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 |
|---|---|---|---|---|---|---|
20,900 | 51,510,635 | scipy ND Interpolating over NaNs | <p>I have been trouble working out how to use the scipy.interpolate functions (either LinearNDInterpolator, griddata or Preferably NearestNDInterpolator)</p>
<p>There are some tutorials online but i am confused what form my data needs to be in.
The online documentation for nearestND is terrible.</p>
<p>The function a... | <blockquote>
<p>i have tried (lat,long) as a tuple</p>
</blockquote>
<p>If <code>lat</code> and <code>long</code> are 1D arrays or lists, try this:</p>
<pre><code>points = np.array((lat, long)).T # make a 2D array of shape Npoints x 2
nd = NearestNDInterpolator(points, data)
</code></pre>
<p>The you can compute in... | python|numpy|scipy|interpolation|python-xarray | 1 |
20,901 | 51,503,848 | Need Moving Average Height in OHLC data without creating additional column | <p>I need to find the Moving Average Height (abs(Close-Open)) for last 5 days. I have daily OHLC data. An easy solution would be to first create a column Height and then calculate 5 days moving average using the rolling function. But this makes my code inefficient. I need to find the Moving average height without creat... | <p>The correct answer is as below:</p>
<pre><code>df['Avg Height'] = abs(df['Close'] - df['Open']).rolling(window = 5).mean()
</code></pre> | python|python-3.x|pandas|dataframe | 0 |
20,902 | 51,461,280 | Start and end indices of continuous True intervals | <p>I have the following Pandas Series:</p>
<pre><code>s = pd.Series([False, True, True, False, True, True, True, False, True])
</code></pre>
<p>How can I get a list of tuples that each tuple represents the start and end index of continuous True intervals. For the above snippet the expected result would be:</p>
<pre>... | <p>IIUC</p>
<pre><code>t=s[s].index.to_series()
t.groupby(t.diff().ne(1).cumsum()).agg(['first','last']).apply(tuple,1).tolist()
Out[257]: [(1, 2), (4, 6), (8, 8)]
</code></pre> | python|pandas|series | 1 |
20,903 | 51,157,360 | Dictionary of Dataframes from existing Data frames | <p>I have the following code for creating a dictionary of data frames using csv files:</p>
<pre><code>l = ['employees','positions']
d = {}
for x in l:
d[x] = pd.read_csv("P:\\python_work\\data_sets\\" + x + ".csv")
</code></pre>
<p>How would I do the same using a list of data frames that already exist in memory? ... | <p>The problem is you're defining a list of strings and building a dictionary mapping each string to itself. Much simpler is to use <code>enumerate</code> with an iterable of dataframes. Assuming <code>df1</code> and <code>df2</code> are dataframes:</p>
<pre><code>d = dict(enumerate((df1, df2), 1))
</code></pre>
<p>T... | python|pandas|csv|dictionary|dataframe | 1 |
20,904 | 51,443,725 | Pandas iterate over DataFrame row pairs | <p>How can I iterate over pairs of rows of a Pandas DataFrame?</p>
<p>For example:</p>
<pre><code>content = [(1,2,[1,3]),(3,4,[2,4]),(5,6,[6,9]),(7,8,[9,10])]
df = pd.DataFrame( content, columns=["a","b","interval"])
print df
</code></pre>
<p>output:</p>
<pre><code> a b interval
0 1 2 [1, 3]
1 3 4 [2, 4]... | <p>shift the dataframe & concat it back to the original using <code>axis=1</code> so that each interval & the next interval are in the same row</p>
<pre><code>df_merged = pd.concat([df, df.shift(-1).add_prefix('next_')], axis=1)
df_merged
#Out:
a b interval next_a next_b next_interval
0 1 2 ... | python|pandas|loops|dataframe | 14 |
20,905 | 48,014,637 | Transform Pandas DataFrame of categorical variables to MultiIndex with count and proportion | <p>I have a Pandas DataFrame containing several categorical variables. For example:</p>
<pre><code>import pandas as pd
d = {'grade':['A','B','C','A','B'],
'year':['2013','2013','2013','2012','2012']}
df = pd.DataFrame(d)
</code></pre>
<p><a href="https://i.stack.imgur.com/AnJm5.png" rel="nofollow noreferrer"><... | <p>Another way you can do this to use <code>melt</code> and <code>groupby</code>:</p>
<pre><code>df_out = df.melt().groupby(['variable','value']).size().to_frame(name='n')
df_out['proportion'] = df_out['n'].div(df_out.n.sum(level=0),level=0)
print(df_out)
</code></pre>
<p>Output:</p>
<pre><code> n pr... | python|pandas|dataframe|categorical-data | 5 |
20,906 | 48,105,922 | numpy covariance between each column of a matrix and a vector | <p>Based on <a href="https://stackoverflow.com/questions/15036205/numpy-covariance-matrix">this post</a>, I can get covariance between two vectors using <code>np.cov((x,y), rowvar=0)</code>. I have a matrix MxN and a vector Mx1. I want to find the covariance between each column of the matrix and the given vector. I kno... | <p>As Warren Weckesser said, the <code>numpy.cov(X, Y)</code> is a poor fit for the job because it will simply join the arrays in one M by (N+1) array and find the huge (N+1) by (N+1) covariance matrix. But we'll always have the <a href="https://en.wikipedia.org/wiki/Covariance#Calculating_the_sample_covariance" rel="n... | python|numpy | 5 |
20,907 | 48,201,729 | Difference between np.dot and np.multiply with np.sum in binary cross-entropy loss calculation | <p>I have tried the following code but didn't find the difference between <strong>np.dot</strong> and <strong>np.multiply with np.sum</strong> </p>
<p>Here is <strong>np.dot</strong> code</p>
<pre><code>logprobs = np.dot(Y, (np.log(A2)).T) + np.dot((1.0-Y),(np.log(1 - A2)).T)
print(logprobs.shape)
print(logprobs)
cos... | <p><code>np.dot</code> is the <a href="https://en.wikipedia.org/wiki/Dot_product" rel="noreferrer">dot product</a> of two matrices.</p>
<pre><code>|A B| . |E F| = |A*E+B*G A*F+B*H|
|C D| |G H| |C*E+D*G C*F+D*H|
</code></pre>
<p>Whereas <code>np.multiply</code> does an <a href="https://en.wikipedia.org/wiki/Hadama... | python|numpy|neural-network|sum|difference | 91 |
20,908 | 48,546,699 | How to create an array with different data types in Python | <p>From what articles I read I would have to create it like such: </p>
<pre><code>fileList = np.array[("chris.txt", 2569437), ("terry.dat", 4596), ("mike.doc", 6593543),
("sarah.txt", 458667), ("david.ppt", 56437456), ("flyer.jpg", 4305),
("fred.png", 54966), ("randy_.ocx", 5968434), ... | <pre><code>fileList = np.array([("chris.txt", 2569437), ("terry.dat", 4596), ("mike.doc", 6593543),
("sarah.txt", 458667), ("david.ppt", 56437456), ("flyer.jpg", 4305),
("fred.png", 54966), ("randy_.ocx", 5968434), ("terry.dmg", 54485656),
("rick.exe", 4538565)])
</cod... | python|arrays|python-2.7|numpy | 1 |
20,909 | 48,865,055 | Multilayer perceptron with sigmoid activation produces straight line on sin(2x) regression | <p>I'm trying to approximate noisy data from the sin(2x) function using a multilayer perceptron:</p>
<pre><code># Get data
datasets = gen_datasets()
# Add noise
datasets["ysin_train"] = add_noise(datasets["ysin_train"])
datasets["ysin_test"] = add_noise(datasets["ysin_test"])
# Extract wanted data
patterns_train = dat... | <p>The default value of <code>stddev=1.0</code> in <a href="https://www.tensorflow.org/api_docs/python/tf/random_normal" rel="nofollow noreferrer"><code>tf.random_normal</code></a>, which you use for weight & bias initialization, is <em>huge</em>. Try an explicit value of <code>stddev=0.01</code> for the weights; a... | python|tensorflow|machine-learning|deep-learning | 1 |
20,910 | 70,747,567 | Using pandas date as the X values of a bar graph Python | <p>The code below outputs the bar graph using <code>graph_vals</code> and <code>Monthly_indx</code>, however the x values of the graph <code>Monthly_indx</code> are messed up and impossible to read when displayed. How would I be able to fix the x axis and make the graph readable?</p>
<p>Code:</p>
<pre><code>import pand... | <p>If you are using Matplotlib, use <code>plt.setp()</code> to tilt your x-labels:</p>
<pre><code>plt.setp(ax.get_xticklabels(), rotation=30, horizontalalignment='right')
</code></pre>
<p>You can adjust the angle to your liking.</p> | python|arrays|pandas|numpy|date | 0 |
20,911 | 71,072,292 | HOW TO run pose estimation on single image with TensorFlow-Lite? | <p>I recently used <a href="https://github.com/tensorflow/examples/tree/master/lite/examples/pose_estimation/android" rel="nofollow noreferrer">this sample</a> of great TensorFlow lite in android.</p>
<p>I can use this project correctly, but I want to estimate poses on single images too (not just in real time mode). so... | <p>Fortunately my code is not wrong! and it works correctly. and you can use it!</p>
<p>The problem was in method that I used to convert my <strong>drawable</strong> image to <strong>Bitmap</strong>.</p>
<p>I used to use these codes:</p>
<pre><code>val drawable = ResourcesCompat.getDrawable(resources, R.drawable.resize... | android|tensorflow-lite|pose-estimation | 0 |
20,912 | 70,817,996 | Python / pandas function to replace '-' negative sign but not negative sign in negative numbers ( '-' in '-1') | <p>I have a pandas dataframe that contains - in missing value,
If I use <code>.replace('-','NaN')</code> it replaces -1 with NaN1
how can I remove only - sign but not - if its -1</p>
<p>Dataframe Example</p>
<pre><code>Co1_1 Values
Pine -
Apple -1
Mango 2
Berry -
Banana -3
</code></pre>
<p>Here I want to replace - in... | <p>There is no need for <code>.replace()</code> at all. Find the cells that contain the dash, and update them:</p>
<pre><code>df[df['Values'] == '-'] = np.nan
</code></pre>
<p>Bear in mind that <code>'NaN'</code> is <strong>not</strong> a NaN: it is a string that looks like a NaN. A "real" NaN is <code>np.nan... | python|pandas|data-manipulation | 4 |
20,913 | 51,792,126 | loading big .npy file causing python to stop working | <p>I am trying to load a .npy file which is quite big (6GB) and I get a <code>Python has stopped working</code> error. Any suggestions as to how to approach getting around this issue? <a href="https://i.stack.imgur.com/AWI7N.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/AWI7N.png" alt="enter image ... | <p>You can use buffering for bigger files eg:</p>
<pre><code>with open("bigfile.npy","r",buffering=1000) as f:
contents=f.read()
#do something with the fist n lines of file at a time
</code></pre>
<p>this willn't open the entire file altogether but instead only the quantity that you specify as integer in buff... | python|python-3.x|numpy|io | 3 |
20,914 | 51,895,374 | Sort dataframe based on first digit of a column | <p>I have a dataframe like this:</p>
<pre><code> Produtos Estoque total Valor Total de estoque
0 70 10000 7180
1 70 2800000 2011550
2 70 125000 89800
3 71 540000 530980
4 ... | <p>First get values of first values by <a href="http://pandas.pydata.org/pandas-docs/stable/text.html#indexing-with-str" rel="nofollow noreferrer">indexing by str</a>, get positions by <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.argsort.html" rel="nofollow noreferrer"><code>argsort</cod... | python|pandas|numpy | 3 |
20,915 | 41,693,000 | How to merge two rows in a dataframe pandas | <p>I have a dataframe with two rows and I'd like to merge the two rows to one row.
