>Use multiple lines of the numpy.where function.
E.g. for Macy's and assuming your dataframe is called df</p>
<pre><code> df["tenant_1_cleaned"] = np.where(df["tenant_1"].str.startswith("MAC"),"MACYS", df["tenant_1"])
df["tenant_2_cleaned"] = np.where... | python|pandas | 0 |
371,767 | 69,938,406 | Multiple qrs were generated with the list, but I want to save them with the same name as the list | <pre><code>import pandas as pd
import qrcode
df = pd.read_excel("Manpower.xls")
df = df.drop(range(31,389))
lista = df.values.tolist()
for row in lista:
qr =qrcode.QRCode(version=1,box_size=2)
qr.add_data(row)
qr.make(fit=True)
img = qr.make_image()
img.save(lista+".png")
</code></... | <p>You are saving all your files with the same name, this is your problem. If you are okay with the name of the QR code having the name of the information in the QR code, then you could simply change the name of each file to the <code>row</code> in your loop:</p>
<pre><code>import pandas as pd
import qrcode
df = pd.re... | python|pandas|qr-code | 0 |
371,768 | 70,003,227 | Generate random cities based on probabilities | <p>I have two tables, one with country, city and the probability a user can be from that city, and another table with users and their countries:</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th>country</th>
<th>city</th>
<th>probability</th>
</tr>
</thead>
<tbody>
<tr>
<td>USA</td>
<td>New Y... | <p>You can use <a href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.choice.html" rel="nofollow noreferrer"><code>np.random.choice</code></a> to generate a random sample according to a given probability distribution:</p>
<pre class="lang-py prettyprint-override"><code>
In [27]: df = pd.DataFrame... | python|pandas | 2 |
371,769 | 69,680,095 | Iterating over dataframes and adding items from a list | <p>Quite new to python for data analysis, still a noob.</p>
<p>I have a list of pandas data frames (+100) who's variables are saved into a list.</p>
<p>I then have the variables saved in another list in string format to add into the dataFrames as an identifier when plotting.</p>
<p>I have defined a function to prepare ... | <pre><code>df = [df1, df2, df3]
strings = ['df1', 'df2', 'df3']
for s, d in zip(strings, df):
d['Strings'] = s
</code></pre> | python|pandas|list|dataframe | 1 |
371,770 | 69,779,512 | How do I make a GitHub into a pandas DataFrame? | <p>How do I import this <a href="https://github.com/pandas-dev/pandas/blob/master/doc/data/titanic.csv" rel="nofollow noreferrer">CSV file</a> into my JupyterLab DataFrame?</p>
<p>I want it to be transferred into a new DataFrame named Titanic I have done a Google search, as well as looked into the:
<a href="https://pan... | <p>Simply:</p>
<pre><code>import pandas as pd
dataset = pd.read_csv("https://raw.githubusercontent.com/pandas-dev/pandas/master/doc/data/titanic.csv", sep=",")
</code></pre>
<p>which gives:</p>
<pre><code>PassengerId Survived Pclass \
0 1 0 3
1 2 ... | python|pandas|jupyter-lab | 0 |
371,771 | 69,682,280 | How to find the max, min value of ALL Dataframe [ not values by column neither rows ] | <p>I know how to find in a Dataframe the max and minimum value of a column:</p>
<pre><code>df.min()
df.max()
</code></pre>
<p>If I want to find the min and max values by row is:</p>
<pre><code>df.max(index=1)
df.max(index=1)
</code></pre>
<p>But this return a list with the respective row or column value. I want to find... | <p>You can try:</p>
<pre><code>max_val = df.max().max()
</code></pre> | python|pandas|dataframe|max|min | 0 |
371,772 | 70,008,413 | Reshaping a PyTorch tensor to 3 dimensions when it is originally 2 dimensions? | <p>I would like to take a PyTorch tensor that I have, originally of shape <code>torch.Size([15000, 23])</code> and reshape it such that it is compatible to run in spiking neural network (<code>snnTorch</code> is the framework I am using in PyTorch). The shape of the tensor to input into the SNN should <code>[time x bat... | <p>The thing with SNNs is that they are time-varying, so if your data is time-static, then your options are either to:</p>
<ol>
<li>pass the same sample at every time step to the network, or</li>
<li>convert it into a spike-train before passing it in.</li>
</ol>
<p>You appear to be going for (2), although (1) might be ... | python|pytorch|reshape|tensor | 2 |
371,773 | 69,900,672 | Conversion issue for Spark dataframe to pandas | <p>I am trying to convert a spark data frame to pandas and there is a error I am encountering with:</p>
<pre><code> databricks/spark/python/pyspark/sql/pandas/conversion.py:145: UserWarning: toPandas attempted
Arrow optimization because 'spark.sql.execution.arrow.pyspark.enabled' is set to true, but has
rea... | <p>In the traceback it says:</p>
<pre><code>Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage
43.0 failed 1 times, most recent failure: Lost task 0.0 in stage 43.0 (TID 97) (ip-10-172-188-
62.us-west-2.compute.internal executor driver): java.lang.OutOfMemoryError: J... | python|pandas|apache-spark|pyspark | 2 |
371,774 | 69,726,086 | How to hide index in Pandas dataframe to JSON function DataFrame.to_json() using 'columns' orientation | <p>Eg. My results are returned in this format</p>
<pre><code> "name": {
"1": "bill",
"2": "mike",
"3": "dave"
},
"age": {
"1": 20,
"2": 21,
"3": 40
},
</code><... | <p>The Pandas function <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_dict.html" rel="nofollow noreferrer"><code>DataFrame.to_dict()</code></a> has an option <code>orient='list'</code> that outputs the layout you want.</p>
<p>The only difference is that it outputs a dict with si... | python|pandas|dataframe | 1 |
371,775 | 69,874,436 | PyInstaller problem making exe files that using transformers and PyQt5 library | <p>So I'm working on an AI project using huggingface library, and I need to convert it into an exe file. I'm using PyQt5 for the interface, and transformers and datasets library from huggingface. I tried using PyInstaller to convert it into an exe file, it does finish building the exe files of the project, but it gives... | <p>First, <code>pip install tqdm</code> if you haven't already. Second, specify the path to your <code>Lib/site-packages</code>. You can do this by either:</p>
<ol>
<li>Adding an argument to <code>pathex</code> in your <code>.spec</code> file
(<code>.venv</code> for a virtual environment at some folder <code>.venv</cod... | python|pyqt|pyside|huggingface-transformers | 1 |
371,776 | 69,905,936 | using groupby for datetime values in pandas | <p>I'm using this code in order to groupby my data by year
df = pd.read_csv('../input/companies-info-wikipedia-2021/sparql_2021-11-03_22-25-45Z.csv')</p>
<pre><code>df = pd.read_csv('../input/companies-info-wikipedia-2021/sparql_2021-11-03_22-25-45Z.csv')
df_duplicate_name = df[df.duplicated(['name'])]
df = df.drop_dup... | <p>I think you're misunderstanding how <code>groupby()</code> works.</p>
<p>You can't do <code>df = df.groupby('foundation')</code>. <code>groupby()</code> does not return a new <code>DataFrame</code>. Instead, it returns a <a href="https://pandas.pydata.org/docs/reference/groupby.html" rel="nofollow noreferrer"><code>... | pandas|dataframe | 0 |
371,777 | 69,883,802 | numpy if A > Value, change B to constant | <p><strong>Problem:</strong></p>
<p>I've got a function with two arrays, but if the input data and result are within certain limits, the returned value is a constant:</p>
<pre><code>def funcB(A, C1, C2):
B = (0.381*A) + (0.05*(C1/C2)) - 0.15
B[B > 1.0] = 1.0
B[B < 0.5] = 0.5
return B
</code></pre>... | <p>Reti43 - that worked!</p>
<p>Solution was as simple as:</p>
<pre><code>def FuncB(A, C1, C2):
B = ((0.381*A) + (0.05*(C1/C2)) - 0.15).reshape(A.shape)
B[B > 1.0] = 1.0
B[B < 0.5] = 0.5
B[A < 1.64] = 0.5
B[A > 3.3] = 1.0
return B
</code></pre> | python|numpy|boolean | 0 |
371,778 | 69,724,974 | Getting NaN when Dividing Aligned DataFrame Columns | <p>I have a dataframe of the form:</p>
<pre><code> A B C
Cat-1 798.26 456.65 187.56
Cat-2 165165.53 45450.00 4897.57
Cat-3 488565.65 15198.56 15654.65
Cat-4 0.00 54256.35 49878.65
Cat-5 1156.61 789.05 89789... | <pre><code>import pandas as pd
import numpy as np
import io
df = pd.read_csv(io.StringIO(""" A B C
Cat-1 798.26 456.65 187.56
Cat-2 165165.53 45450.00 4897.57
Cat-3 488565.65 15198.56 15654.65
Cat-4 0.00 5... | python|pandas | 0 |