The df Looks as follows:</p>
<pre><code> PC Rating CY Rating PY HT
0 DE101 NaN AA GV
0 DE101 AA+ NaN GV
</code></pre>
<p>I have tried... | <p>You could use <code>max</code> with transpose like</p>
<pre><code>In [2103]: df.max().to_frame().T
Out[2103]:
PC Rating CY Rating PY HT MV1 MV2
0 DE101 AA+ AA GV 10 20
</code></pre> | pandas|dataframe|merge | 7 |
20,916 | 64,523,487 | How do I append different table in multiple excel sheets with Python? | <p>I need to send new data to a table in excel with data from python. I want to sort data from an excel book in python and depending on the result I want to send it to a specific sheet in another excel workbook. I can't seem to find a way to simply add data to the bottom of a table in excel without rewriting the table ... | <p>The helper function for appending DataFrame to existing Excel file sheet<a href="https://pupli.net/2019/12/append-existing-excel-sheet-with-new-dataframe-using-python-pandas/" rel="nofollow noreferrer"> [see the link]</a>:</p>
<pre><code>import pandas as pd
def append_df_to_excel(filename, df, sheet_name='Sheet1', s... | python|excel|pandas | 0 |
20,917 | 64,310,515 | Transformers pipeline model directory | <p>I'm using the Huggingface's Transformers pipeline function to download the model and the tokenizer, my Windows PC downloaded them but I don't know where they are stored on my PC. Can you please help me? <a href="https://i.stack.imgur.com/eHRNO.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/eHRNO.... | <p>You can check the default location with:</p>
<pre class="lang-py prettyprint-override"><code>import transformers #it is important to load the library before checking!
import os
os.environ['TRANSFORMERS_CACHE']
</code></pre>
<p>In case you want to change the default location, please have a lock at this <a href="https... | python|python-3.x|pipeline|bert-language-model|huggingface-transformers | 1 |
20,918 | 64,473,929 | Removing Row Indices and Reverting them to a Column | <p>I have a pandas dataframe that looks like the following.</p>
<p><a href="https://i.stack.imgur.com/OUxjg.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/OUxjg.png" alt="enter image description here" /></a></p>
<p>I would like to create a bar chart; however, I can't because <code>LotType</code> isn... | <p>Take a look to reset_index method:</p>
<p><a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.reset_index.html" rel="nofollow noreferrer">https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.reset_index.html</a></p>
<p>df.reset_index() will create a new index c... | python|python-3.x|pandas|dataframe|keyerror | 4 |
20,919 | 47,784,648 | Apply function for counting length of a DataFrame filter | <p>What's the best way to create a new pandas column with the length of filtering of another df based on a value from the first df?</p>
<p><code>df_account</code> has account numbers</p>
<p><code>df_retention</code> has rows for each date an account numbers was active</p>
<p>I am trying to create a new column on <co... | <p>You could use groupby and count the rows in the df_retention dataframe. Assuming <code>account</code> is your index on df_account</p>
<pre><code>df_account.set_index('account',inplace=True)
df_account['retention_total'] = df_retention.groupby('account').count()
</code></pre> | pandas | 1 |
20,920 | 47,583,051 | Python: Conditional Arithmetic on pandas columns | <p>My data looks like:</p>
<pre><code>df =
contact_id date answer
1 1-May 9.00-10.00
1 2-May 9.00-10.00
1 3-May 11.00-12.00
1 4-May 14.00-15.00
1 5-May 10.00-11.00
2 2-May 9.00-10.00
2 3-May 9.00-10.00
3 1-May 14.00-15... | <pre><code>pd.concat([df,pd.get_dummies(df.answer)],1)
Out[1272]:
contact_id date answer 10.00-11.00 11.00-12.00 14.00-15.00 9.00-10.00
0 1 1-May 9.00-10.00 0 0 0 1
1 1 2-May 9.00-10.00 0 0 0 ... | python-3.x|pandas | 2 |
20,921 | 47,822,457 | combining two different formats of datetime in pandas | <p>I have many csv files that contain date and time information. Problem is that I have two different formats of date.</p>
<pre><code> MM/DD/YYYY HH:MM:SS
</code></pre>
<p>and</p>
<pre><code>MM-DD-YYYY HH:MM:SS
</code></pre>
<p>I do not want to modify each file. Is there a way to for example modify all the MM-DD-YY... | <p>Use <a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html" rel="noreferrer">pandas.to_datetime</a> before you merge them into one DataFrame/Series.</p> | python-2.7|pandas|parsing|datetime | 6 |
20,922 | 49,094,123 | tf-slim resnet pretrained model can't get correct results | <p>I am using pretrained <code>resnet50</code> model provided by <code>tensorflow slim</code>. When I am using this model to inference, I can't get correct result. Does anyone can help me to solve problem?
The follow is the code I used to do inference.
The image preprocess method following this issue <a href="https://g... | <p>Try using resnetv2 from tensorflow slim with inception preprocessing and size 299
It gives below results for the image provided</p>
<p>Probability 0.22% => [cocker spaniel, English cocker spaniel, cocker]</p>
<p>Probability 0.18% => [Sussex spaniel]</p>
<p>Probability 0.17% => [English setter]</p>
<p>Probability... | python|tensorflow|classification|resnet | 0 |
20,923 | 49,177,782 | Downsample weekly to daily data using SQL or Pandas | <p>I have weekly data going back to 2009. I am looking take this weekly data and interpolate it to daily data. This is sales data so there is certainly day/weekend trends, so I'd like to do something smarter than just dividing the weekly number by 7. However all the datapoints end on Saturdays so I would have to tak... | <p>Create a table with one row per day of week, and an associated weighting value (a percentage of the weekly total). </p>
<pre><code>CREATE TABLE WeightByDow (
dow TINYINT NOT NULL PRIMARY KEY,
weight DECIMAL(2,2) NOT NULL
);
INSERT INTO WeightByDow (dow, weight) VALUES
(1, 0.10), (2, 0.16), (3, 0.16), (4, 0.16), (... | mysql|pandas | 1 |
20,924 | 49,162,161 | Pandas: Conditionally Set Value in New Column | <p>I have a Pandas DataFrame, <code>df</code>, that has a <code>DatetimeIndex</code>. I'm trying to create a new column <code>is_weekend</code> based on whether the row's index's day field is 5 or 6.</p>
<p>Here's my attempt:</p>
<pre><code>df["is_weekend"] = np.where(df.index.dayofweek in [5,6], 1, 0)
</code></pre>
... | <p>Can you try:</p>
<pre><code>df["is_weekend"] = df.index.dayofweek.isin([5,6]).astype(int)
</code></pre>
<p>Or:</p>
<pre><code>df["is_weekend"] = df.apply(lambda x: 1 if x.name.dayofweek in [5,6] else 0, axis=1)
</code></pre>
<p>Or to use np.where, change it to:</p>
<pre><code>df["is_weekend"] = np.where(((df.in... | python|pandas | 1 |
20,925 | 49,313,585 | No module named pandas_datareader - error | <p>I am trying to gain some more experience when it comes to Python with finance and I would like your help please.</p>
<p>I am currently going through a Youtube series, and in the first 2 minutes it says to download and install certain programs, one of these is pandas-datareader. The link for the video is below.</p>
... | <p>Make sure you've installed pandas_datareader lib? </p>
<ul>
<li>Go to your terminal and type this : </li>
</ul>
<p><code>pip install pandas_datareader</code></p> | python|pandas|pandas-datareader | 1 |
20,926 | 49,134,152 | Finding implementation of methods in Tensorflow | <p>I want to change a bit minimization optimizers like <code>AdadeltaOptimizer</code>, which is used in Tensorflow.