371,779 | 69,747,403 | Use numpy vectorize or map to speed up a loop - Python NumPy 3D matrix "get rid of a loop" Python question, Monte Carlo | <p>Now I have 1 loop that populates a 3D NumPy matrix. I'm not exactly the best at understanding a 3D array structure even though I know it's really just a XxYxZ representation of the normal XxY that I'm used to thinking in (2D). So if you want to know what this is it is a Brownian Bridge (BB) construction used in Mo... | <p>One simple solution to speed up this code is to <strong>parallelize</strong> it using Numba. You only need to use the decorator <code>@nb.njit('float64[:,:,::1](int64, int64, int64)', parallel=True)</code> for the function <code>sample_path_batches</code> (where <code>nb</code> is the Numba module). Note that <code>... | python|numpy|vectorization|montecarlo | 1 |
371,780 | 69,738,376 | How to optimize Mean Square Displacement for several particles in two dimensions in python? | <p>I want to calculate the mean square displacement for several particles, defined as:
<a href="https://i.stack.imgur.com/R8VXX.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/R8VXX.png" alt="enter image description here" /></a></p>
<p>where <code>i</code> is the index for the particle, <code>Dt</cod... | <p>Adapting the answer from <a href="https://stackoverflow.com/q/34222272/12131616">Computing mean square displacement using python and FFT</a> that uses FFT transforms, I managed to do this calculation faster by <strong>two orders of magnitude</strong>.</p>
<hr />
<h2>Generalized function for any n dimensional array<... | python|numpy|optimization|multidimensional-array|vectorization | 0 |
371,781 | 69,857,708 | Python: "could not convert string to float" error when fitting np arrays into model | <p>I am trying to do a Fake News Classification model, so as part of the pre processing, I did stopword removal, stemming and lemmatization. Afterwards I used Doc2Vec to convert the text into vectors.
x and y are the vectorized text and title columns of the FER2013 dataset, and looks something like this:</p>
<pre><code... | <p>Please remove <strong>machine learning</strong> from tags it is a <strong>python</strong> error !</p>
<p>The error is <strong>clear</strong> you are trying to transform a String to float and that String contains <strong>'['</strong> char and contains <strong>blank spaces</strong> ... it can't be transformed
you need... | python|type-conversion|numpy-ndarray|dtype | 0 |
371,782 | 43,237,043 | Smoothing spline De Boor's approach | <p>Is it possible to find smoothing spline based on De Boor's approach in Python? For data approximation.</p>
<p>Earlier I used Smoothing spline in matlab, I need exactly same algorithm in python.</p> | <p>You might want to use <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.CubicSpline.html" rel="nofollow noreferrer">scipy.interpolate.CubicSpline</a>. The example below is directly taken from the documentation:</p>
<p><a href="https://i.stack.imgur.com/MGGp3.png" rel="nofollow noreferr... | python|numpy|scipy | 2 |
371,783 | 43,461,954 | NetCDF4 Assign value into variable issue | <p>I have a netCDF file and was trying to create new variables using python netCDF4. However the program failed to assign values into the array. </p>
<p>Below is my code</p>
<pre><code>import netCDF4
file = netCDF4.Dataset(filename, "r+")
tmp = file.createVariable("tmp", "f4", ('time','height','lat','lon'), zlib=True... | <p>Accessing variables that are in a NetCDF file is done like this:</p>
<pre><code>file.variables["tmp"][0,0,0,0] = 0.1234
//read complete var
putDataInHere = file.variables["tmp"][:]
</code></pre>
<p>If you look at the <code>tmp</code> variable and print it you will see something like this:</p>
<pre><code><type ... | python|numpy|netcdf4 | 0 |
371,784 | 43,145,920 | Calculate maximum value by day of the year over certain period | <p>I have the following <strong>data frame</strong>:</p>
<pre><code>my_index = ['2005-03-20', '2008-03-20', '2014-03-20', '2007-08-15', '2012-08-15', '2007-12-31', '2011-12-31', '2013-12-31', '2014-12-31']
df = pd.DataFrame([42, 51, 36, 217, 228, -56, -50, -66, -32], index = my_index, columns = ['Temperature'])