I got the license but there is no code in lib, only reference, so how can I find the implementation? Here is Adadelta example of API:</p>
<pre><code>@tf_export("train.AdadeltaOptimizer") class
AdadeltaO... | <p>The first entry point is <a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/training/adadelta.py" rel="nofollow noreferrer"><code>python/training/adadelta.py</code></a> from tensorflow main repo. But you may notice it's a python wrapper, all ops are actually implemented in native C++ and... | python|optimization|tensorflow|gradient-descent | 1 |
20,927 | 49,022,081 | Pandas - ValueError: Error parsing datetime string "17-Jan-23" at position 3 | <p>I have the following code where I am reading date column:</p>
<pre><code>data = pd.DataFrame(array, columns=names)
data[['D_DATE']] = data[['D_DATE']].astype('datetime64')
</code></pre>
<p>But this is giving me error:</p>
<pre><code>ValueError: Error parsing datetime string "17-Jan-23" at position 3
</code></pre... | <p>Try this:</p>
<pre><code>data['D_DATE'] = pd.to_datetime(data['D_DATE'])
</code></pre>
<p>Indexing a single column with double brackets (<code>df[['D_DATE']]</code>) returns a <code>DataFrame</code> with one column named <code>'D_DATE'</code>. Indexing with a single set of brackets (<code>df['D_DATE']</code>) retu... | python|pandas | 1 |
20,928 | 59,033,369 | Is there a way to vectorize a 2D numpy array that uses start/end slicing? | <p>I have a 2D numpy array. In a foor loop, a filter window is estimated around each specified value (missing values). In my example the window size is 5x5. Then, median values of neighborhoods are calculated for each missing value. I can solve the problem with a for-loop, as below: </p>
<pre><code> ... | <p>For those wondering about the solution, well I couldn't find a direct solution about vectorized slicing, instead I solved my problem with an indirect way and an assumption of fixed 5x5 filter windows as below:</p>
<pre><code>from itertools import product
import numpy as np
import numpy.matlib as npm
window = npm.r... | python|arrays|numpy|vectorization | 0 |
20,929 | 58,789,789 | Facing issue while loading the pre-trained model | <p>I've trained my model using google colab and saved it as model.pkl. When I try to load the model in my laptop it is throwing the below error:</p>
<pre><code>Traceback (most recent call last):
File "app.py", line 8, in <module>
model = pickle.load(open('model.pkl', 'rb'))
File "sklearn\tree\_tree.pyx", line 60... | <p>Not sure about '.pkl' format,but can you try saving it as<br>
model.save('modelweight.h5') and then load as model.load ('modelweight.h5').
This shall work.
Thanks.</p> | python|pandas|numpy|machine-learning|scikit-learn | 0 |
20,930 | 58,766,604 | how to append rows of an empty column with dictionaries? | <p>i have a column <code>df["id"]</code> that has 53000 ids(rows) as <code>str</code> type. I want to append them to third column <code>df['ID']</code> but in the form of dictionaries in each row: <code>{'id':'id from the column df[id']}</code>.</p>
<pre><code>df['ID']=''
for i in range(0,len(df['id'])):
x = {'id'... | <p>You can use <a href="https://docs.python.org/3/tutorial/datastructures.html#list-comprehensions" rel="nofollow noreferrer"><code>list comprehension</code></a>:</p>
<pre><code>df['ID'] = [{'id': x} for x in df['id']]
</code></pre>
<h3>Example</h3>
<pre><code>df = pd.DataFrame({'id':['id1', 'id2', 'id3', 'id4']})
... | python|pandas | 3 |
20,931 | 58,694,645 | Generate dates based on 2 different ranges | <p>I have a dataframe df with columns : </p>
<pre><code>Date_1 count
01/09/2019 5
02/09/2019 4
03/09/2019 5
04/09/2019 6
05/09/2019 7
06/09/2019 8
07/09/2019 10
08/09/2019 9
09/09/2019 11
10/09/2019 12
11/09/2019 13
12/09/2019 14
13/09/2019 15
14/09/2019 18
15/09/2019 17
16/09/2019 18
17/09/2019 19
1... | <p>The first function to define is:</p>
<pre><code>def getSample(rng, n):
siz = rng.size
return rng.sample(n = n, replace = n > siz)
</code></pre>
<p>It returns a sample of <em>n</em> elements from <em>rng</em>.
If it is possible (the required number of elements is less or equal
to the number of elements i... | python|pandas|numpy | 0 |
20,932 | 58,988,652 | Grammar.txt not found pip3 install --upgrade tensorflow-gpu | <p>I have <code>python-3.5.0-embed-amd64</code> on my system and <code>pip</code>, <code>pip3</code> also.
They are also defined in path.</p>
<p>Now when I try to install : <strong>pip3 install --upgrade tensorflow-gpu</strong></p>
<p>It gives me error saying :</p>
<pre><code>error: [Errno 2] No such file or directo... | <p>As <a href="https://stackoverflow.com/users/2308683/cricket-007">@cricket_007</a> mentioned, it worked with <strong>not using embedded version of 3.5 specifically</strong>.</p> | python|python-3.x|tensorflow|pip | 0 |
20,933 | 70,084,499 | Extracting date time from a mixed letter and numeric column pandas | <p>I have a column in pandas dataframe that contains two types of information = 1. date and time, 2=company name. I have to split the column into two (date_time, full_company_name). Firstly I tried to split the columns based on character count (first 19 one column, the rest to other column) but then I realized that som... | <p>If the dates are all properly formatted, maybe you don't have to use regex</p>
<pre><code>df = pd.DataFrame({"A": ["2021-01-01 05:00:00Acme Industries",
"2021-01-01 06:00:00Acme LLC"]})
df["date"] = pd.to_datetime(df.A.str[:19])
df["company"]... | python|regex|pandas|dataframe|split | 1 |
20,934 | 70,111,516 | how to install Tensorflow for deeplearnig on Mac 2020 M1 (silicon) | <p>first I downloaded anaconda then I have made virtual env, and over there I tried to install Tensorflow typing " conda install tensorflow " but it didn't worked. can anyone guide me up to properly setup tensorflow on Mac m1, and my python version is 3.8</p> | <p>As the <a href="https://anaconda.org/conda-forge/tensorflow" rel="nofollow noreferrer">docs</a> says, try using:</p>
<pre><code>conda install -c conda-forge tensorflow
</code></pre>
<p>If you want to install a specific version, use:</p>
<pre><code>conda install -c conda-forge tensorflow=2.3
# 2.3 is an example
</cod... | python|tensorflow|deep-learning|jupyter-notebook | 0 |
20,935 | 56,370,383 | Pandas: Faster way to do complex column transposition with pivot function | <p>Simply put, I need to convert the following input dataframe to the output below. </p>
<p>After a few hours struggling to figure out how, by combining multiple previous stackoverflow questions, I could transform dataframe, but it takes so much time for large dataframe to convert since I use pivot and apply method.</... | <p>I would define a custom function <code>stack_str</code> to unpack string column to dataframe using <code>expand=True</code> and <code>stack</code> and <code>reset_index</code> to a series. </p>
<p>Apply <code>stack_str</code> to 2 columns of strings to make <code>df1</code> of 2 columns. </p>
<p>Next, do <code>piv... | python|pandas|pivot | 1 |
20,936 | 55,691,980 | Splitting a matrix using an array of indices | <p>I have a matrix that I want to split up into two. The two new are sort of tangled together, but I do have a "start" and "stop" array indicating what rows belong to each new matrix.</p>
<p>I have given a small example below including my own solution which I do not find satisfying.</p>
<p>Is there a smarter way of s... | <p>You can use fancy indexing functionality of numpy. </p>
<pre><code>index_b = np.array([np.arange(b_start[i], b_stop[i]) for i in range(b_start.size)])
index_c = np.array([np.arange(c_start[i], c_stop[i]) for i in range(c_start.size)])
b = a[index_b].reshape(-1, a.shape[1])
c = a[index_c].reshape(-1, a.shape[1])
</c... | python-3.x|numpy|matrix|numpy-ndarray | 0 |
20,937 | 55,924,970 | How to filter the contents of a dataframe based on the columns names | <p>I want to filter in a dataframe (with text) based on the column name.