df.ind... | <p>You can use <code>groupby</code> by <code>Series</code> created by <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DatetimeIndex.strftime.html" rel="nofollow noreferrer"><code>strftime</code></a>:</p>
<pre><code>print (df.groupby(df.index.strftime('%m-%d')).max())
Temperature
03-20 ... | python|pandas | 3 |
371,785 | 43,297,853 | Modifying timestamps in pandas to make index unique | <p>I'm working with financial data, which is recorded at irregular intervals. Some of the timestamps are duplicates, which is making analysis tricky. This is an example of the data - note there are four <code>2016-08-23 00:00:17.664193</code> timestamps: </p>
<pre><code>In [167]: ts
Out[168]:
... | <p>Here is a faster numpy version (but little less readable) which is inspired from this <a href="https://stackoverflow.com/questions/18196811/cumsum-reset-at-nan">SO article</a>. The idea is to use <code>cumsum</code> on duplicated timestamp values while resetting the cumulative sum each time a <code>np.NaN</code> is ... | python|pandas | 7 |
371,786 | 43,183,850 | TensorFlow ValueError: Variable does not exist, or was not created with tf.get_variable() | <p>I am a newbie to Tensorflow and trying to implement a Generative Adversarial Network. I am following <a href="https://github.com/adeshpande3/Generative-Adversarial-Networks/blob/master/Generative%20Adversarial%20Networks%20Tutorial.ipynb" rel="noreferrer">this</a> tutorial for the same in which we are trying to gene... | <p>Please modify your code as following, </p>
<p><code>with tf.variable_scope(tf.get_variable_scope(),reuse=False):</code>
<code>
trainerD = tf.train.AdamOptimizer().minimize(d_loss, var_list=d_vars)
trainerG = tf.train.AdamOptimizer().minimize(g_loss, var_list=g_vars)
</code></p> | python|tensorflow|optimization|conv-neural-network|mnist | 3 |
371,787 | 43,226,050 | python pandas dataframe: need speed up process related to calculate 3 rows data | <p>I have data as below:</p>
<pre><code>Tran|Type|Amount|comment
1212|A|12|Buy
1212|AA|13|Buy
1212|CC|25|S
1213|AA|1112|B
1213|A|78|B
1213|CC|1190|SEllding
1214|AA|1112|B
1214|A|78|B
1214|CC|1190|SEllding
1215|AA|1112|B
1215|A|78|B
1216|AA|1112|B
....
</code></pre>
<p>I need to filter out all tran that have 3 type ... | <p>Using <code>set_index</code>. Nice thing is, <code>A + AA == CC</code> won't happen unless all three are there so no need to check if all three are there.</p>
<pre><code>df.set_index(['Tran', 'Type']).Amount.unstack().query('A + AA == CC')
Type A AA CC
Tran
1212 12.0 13.0 ... | python|pandas|dataframe | 3 |
371,788 | 43,068,928 | DataFrames repeat combination | <p>I have the DataFrame df1 and df2:</p>
<pre><code>df1 = pd.DataFrame(['A1','A2'])
0
0 A1
1 A2
df2 = pd.DataFrame(pd.date_range('2016-01-01',periods = 2, freq = '1D'))
0
0 2016-01-01
1 2016-01-02
</code></pre>
<p>how am i gonna get this dataframe?</p>
<pre><code> 0 1
0 A1 2016-01-01
1 A1 ... | <p>You can use itertools:</p>
<pre><code>import itertools as it
pd.DataFrame(list(it.product(df1[0], df2[0])))
0 1
0 A1 2016-01-01
1 A1 2016-01-02
2 A2 2016-01-01
3 A2 2016-01-02
</code></pre>
<p><code>itertools</code> returns an generator, so you need to convert it into a list before converting it ... | python|pandas | 4 |
371,789 | 43,442,883 | Count the number of blanks before current row in pandas | <p>I have a DataFrame with a row <code>is_blank</code> that indicates whether a row is <code>NaN</code> or not. I would like to generate a new feature that counts the number of <code>NaN</code> rows before current row within each set of records grouped by <code>id</code>.</p>
<p>An example below:</p>
<pre><code>impor... | <p>You can create another group variable based on <code>is_blank</code> to reset the <em>cumsum</em>:</p>
<pre><code>test_df['outval'] = (test_df.groupby([test_df.id, (test_df.is_blank.diff() != 0).cumsum()])
.is_blank.cumsum().groupby(test_df.id).shift().fillna(0))
test_df
</code></pre>
<p><a hr... | python|pandas | 3 |
371,790 | 43,218,731 | Tensorflow can't restore global_step from checkpoint | <p>I can't seem to retrieve <code>global_step</code> from my saved checkpoint. My code:</p>
<pre><code>//(...)