For a given column, if an item contains the name of the column, then it is maintained, if not it is removed.
The contents of a given row is the same.</p>
<p>Consider this dataframe : </p>
<pre><code>dog cat ... | <p>You can try this:</p>
<p><code>df.where(df.apply(lambda x: x.str.contains(x.name))).bfill().head(1)</code></p>
<p>Output:</p>
<pre><code> dog cat monkey
0 The dog is beautiful The cat is beautiful The monkey is beautiful
</code></pre> | python|pandas | 1 |
20,938 | 55,742,145 | Broadcasting error when reclassifying numpy float array using vectorized function | <p>I want to evaluate each value in a 2D numpy float array if it falls within the min, max boundaries of a certain numerical class. Next, I want to reassign that value to the 'score' associated with that class. </p>
<p>E.g the class boundaries could be:</p>
<pre><code>>>> class1 = (0, 1.5)
>>> class... | <p>Try to first make the code work without using <code>np.vectorize</code>. The code above won't work even with a single float as first argument. You misspelled <code>ValueError</code>; also it's not a good idea to use <code>min</code> and <code>max</code> as variable names (they are Python functions). A fixed version ... | python|numpy|broadcast|array-broadcasting | 1 |
20,939 | 55,690,911 | Pandas, call custom function on rolling | <p>I have a function <code>my_function(data)</code> that takes in <code>data</code> a Python array (or Numpy serie) and returns an integer.</p>
<p>I could feed to this function a column from a Pandas dataframe and I would get the <code>my_function</code> of the entire series.</p>
<p>Now I would like to do that but on... | <p>Use <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.window.Rolling.apply.html" rel="nofollow noreferrer"><code>Rolling.apply</code></a>:</p>
<pre><code>data['column'].rolling(30).apply(my_function)
</code></pre>
<p>Also help <a href="https://stackoverflow.com/q/45254174">How do panda... | python|pandas | 2 |
20,940 | 64,841,815 | barplot in grid does work for more than 2 plots | <p>I am trying to group several barplots created with <code>seaborn</code> in one single grip. The data is a list of 160 000 songs from 1921 to 2000 and can be found <a href="https://www.kaggle.com/yamaerenay/spotify-dataset-19212020-160k-tracks" rel="nofollow noreferrer">here</a>. My barplot identifies the 30 most pop... | <p>You code is plotting on <code>ax2</code> for the two last data:</p>
<pre><code>lead_artists3 = Spotify_1990s.groupby('artists')['popularity'].sum().sort_values(ascending=False).head(30)
# which axis are you drawing on here?
ax3 = sns.barplot(x=lead_artists3.values, y=lead_artists3.index, palette="Greens",... | pandas|plot|seaborn | 1 |
20,941 | 64,934,830 | What is the .optimizer package in Pytorch? | <p>I am trying to write my own optimizer for pytorch and am looking at the source code <a href="https://pytorch.org/docs/stable/_modules/torch/optim/sgd.html#SGD" rel="nofollow noreferrer">https://pytorch.org/docs/stable/_modules/torch/optim/sgd.html#SGD</a></p>
<p>to get started. When I try to run the code for the SGD... | <p>Here <code>import .optimizer</code> means import <code>optimizer.py</code> from the same directory as the current .py file, so this <a href="https://github.com/pytorch/pytorch/blob/22a34bcf4e5eaa348f0117c414c3dd760ec64b13/torch/optim/optimizer.py" rel="nofollow noreferrer">file</a></p> | optimization|pytorch | 0 |
20,942 | 64,808,733 | Integration using trapezium method SciPy problems ('numpy.ndarray' object is not callable) | <p>I'm trying to create a function that lets you input an expression for y with upper and lower bounds and N number of steps. It takes the expression, should integrate it and then run it through the trapezium method for N steps and spit out the result. Currently there is an error ('numpy.ndarray' object is not callable... | <p>By looking quickly, I see that this error stems from the function trapezium_rule_integration_function,</p>
<pre><code>y = np.array(yf)
return multistep(y,trap,b,a,N)
</code></pre>
<p>The first argument of multistep should be a function, but you provided an array y, which is not callable by '()'. This array is then p... | python|python-3.x|numpy|numpy-ndarray|integrate | 0 |
20,943 | 64,869,590 | How can I remove or omit data using map method for tf.data.Dataset objects? | <p>I am using tensorflow 2.3.0</p>
<p>I have a python data generator-</p>
<pre><code>import tensorflow as tf
import numpy as np
vocab = [1,2,3,4,5]
def create_generator():
'generates a random number from 0 to len(vocab)-1'
count = 0
while count < 4:
x = np.random.randint(0, len(vocab))
... | <p>Shortly no, you cannot filter data using <code>map</code>. Map functions apply some transformation to every element of the dataset. What you want is to check every element for some predicate and get only those elements that satisfy the predicate.</p>
<p>And that function is <a href="https://www.tensorflow.org/api_do... | python-3.x|tensorflow|tensorflow2.0|tensorflow-datasets|tf.data.dataset | 6 |
20,944 | 64,876,172 | Python: Trouble making separate plots using plot_drawdown_underwater from pyfolio and get_data_yahoo from pandas-datareader to plot stock drawdown | <p>I have this code:</p>
<pre><code>from pandas_datareader import data as pdr
import pyfolio as pf
myStartDate = "2019-1-1" # (Format: Year-Month-Day)
myEndDate = "2020-11-11" # (Format: Year-Month-Day)
myTickers = ["AAPL", "MSFT"]
myData = pdr.get_data_yahoo(myTickers, myS... | <p>This works:</p>
<pre><code>from pandas_datareader import data as pdr
import pyfolio as pf
import matplotlib.pyplot as plt
myStartDate = "2019-1-1" # (Format: Year-Month-Day)
myEndDate = "2020-11-11" # (Format: Year-Month-Day)
myTickers = ["AAPL", "MSFT"]
myData = pdr.get... | python|stock|yahoo-finance|pandas-datareader | 0 |
20,945 | 65,054,715 | Concatenating data frames | <p>I have scraped some tables from Yahoo finance and I've tried concatenating them into one data frame but I'm getting an error message:</p>
<p>"ValueError: No objects to concatenate"</p>
<p>Here's the script:</p>
<pre><code>CalendarDays = 3 #<-- specify the number of calendar days you want to grab earning... | <p>First of all there are 2 loops that seems to me to repeat the same task.
<code>for i in trange(CalendarDays...): </code> and <code>for i in range(CalendarDays):</code></p>
<p>all the rest in the code works OK. Try to take longer period for testing. Not each day earnings are released. I have tested for 6 days and 4 w... | python|pandas | 1 |
20,946 | 40,265,591 | Python Pandas Using dataframe.stack().value_counts() -- How to get values of counted objects? | <p>I have a data frame that in which every row represents a day of the week, and every column represents the serial number of an internet-connected device that failed to communicate with the server on that day.</p>
<p>I am trying to get a Series of serial numbers that have failed to communicate for a full week.</p>
<... | <p>Another solution is use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.iteritems.html" rel="nofollow"><code>Series.iteritems</code></a>:</p>
<pre><code>counts = df.stack().value_counts()
seven_day = counts[counts == 7]
for index, val in seven_day.iteritems():
print(index)
</code><... | python|loops|pandas|indexing|series | 1 |
20,947 | 39,904,593 | how do I slice a numpy array using an array of 1d positions | <p>consider the arrays <code>a</code> and <code>b</code></p>
<pre><code>a = np.arange(25).reshape(5, -1)
b = np.array([4, 2, 3, 0, 1])
</code></pre>
<p>How do I slice <code>a</code> using <code>b</code> to get these elements?</p>
<p><a href="https://i.stack.imgur.com/5asw2.png" rel="nofollow noreferrer"><img src="ht... | <p>Try this:</p>
<pre><code>>>> a[np.arange(5), b]
array([ 4, 7, 13, 15, 21])
</code></pre>
<p>When indices are arrays, they are interpreted element-wise, following <a href="http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html" rel="nofollow">broadcasting rules</a>.</p> | python|numpy | 1 |
20,948 | 69,322,580 | How to print only integers that are true in a column in jupyter | <p>Uploading a CSV , and importing pandas, and matplotlib, I produce 10k rows, and some of the rows in "attachments" have attachments and some do not. I would like to know how to make print only the rows with 1 or more attachments associated with them and disregard the ones that have zero.</p>
<pre><code>df[[... | <p>You may try this one :</p>
<pre class="lang-py prettyprint-override"><code>df[df["attachments"] >= 1]
</code></pre>
<p>To answer your commentary :</p>
<blockquote>
<p>Is there a way to have this result, in conjunction with the attachments that have only 1 or more with "to", "from"</p... | python|pandas|database|dataframe | 0 |
20,949 | 69,367,991 | Pandas pd.pivot_table where aggfunc returns a set or list of unique items within a subgroup | <p>I am looking for an efficient method to <code>pivot</code> or <code>groupby</code> a data frame where the output is the list of unique items in that subgroup. I am not interested in using loops and would prefer to retain the result as a data frame. Thank you in advance.</p>
<p><strong>Example df:</strong></p>
<div c... | <p>Try:</p>
<pre><code>>>> pivoted = pd.pivot_table(df, index='number', columns='letter', values='fruit', aggfunc=lambda x: list(set(x)))
letter a b
number
101 [apple, peach, pear] [melon, orange]
201 [peach] [gra... | python|pandas|set|pivot-table|aggregate-functions | 1 |
20,950 | 41,195,036 | How to get the value in front of a specified value from Series | <pre><code>xSeries = pd.Series([1,200,5,13,3,301],index=['b', 's', 'h', 'd', 'e','a'])
</code></pre>
<p>When i was given the value 13,i want to get the value 200 before the value 13.How do i write the code?</p> | <p>If I understand you correctly, you're looking for the value 2 rows before a known value. In this case, the known value is 13.</p>
<pre><code>v = 13
xSeries = pd.Series([1,200,5,13,3,301],index=['b', 's', 'h', 'd', 'e','a'])
print(xSeries)
b 1
s 200
h 5
d 13
e 3
a 301
dtype: int64
idx =... | pandas | 0 |
20,951 | 66,051,641 | How to create a submodel from a pretrained model in pytorch without having to rewrite the whole architecture? | <p>So, I have been working on neural style transfer in Pytorch, but I'm stuck at the point where we have to run the input image through limited number of layers and minimize the style loss. Long story short, I want to find a way in Pytorch to evaluate the input at different layers of the architecture(I'm using vgg16). ... | <p>Of course you can do that:</p>
<pre><code>import torch
import torchvision
pretrained = torchvision.models.vgg16(pretrained=True)
features = pretrained.features
# First 4 layers
model = torch.nn.Sequential(*[features[i] for i in range(4)])
</code></pre>
<p>You can always <code>print</code> your model and see how it... | machine-learning|pytorch|pre-trained-model|style-transfer | 1 |
20,952 | 66,234,097 | I would like to combine multiple csv files from my google drive to pandas | <p>This is what I have so far. I need to combine 3 files from my google drive to one. I do not get an error with this code, but it only imports 1 file.</p>
<pre class="lang-py prettyprint-override"><code>import pandas as pd
import glob
path = '/content/gdrive/My Drive/Colab Datasets/'
all_files = glob.glob(path + &q... | <p>Simply by following this code:</p>
<pre><code>import os
import glob
import pandas as pd
os.chdir("content/gdrive/My Drive/Colab Datasets")
extension = 'csv'
all_filenames = [i for i in glob.glob('*.{}'.format(extension))]
#combine all files in the list
combined_csv = pd.concat([pd.read_csv(f) for f in ... | python|pandas|csv | 1 |
20,953 | 52,714,232 | Different numpy version in Anaconda and numpy.__version__ in IPython Shell | <p>I used <a href="https://stackoverflow.com/questions/1520234/how-do-i-check-which-version-of-numpy-im-using">How do I check which version of NumPy I'm using?</a> to learn how to get the version of numpy. However, when I run <code>conda list | grep numpy</code>, I get:</p>
<pre><code>numpy 1.1... | <p>It seems that you have besides your <code>python 3</code> environment in <code>anaconda</code>, another <code>python</code> with <code>IPython</code> and <code>numpy</code> installed.</p>
<p>It looks like that PyCharm and Anaconda see (correctly) the same <code>numpy</code> versions, while <code>IPython</code> whic... | python|numpy | 1 |
20,954 | 52,457,221 | Tensorflow unpooling after max_pool_with_argmax using indices | <p>While trying to implement U-SegNet from the paper by Google, I've got a problem implementing unpooling operation using argmax indices.</p>
<p>The full code:</p>
<pre><code>import tensorflow as tf
def unpool(pool, ind, ksize=[1, 2, 2, 1], name=None):
with tf.variable_scope('name') as scope:
input_shap... | <p>@Tofik.AI witch Tensorflow version do you use?
According to the latest documentation, it's incorrect.
My implementation:</p>
<pre><code>def unpool(pool, ind, ksize=[1, 2, 2, 1], name=None):
with tf.variable_scope('name') as scope:
input_shape = tf.shape(pool)
output_shape = [input_shape[0], input_shape[1] *... | tensorflow|machine-learning|deep-learning | 1 |
20,955 | 52,702,247 | Pandas: renaming columns that have the same name | <p>I have a dataframe that has duplicated column names a, b and b. I would like to rename the second b into c. </p>
<pre><code>df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "b1": [7, 8, 9]})
df.rename(index=str, columns={'b1' : 'b'})
</code></pre>
<p>Trying this with no success..</p>
<pre><code>df.rename(index=... | <p>try:</p>
<pre><code>>>> df.columns = ['a', 'b', 'c']
>>> df
a b c
0 1 4 7
1 2 5 8
2 3 6 9
</code></pre> | python|pandas|dataframe|indexing | 6 |
20,956 | 58,251,062 | JSONDecodeError: Expecting value: line 1 column 2 (char 1) | <p>I am getting this error while importing a JSON dataset from a website. </p>
<blockquote>
<p>JSONDecodeError: Expecting value: line 1 column 2 (char 1)</p>
</blockquote>
<p>I am working in colaboratory and wanted to import the sarcastic dataset, but since I don't know JSON, I am stuck. I have tried different plac... | <p>In my case, i was able to resolve this by replacing single quotes with double quotes.</p>
<p><code>a = "['1','2']"</code></p>
<p><code>json.loads(a.replace("'",'"'))</code></p> | json|python-3.x|tensorflow|deep-learning | 1 |
20,957 | 58,595,531 | How to autofill rows in one column with conditioning of other column data? | <p>I have a dataframe A:</p>
<pre><code> State Region Code
0 Texas Texas 1
1 Houston 0
2 Dallas 0
3 Austin 0
4 Michigan Michigan 1
5 Ann Arbor 0
6 Yipsilanti 0
7 Alaska Alaska 1
8 Troy 0
</code></pre>
<p>... | <p>You want <code>cumsum</code> for <code>Group</code> and <code>ffill</code> for <code>State</code>:</p>
<pre><code>df['Group'] = df['Code'].eq(1).cumsum()
df['State'] = df['State'].ffill()
</code></pre>
<p>Output:</p>
<pre><code> State Region Code Group
0 Texas Texas 1 1
1 Texas ... | pandas|autofill | 0 |
20,958 | 58,359,148 | i am trying to write a function which divide a column 3 parts | <p>i am trying to write a function which takes a column as input and divide it 3 parts as short, medium , long then return them as list.</p>
<p>i tried to do it with loc function, but, however, it return a dataframe rather than a list.</p>
<pre><code>def DivideColumns(df,col):
mean = df[col].mean()
maxi = df... | <p>Since you are using pandas you can use the concept of binning. By using the pandas <code>cut</code> function you can divide in the ranges you like and it makes your code easier to read. More info <a href="https://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.cut.html" rel="nofollow noreferrer">here</... | python|pandas | 0 |
20,959 | 58,562,662 | apply function on subset of dataframe rows in column based on value in other column | <p>New to pandas, so please bear with me.</p>
<p>I have a text processing function I'd like to run on a column in my datafame conditional on the value in another column. I've seen </p>
<p>Depending on whether something is flagged, I want to run a translation function on it.</p>
<pre><code> account article ... ... | <p>I used a test DataFrame similar to yours, without <em>translation</em> column:</p>
<pre><code> account article flag
0 123 text1 1
1 123 text2 0
2 123 text3 1
</code></pre>
<p>Then I defined a "surrogate" translation function:</p>
<pre><code>def translate(txt):
return... | python|pandas|dataframe | 10 |
20,960 | 68,920,479 | pandas.Dataframe equivalent for Pandas.read_csv converters? | <p>This <a href="https://stackoverflow.com/questions/34139102/whats-the-difference-between-dtype-and-converters-in-pandas-read-csv">discussion</a> covers the differences between <code>dtypes</code>and <code>converters</code> in <code>pandas.read_csv</code> function.</p>
<p>I could not find an equivalent to converters f... | <p>Change <code>f_population_to_int</code> function for return same value if any error (remove <code>KeyError</code>) and after create DataFrame use <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.apply.html" rel="nofollow noreferrer"><code>Series.apply</code></a>:</p>
<pre><code>data =... | python|pandas|dataframe | 1 |
20,961 | 69,259,606 | how to fetch and store integer value inside (...) in a cell in excel to a variable using python | <p>I'm trying to read integer values 10, 12, 2 from the second column of the excel sheet and store it to variables using python.</p>
<p>provided link to the png image representing cells in excel sheet</p>
<p>[https://i.stack.imgur.com/82por.png]</p>
<p>Using below code I'm able to read the entire data - <code>Parrot(10... | <p>Since the question is actually 'how do I extract the number from a mixed string of numbers and letters' you probably want a regex.</p>
<pre class="lang-py prettyprint-override"><code>import re
cell = "Parrot(10)"
matches = re.search(r"([0-9]+)", cell)
number = int(matches.group(1))
</code></pre>
... | python|excel|pandas|dataframe | 1 |
20,962 | 69,219,923 | Python Dataframe Chunk Column Indexing Incorrectly | <p>I am learning DataFrame Chunking. My pseudocode is simple:</p>
<ol>
<li>Break down the SOURCE_FILE into a number of chunks</li>
<li>Load a chunk (with a loop)</li>
<li>Add a column with a predicted label & another with confidence</li>
<li>Write the chunk to the drive</li>
<li>Continue the loop</li>
</ol>
<p>The ... | <p>This is a little late, but I wanted to answer this in case anyone has the same problem in the future. You are attempting to concatenate two new columns to an already existing chunk with two columns. Pandas concatenates columns side-by-side using the index of the columns, and the index of your first chunk matches the... | python|pandas|dataframe|chunking | 0 |
20,963 | 68,953,887 | Got ValueError: No gradients provided for any variable when retraining (fit) the loaded subclassed-Model with tf.Keras | <p>This has been posted in the <a href="https://github.com/keras-team/keras/issues/15261" rel="nofollow noreferrer">official Keras repo</a>, but just need to wait so keen to post here as well to see if I can find the solution earlier.</p>
<p><strong>System information</strong></p>
<ul>
<li>Have I written custom code (a... | <p>Ok, I tried it out myself and here is what I found and hence the possible workaround.