checkpoint_file = tf.train.latest_checkpoint(checkpoint_dir)
saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file), clear_devices=True)
saver.restore(sess, checkpoint_file)
for v in tf.global_v... | <p>You can only use <code>tf.get_variable</code> to retrieve an existing variable if that variable was created with <code>tf.get_variable</code> in the first place. Also, the variable scope must be set appropriately. It seems that here it is trying to create a new variable called <code>'global_step'</code>, indicating ... | tensorflow | 1 |
371,791 | 43,314,859 | Joining two unequal dataframes | <p>I have two dataframes of unequal length. The first dataframe(df1) has column A with unique values and corresponding to that are column B and column C in the same dataframe.
The second dataframe(df2) has column named column A having multiple repetitions of values of Column A of df1 and corresponding to that column D ... | <p>You can use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.merge.html" rel="noreferrer"><code>merge</code></a> without parameter <code>on</code> if only common column in both DataFrame is joined column <code>Column A</code> with left join:</p>
<pre><code>print (DF1)
Column A Colum... | python|pandas | 5 |
371,792 | 43,301,247 | How to do greater than/less than binning with pandas DataFrame? | <p>I have a pandas DataFrame as follows:</p>
<pre><code>import pandas as pd
import numpy as np
data = {"first_column": ["item1", "item2", "item3", "item4", "item5", "item6", "item7"],
"second_column": ["cat1", "cat1", "cat1", "cat2", "cat2", "cat2", "cat2"],
"third_column": [5, 1, 8, 3, 731, 189, 9]}
... | <p>While fully understanding that my proposed solution looks like a hack and gives numbers that are different from yours, I still offer it here:</p>
<pre><code>df['less_than_ten'] = (df.second_column=='cat1').astype(int) +\
(df.third_column<10).astype(int)
# first_column second_column thir... | python|pandas|dataframe|binning | 1 |
371,793 | 43,457,429 | Cost of simple non object oriented Neural Network "jumping" | <p>I am building a sketch of a neural network in Python 3.4 with numpy and matrices to learn a simple XOR.