<strong>BOTH</strong> <code>get_config</code> and <code>from_config</code> <strong>MUST</strong> be explicitly defined.</p>
<p>If only <code>get_config</code> is defined will end up getting:</p>
<pre><code>Traceback (most recent ca... | python|tensorflow|machine-learning|keras|tf.keras | 1 |
20,964 | 69,066,267 | This torch project keep telling me "Expected 2 or more dimensions (got 1)" | <p>I was trying to make my own neural network using PyTorch. I do not understand why my code is not working properly.</p>
<pre><code>import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optimizers
import numpy as np
from tqdm import tqdm
import os
import hashlib
# Only for the first... | <p>The tensor you use as the dataset, <code>Xs</code> is shaped <code>(n, 2)</code>. So when looping over it each element <code>x</code> ends up as a 1D tensor shaped <code>(2,)</code>. However, your module expects a batched tensor as input, <em>i.e.</em> here a 2D tensor shaped <code>(n, 2)</code>, just like <code>Xs<... | python|pytorch | 1 |
20,965 | 44,582,503 | Drop all drows in python pandas dataframe except | <p>Beginner Pandas Question:</p>
<h2>How do I drop all rows except where Ticker = NIVD?</h2>
<p>That is, return a dataframe like:</p>
<pre><code> Sector Ticker Price
0 Future NVID 350
1 Future NVID NaN
</code></pre>
<p>Dataframe Code:</p>
<pre><code>import numpy as np
import pandas as pd
raw_data... | <p>You are really close.</p>
<p>Use <a href="http://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-indexing" rel="nofollow noreferrer"><code>boolean indexing</code></a> or <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.query.html" rel="nofollow noreferrer"><code>query</code... | python|pandas|dataframe | 3 |
20,966 | 44,769,126 | In Tensorflow, how to use a restored meta-graph if the meta graph was feeding with TFRecord input (without placeholders) | <p>I trained a network with TFRecord input pipeline. In other words, there was no placeholders. Simple example would be:</p>
<pre><code>input, truth = _get_next_batch() # TFRecord. `input` is not a tf.placeholder
net = Model(input)
net.set_loss(truth)
optimizer = tf...(net.loss)
</code></pre>
<p>Let's say, I acquire... | <p>You can build a graph that uses <code>placeholder_with_default()</code> for the inputs, so can use both <code>TFRecord input pipeline</code> as well as <code>feed_dict{}</code>.</p>
<p>An example:</p>
<pre><code>input, truth = _get_next_batch()
_x = tf.placeholder_with_default(input, shape=[...], name='input')
_y ... | tensorflow | 9 |
20,967 | 44,629,205 | Google TensorFlow based seq2seq model crashes while training | <p>I have been trying to use Google's RNN based <a href="https://github.com/google/seq2seq" rel="nofollow noreferrer">seq2seq model. </a></p>
<p>I have been training a model for text summarization and am feeding in a textual data approximately of size 1GB. The model quickly fills up my entire RAM(8GB), starts filling ... | <p>You model looks pretty small. The only thing kind of big is the train data. Please check to make sure your <code>get_batch()</code> function has no bugs. It is possible that each batch you are actually loading the whole data set for training, in case there is a bug there. </p>
<p>In order to quickly prove this, jus... | tensorflow|nlp|lstm|sequence-to-sequence | 0 |
20,968 | 60,981,310 | Question about numpy correlate: not giving expected result | <p>I want to make sure I am using numpy's correlate correctly, it is not giving me the answer I expect. Perhaps I am misunderstanding the correlate function. Here is a code snipet with comments:</p>
<pre><code>import numpy as np
ref = np.sin(np.linspace(-2*np.pi, 2*np.pi, 10000)) # make some data
fragment = ref[2149:7... | <p>That looks like a precision error to me, cross-correlation is an integral and it will always have problems when being represented in discrete space, I guess the problem arises when the values are close to 0. Maybe if you increase the numbers or increase the precision that difference will disappear but I don't think ... | python|numpy|correlation | 1 |
20,969 | 61,178,564 | How to stack seaborn pairgrids in python | <p>I am making a pairgrid that compares multiple dependent variables to one independent variable. I have the plot and it's exactly the way I want it except for one thing: I don't want the plots to be in one really long line.</p>
<p>Here is my code:</p>
<pre><code>g = sns.PairGrid(df, y_vars=["W%"], x_vars=["PPG", "FG... | <p>Use <code>col_wrap</code>and <code>height</code> parameters to <code>sns.FacetGrid</code>.</p>
<p>From the seaborn tutorial <a href="https://seaborn.pydata.org/tutorial/axis_grids.html" rel="nofollow noreferrer">https://seaborn.pydata.org/tutorial/axis_grids.html</a></p>
<pre><code>attend = sns.load_dataset("atten... | python|pandas|matplotlib|data-visualization|seaborn | 0 |
20,970 | 71,604,622 | Check if dictionaries are equal in df | <p>I have a df, in which a column contains dictionaries:</p>
<pre><code> a b c d
0 a1 b1 c1 {0.0: 'a', 1.0: 'b'}
1 a2 b2 c2 NaN
2 a3 b3 c3 {0.0: 'cs', 1.0: 'ef', 2.0: 'efg'}
</code></pre>
<p>and another dict:</p>
<pre><code>di = {0.0: 'a', 1.0: 'b'}
</code></pre>
<p>I want to add... | <p>You can try</p>
<pre><code>df['new'] = df['d'].eq(di).map({True:'yes',False:''})
</code></pre> | python-3.x|pandas|dataframe|dictionary | 1 |
20,971 | 71,577,525 | huggingface sequence classification unfreezing layers | <p>I am using longformer for sequence classification - binary problem</p>
<p>I have downloaded required files</p>
<pre><code># load model and tokenizer and define length of the text sequence
model = LongformerForSequenceClassification.from_pretrained('allenai/longformer-base-4096',
... | <ol>
<li><code>requires_grad==True</code> means that we will compute the gradient of this tensor, so the default setting is we will train/finetune all layers.</li>
<li>You can only train the output layer by freezing the encoder with</li>
</ol>
<pre><code>for param in model.base_model.parameters():
param.requires_gr... | python|pytorch|classification|huggingface-transformers | 1 |
20,972 | 71,642,344 | Capture the first three unique values from each row in a pandas dataframe | <p>I have a pandas dataframe like below:</p>
<pre><code>pd.DataFrame({'col1': ['A', 'C'],
'col2': ['A', 'B'],
'col3': ['B', 'B'],
'col4': ['A', 'C'],
'col5': ['C', 'F'],
'col6': ['D', 'D'],
'col7': ['E', 'G'],
'col8': ['E'... | <p>In your case do <code>unique</code></p>
<pre><code>df = df.apply(lambda x : pd.Series(x.unique()[:3]),axis=1)
Out[96]:
0 1 2
0 A B C
1 C B F
</code></pre> | python|pandas|dataframe | 4 |
20,973 | 42,414,158 | How to find precision of float if n bits are corrupt or lost | <p>I am currently solving a sparse linear system with the sparse solver from scipy, Python. </p>
<p>I am comparing an analytical solution to the result of the simulated solution. However, in some regime, I have got some doubts as to the precision of the simulated result. </p>
<p>It is quite hard to estimate the <a hr... | <p>Roughly, <code>float64</code> have 52 bits of mantissa and ~ 16 significative decimal digits.
so if you are sure that 26 bits are corrupted, conserve 8 decimal digits.</p>
<p>more precisely the error is about <code>Number_to_ verify * 2**(-26) # 2e-05</code>.</p>
<p>A technical approach to valid that :</p>
<pre><... | python|numpy|floating-point|precision | 2 |
20,974 | 42,261,627 | merge values of groupby results with dataframe in new column Python Pandas | <p>I have a groupby object which looks like:</p>
<pre><code>Age Pclass fam_size
0.0 3 alone 11.586475
1.0 1 alone 83.210410
2 alone 18.092672
3 alone 7.974073
2.0 1 alone 72.784513
2 alone 12.058114
3 alone 10... | <p>IIUC you can do it this way:</p>
<pre><code>In [299]: df
Out[299]:
a b c d
0 1 1 1 11
1 1 1 2 12
2 1 2 3 13
3 1 2 4 14
4 2 1 5 15
In [300]: g
Out[300]:
a b
1 1 3
2 7
Name: c, dtype: int64
In [301]: df.merge(g.reset_index(), on=['a','b'], how='left',
suffixe... | python|python-3.x|pandas | 1 |
20,975 | 43,266,104 | Renaming multiple numbered files | <p>I have a series of data from one of my data loggers and it saves them sequentially:</p>
<pre><code>data_0, data_1, ... , data_10, data_11,.., data_100, data_101
</code></pre>
<p>and so on.</p>
<p>I was importing the files one by one in pandas and processing them, later realizing that the file sequence that pandas... | <p>The problem is that your file-names are being returned sorted, which for strings uses lexicographic ordering (the normal ordering for strings).</p>
<pre><code>In [23]: x = ['data_0', 'data_1', 'data_100', 'data_101', 'data_109', 'data_11', 'data_110']
In [24]: sorted(x)
Out[24]: ['data_0', 'data_1', 'data_100', 'd... | python|file|pandas | 4 |
20,976 | 43,422,742 | Pandas define a seasonal year from June 1 - July 30 instead of Jan 1 - Dec 31 | <p>I have seasonal snow data which I want to group by snow year (July 1, 1954 - June 30, 1955) rather than having one winter's data split over two years (January 1, 1954 - December 31, 1954 and January 1, 1955 - Dec 31, 1955.) </p>
<p><a href="https://i.stack.imgur.com/qp7Y1.png" rel="nofollow noreferrer">example dat... | <p>I think yes, with <a href="http://docs.scipy.org/doc/numpy-1.10.1/reference/generated/numpy.where.html" rel="nofollow noreferrer"><code>numpy.where</code></a>:</p>
<pre><code>years = df['date'].dt.year
df['Seasonal_Year'] = np.where(df['date'].dt.month <= 7, years, years + 1)
</code></pre> | python|pandas | 3 |
20,977 | 43,113,007 | Is the batch size specified for each GPU in tensorflow | <p>In Tensorflow models example (slim) , when we specify the batch size is it per GPU or is that the total batch size split among the gpus ?</p>
<p>python train_image_classifier.py --dataset_name=flowers --dataset_split_name=train --dataset_dir=/tmp/flowers-data --model_name=inception_v3 --log_ever... | <p>That depends on how the specific model was implemented. In this case I believe it's the per-gpu batch size, but that's only if you're using the model I'm thinking of. The source code should be clearer.</p> | tensorflow|models | 0 |
20,978 | 50,385,447 | how to deal with keras: erro:slice index 0 of dimension 0 out of bounds | <p>I use keras(tensorflow backend) to build my lstm network,this is my code:</p>
<pre class="lang-python prettyprint-override"><code>from keras.models import Sequential,Model
from keras.layers import LSTM,Conv1D,Dense,MaxPooling1D,GlobalMaxPooling1D,Input,Concatenate
from keras.optimizers import Adam
x_input = Inpu... | <p>Let's take a single observation and see what happens. A single observation has the following shape : (14 , 49).
After the first Conv1D layer, it will change to (11,32) (kernel size of 4 and strides of 1). After the first Maxpooling1D layer, it will go to (3 , 32) (since you din't set strides, it will default to you... | tensorflow|keras|deep-learning | 7 |
20,979 | 50,242,709 | Tensorflow's inverse transformation to squeeze | <p>I'd like to use <code>tf.image.crop_and_resize</code> on an image that is a 3-D tensor with shape <code>[image_height, image_width, depth]</code>, yet <code>tf.image.crop_and_resize</code> expects a 4-D tensor with shape <code>[batch, image_height, image_width, depth]</code>.</p>
<p>How can I temporarily "un-squeez... | <p>You can use <code>tf.expand_dims</code> to make a <code>3-D</code> image to a <code>4-D</code> shape of <code>batch size</code> 1. </p>
<pre><code>tf.image.crop_and_resize(tf.expand_dims(image, 0), ...)
</code></pre> | tensorflow | 1 |
20,980 | 50,434,710 | Keras An operation has None for gradient when train_on_batch | <p>Google Colab to reproduce the error <a href="https://colab.research.google.com/drive/1YgFrrSaxj8O1cXgU3ZnLx4uKVOa6bEZF#scrollTo=UjFe1KXVI-c-&forceEdit=true&offline=true&sandboxMode=true" rel="nofollow noreferrer">None_for_gradient.ipynb</a></p>
<p>I need a custom loss function where the value is calcula... | <p>I think the reason is that input_y and input_y_pred are all keras Input,your loss function is calculated with these two tensor,they are not binded up with the model parameters,so the loss function gives no gradient to your model</p> | python|tensorflow|keras|deep-learning|artificial-intelligence | 0 |
20,981 | 45,502,215 | How to create a histogram-like bar plot for my dataset? | <p>I have the following dataframe <code>df</code>:</p>
<pre><code>time_diff avg_trips_per_day
631 1.0
231 1.0
431 1.0
7031 1.0
17231 1.0
20000 20.0
21000 15.0
22000 10.0
</code></pre>
<p>I want to create a histogram with <code>time_diff</code> in X axis and <co... | <pre><code>import pandas as pd
import seaborn as sns
from io import StringIO
data = pd.read_table(StringIO("""time_diff avg_trips_per_day
631 1.0
231 1.0
431 1.0
7031 1.0
17231 1.0
20000 20.0
21000 15.0
22000 10.0"""), delim_whitespace=True)
data['timegroup'] = pd... | python|pandas|matplotlib | 5 |
20,982 | 45,545,675 | Rename Pandas dataframe with NaN header | <p>i currently work with dataframes, and i'm stacking them thus to achieve specific format. I have a question i'm trying to change name of the header but it doesn't work ( by using.. .rename(columns={'NaN'='type', inplace=True), same thing im trying to change the name of columns '6' to Another with the same principe as... | <p>I think you need <code>rename</code> by <code>dict</code> or <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Index.fillna.html" rel="noreferrer"><code>Index.fillna</code></a>:</p>
<pre><code>df = df.rename(columns={np.nan: 'type'})
</code></pre>
<hr>
<pre><code>df.columns = df.columns.fillna... | python|pandas|dataframe|header | 16 |
20,983 | 45,521,499 | legacy_init_op in TensorFlow Serving | <p>I've noticed every example on TensorFlow Serving uses <code>legacy_init_op</code> parameter in <code>SavedModelBuilder</code> but I have not found any clear explanations on what this is and why it is called <strong>legacy</strong>. Anyone knows the purpose of this argument? </p>
<p>Example:</p>
<pre><code>legacy_i... | <p>Tensorflow <a href="https://tensorflow.github.io/serving/architecture_overview.html" rel="noreferrer">Serving</a> uses lookup tables for embedding or vocabulary lookups. Previous version of tf < 1.2 initialization of tables need a separate op. So you need to use the <code>tf.tables_initializer()</code> separately... | python|machine-learning|tensorflow|deep-learning | 7 |
20,984 | 62,602,324 | Inconsistencies with R functions taking `POSIXct` from rpy2 | <p>Earlier I learned that one needs to enclose a <code>datetime64[ns]</code> series in a <code>pandas.DataFrame</code>, even with a single column to R's <code>summary</code> function for the input to be properly taken as <code>POSIXct</code>, like so:</p>
<pre class="lang-py prettyprint-override"><code>import pandas as... | <blockquote>
<p>Earlier I learned that one needs to enclose a <code>datetime64[ns]</code> series in a <code>pandas.DataFrame</code>, even with a single column to R's <code>summary</code> function for the input to be properly taken as <code>POSIXct</code>, like so:</p>
</blockquote>
<p>(...)</p>
<p>A <code>pandas.Series... | pandas|rpy2 | 0 |
20,985 | 62,733,461 | I want to convert a dictionary to pandas dataFrame | <pre><code>di={'ind': 1, 'age': 59, 'bp': 70, 'sg': 1.01, 'al': 1.0, 'su': 3.0, 'rbc': 0.0, 'pc': 0.0, 'pcc': 0.0, 'ba': 0.0, 'bgr': 424.0, 'bu': 55.0, 'sc': 1.7, 'sod': 138.0, 'pot': 4.5, 'hemo': 12.0, 'pcv': 37.0, 'wbcc': 10200.0, 'rbcc': 4.1, 'htn': 1.0, 'dm': 1.0, 'cad': 1.0, 'appet': 1.0, 'pe': 0.0, 'ane': 1.0}
</... | <p>You can try</p>
<pre><code>df = pd.Series(di).to_frame(0).T.set_index('ind')
</code></pre> | python-3.x|pandas|dataframe|dictionary|data-analysis | 1 |
20,986 | 62,646,601 | How to extract user ratings from a movie dataset | <p>This screenshot is the sample of the merged movielens dataset, I have two questions:</p>
<ol>
<li>If I want to extract only user 191 movieid, title, genres and ratings alone, how will I do this?</li>
<li>How can I list out <strong>only</strong> the years at the end of each movie title?</li>
</ol>
<p>Any guide will b... | <p>First question; Use a Boolean selection</p>
<pre><code>df[df['userid']=='191']
</code></pre>
<p>Second question# Use regex to extract phrases between brackets</p>
<pre><code>df['Year']=df.title.str.extract('\((.*?)\)')
</code></pre> | python-3.x|pandas|string|data-science|data-scrubbing | 2 |
20,987 | 62,704,666 | Create New Rows Based on Other Rows in Python? | <p>df:</p>
<pre><code> Date Month year Month_yr
0 Jul 19 Jul 19 Jul_2019
1 Ogf 19 Jul 19 Jul_2019
2 May19 May 19 May_2019
3 May 19 May 19 May_2019
4 19May May 19 May_2019
5 Jun19 Jun 19 Jun_2019
6 Jun 19 Jun 19 Jun_2019
7 May 20 May 20 May_2019
8 20May ... | <p>You can select from year 19, all the records that are greater than the last month in year 20 and union them</p>
<pre><code>import pyspark.sql.functions as F
# Test data
dfs = sqlContext.createDataFrame([('Jan',19,1),('Feb',19,1),('Mar',19,1),('Aug',19,5),('Sep',19,1),('Dec',19,1),('Jan',20,6),('Feb',20,8),('Feb',20,... | python|python-3.x|pandas|pyspark | 1 |
20,988 | 62,664,710 | How to stack this specific row on pandas? | <p>Consider the below df</p>
<pre><code>df_dict = {'name': {0: ' john',
1: ' john',
4: ' daphne '},
'address': {0: 'johns address',
1: 'johns address',
4: 'daphne address'},
'phonenum1': {0: 7870395,
1: 7870450,
4: 7373209},
'phonenum2': {0: None, 1: 123450 , 4: None},
'phonenum3': {0: None, 1: 123456... | <p>you can do it using <code>set_index</code> and <code>stack</code>, then <code>groupby.cumcount</code> per name and address to get the later column names, then <code>unstack</code> and do some <code>reset_index</code> and <code>rename_axis</code> for cosmetic.</p>
<pre><code>df_ = (df.set_index(['name', 'address'])
... | python|pandas|pandas-groupby | 4 |
20,989 | 62,491,418 | recursionerror: maximum recursion depth exceeded in comparison in tensorflow with skopt | <p>I want to compute a Bayesian Search with [skopt] (<a href="https://scikit-optimize.github.io/stable/auto_examples/bayesian-optimization.html" rel="nofollow noreferrer">https://scikit-optimize.github.io/stable/auto_examples/bayesian-optimization.html</a>).