My Notation is as follows:</p>
<p><em>a</em> is the activity of a neuron</p>
<p><em>z</em> is the input of a neuron</p>
<p><em>W</em> is a weight matrix with size R^{#number of neurons in previous layer}x{#numb... | <p>I think your cost function is jumping since you perform your weight updates after each sample. However, your network is training the correct behavior nonetheless: </p>
<pre><code>479997
J = 4.7222501603409765e-05
I = [[1]
[0]], O = [[ 0.99028172]]
T = [[1]]
479998
J = 7.3205311398742e-05
I = [[0]
[0]], O = [[ 0.0... | python|numpy|machine-learning|neural-network|artificial-intelligence | 2 |
371,794 | 43,219,806 | Python - Get the Indexes of a value on Pandas' Apply function | <p>I have to recode some haplotypes that I have to code. I have them on a Pandas DataFrame of 305 rows and 129902 columns, and it looks like this (only one column and 20 rows):</p>
<pre><code>rs# rs12914615
SNPalleles C... | <pre><code>df.filter(
like='NA', axis=0
).eq(df.loc['SNPalleles'].str.replace('/', '')).astype(int)
rs12914615
rs#
NA06985 1
NA06991 1
NA06993 1
NA06993.dup 0
NA06994 0
NA07000 0
NA07019 1... | python|pandas|numpy|dataframe|apply | 1 |
371,795 | 43,292,319 | What is the structure of the data and labels in tensorflow.examples.tutorials.mnist input_data | <p>I'm trying to learn to introduce data to conv nets properly in Tensorflow, and a majority of example code uses <code>from import tensorflow.examples.tutorials.mnist import input_data</code>.</p>
<p>It's simple when you can use this to access mnist data, but not helpful when trying to establish the equivalent way to... | <p>The format of the MNIST data obtained from that example code depends on exactly how you initialize the <a href="https://github.com/tensorflow/tensorflow/blob/901ab86f35bc24461bad6d3145e62874d105038f/tensorflow/contrib/learn/python/learn/datasets/mnist.py#L104" rel="nofollow noreferrer"><code>DataSet</code></a> class... | tensorflow | 2 |
371,796 | 43,079,057 | Replicate Countifs() in pandas with multiple conditions | <p>I have a DataFrame of the form:</p>
<p><a href="https://i.stack.imgur.com/kVpeD.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/kVpeD.png" alt="enter image description here"></a></p>
<p>What I would like to achieve is a DataFrame that has unique groups and a count of non-zeros for each column 1-... | <p>Assuming you have the following DF:</p>
<pre><code>In [82]: df
Out[82]:
Group 1 2 3 4 5
0 Group1 0 1 4 0 1
1 Group1 3 0 4 1 5
2 Group2 0 1 4 3 6
3 Group2 5 1 4 0 7
4 Group3 0 0 4 7 8
5 Group3 7 1 4 7 9
</code></pre>
<p>Solution:</p>
<pre><code>In [83]: df.set_index('Gr... | python|python-3.x|pandas | 3 |
371,797 | 43,194,641 | subclassing ndarray drops information when broadcast in pyspark | <p>I'm hoping someone can help me debug an issue we're seeing with subclassed <code>ndarray</code>s in spark. Specifically when <a href="https://people.eecs.berkeley.edu/~jegonzal/pyspark/_modules/pyspark/broadcast.html" rel="nofollow noreferrer">broadcast</a> a subclassed array it seems to lose the extra information. ... | <p>At a minimum, you have a small typo - you're checking for <code>hasattr(obj, "info")</code> when instead you should be checking <code>if hasattr(self, "info")</code>. Because of the if statement flip, info isn't being carried over.</p>
<pre><code>test = Test(np.array([[1,2,3],[4,5,6]]), info="info")
print test.inf... | python|numpy|pyspark | 0 |
371,798 | 43,337,838 | google finance pandas read table to pandas dataframe | <p>How to convert that html table to pandas dataframe?</p>
<p><a href="https://www.google.com/finance/getprices?q=HINDALCO&i=60&p=15d&f=d,o,h,l,c,v" rel="nofollow noreferrer">https://www.google.com/finance/getprices?q=HINDALCO&i=60&p=15d&f=d,o,h,l,c,v</a></p>
<p>Example data:</p>
<p><a href=... | <p>You can use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html" rel="nofollow noreferrer"><code>read_csv</code></a> with parameters <code>skiprows</code> and <code>names</code> for new column names:</p>
<pre><code>url = 'https://www.google.com/finance/getprices?q=HINDALCO&i=60&a... | python|pandas|finance|google-finance | 2 |
371,799 | 43,261,747 | A Better Way to Calculate Odd Ratio in Pandas | <p>I have a dataframe counts1 which looks like:</p>
<pre><code>Factor w-statin wo-statin
AgeGroups Cancer
0-5 No 108 6575
Yes 0 223
11-15 No 5 3669
Yes 1 143
16-20 No 28 ... | <p>AFAIK pandas does not provide statistical computations and tests except basic moments like mean, variance, correlations etc...</p>
<p>However, you can rely on <a href="https://docs.scipy.org/doc/scipy/reference/index.html" rel="noreferrer"><code>scipy</code></a> for this requirement. You'll find most of what you ne... | python|pandas | 16 |
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