My dataset is a Time series, and t is my time step.</p>
<p>Bu... | <pre class="lang-py prettyprint-override"><code>sys.setrecursionlimit(10000)
</code></pre>
<p>seems to resolve my problem.</p> | python-3.x|tensorflow2.0|skopt | 1 |
20,990 | 54,333,293 | How do you embed a tflite file into an Android application? | <p>What are the step-by-step instructions for using a TFlite file and embedding it in an actual Android application? For reference, this is regression. Input will be an image, output should be a number. I have already looked at the TensorFlow documentation but they do not explain how to do it from scratch.</p> | <p>The following steps are required to use TFLite in Android:</p>
<ol>
<li>include the dependency 'org.tensorflow:tensorflow-lite:+' to your build.gradle</li>
<li>Make sure files of type .tflite will not be compressed using the aaptOptions in your build.gradle</li>
<li>Make the model .tflite file available by putting i... | tensorflow|tensorflow-lite | 4 |
20,991 | 73,624,893 | Gradient of X is NoneType in second iteration | <p>I'm trying to make images which will fool model, but I have some problem with this code. In the second iteration I get <code>TypeError: unsupported operand type(s) for -: 'Tensor' and 'NoneType' </code>
Why is the grad NoneType even if it's working for the first time?</p>
<pre class="lang-py prettyprint-override"><c... | <p>I could not fool the network with a target size of 1000. But I was able to fool it with a target size of 64. Here is a minimal code snippet that runs without error:</p>
<pre class="lang-py prettyprint-override"><code>import matplotlib.pyplot as plt
import torch
torch.manual_seed(0)
target_size = 64
model = torch.nn... | machine-learning|neural-network|pytorch|conv-neural-network | 2 |
20,992 | 73,588,307 | matching values and creating a pandas dataframe | <p>So the code down below looks for lines in the csv file that contain the keywords <code>$$n[]:</code> or <code>$$n[<characters>]:</code> if the code has these it will be a <code>note</code> and this note will be matched with <code>text</code>(A line without the <code>$$n:</code> or <code>$$n[<characters>]... | <p>This should work:</p>
<pre><code>import re
import pandas as pd
with open('test.csv') as f:
lines = [s.strip() for s in f.read().split('\n') if s]
texts = []
notes = []
for i, line in enumerate(lines):
if re.search(r'\$\$.*\:', line):
notes.append(re.sub(r'\$\$.*\:', '', line).strip())
else:
... | python|pandas|database|dataframe|numpy | 0 |
20,993 | 71,380,862 | how to convert a mat converted dictionary(images) into pandas dataframe | <p><a href="https://i.stack.imgur.com/QNxu1.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/QNxu1.jpg" alt="The image contains the output" /></a>
I have converted a mat file into python dictionary. Now how can i convert this into Pndas data frame.</p> | <p>IIUC use if need create dictionary of <code>DataFrames</code>:</p>
<pre><code>d = {k: pd.DataFrame(v) for k, v in matdata.items()}
</code></pre> | python|pandas|dataframe|mat | 0 |
20,994 | 71,156,108 | Pandas Dataframe create columns by grouping cells by values | <p>I have the following problem. My DataFrame looks like this (only with 100.000 entries):</p>
<pre><code>col_1 col_2 col_3
green yellow red
yellow green purple
green yellow red
yellow brown green
red yellow purple
red green yellow
</co... | <p>Here's one approach: With <code>get_dummies</code> convert it to one-hot encoded columns; sum across the columns and use <code>np.where</code> to populate the DataFrame with column names. Finally, fix the column names:</p>
<pre><code>s = pd.get_dummies(df)
s.columns = [c.split('_')[-1] for c in s.columns]
s = s.grou... | python|pandas|dataframe | 2 |
20,995 | 71,174,542 | Python Numpy broadcasting 4D array eating RAM | <p>I am trying to do simple calculations on 4D image arrays (timeseries included), but the broadcasting eat up a lot of RAM compared to the initialized arrays, and I have already tried to read others with the some what similar problems. E.g.
<a href="https://stackoverflow.com/questions/31536504/memory-growth-with-broad... | <p>First of all, the two input arrays contains 2_621_440_000 items resulting in 15 GiB of RAM. The temporary array generated by <code>np.subtract</code> contains the same number of element resulting in 10 GiB of RAM. The output array is overwritten so it should not take more RAM. This means Numpy should already take 25... | python|arrays|numpy|performance|array-broadcasting | 0 |
20,996 | 52,420,357 | 'contains' for numpy library? | <p>Something like this:</p>
<p>This works:</p>
<pre><code>df.C = np.where((df.C.values == 'yes')), 'no', df.C.values)
</code></pre>
<p>But if the word is part of the row and no tht e whole row something like 'contains' from pandas would be needed.</p>
<pre><code>df.C = np.where((df.C.values.contains('ye')), 'no', d... | <p>Use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.str.contains.html" rel="nofollow noreferrer"><code>Series.str.contains</code></a>:</p>
<pre><code>df.C = np.where(df.C.str.contains('ye').values, 'no', df.C.values)
</code></pre>
<p>If performance is important:</p>
<pre><code>df.C = ... | python|pandas|numpy | 1 |
20,997 | 52,124,792 | How to Correctly Install Tensorflow on Windows x64-bit Python 3.6.x | <p>When I first used <code>$ pip install --upgrade tensorflow</code>, I was unable to install it. After looking around on multiple sites, I realized that it was because I had installed the x32-bit version of Python, not the x64-bit. After uninstalling the old Python and installing the new one, I was able to install ten... | <h2>SOLUTION</h2>
<p>Thank you P-Gn for marking this as a duplicate. I would not have found the answer otherwise. Tensorflow works in version 1.5 for me, but I will probably have to find a way to get 1.10 working.</p> | python|tensorflow|pip | 0 |
20,998 | 60,416,296 | How to drop rows that are composed of a series of specifc values | <p>So I have the following df:</p>
<pre><code>import pandas as pd
import numpy as np
data = [['John', 1.0, 2.0, 3.0],
['Mary', 0.0, np.nan, ""],
['Chad', 0.0, np.nan, 1.0]]
df = pd.DataFrame(data, columns=['Name', 'Value1', 'Value2', 'Value3'])
df.set_index('Name', inplace=True)
</code></pre>
<p... | <p>One way could be to simply use <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.any.html" rel="nofollow noreferrer"><code>any</code></a>, since all <code>0.0</code> , <code>NaN</code> and <code>""</code> evaluate to <code>False</code>:</p>
<pre><code>df[df.any(1)]
Value1 ... | python|pandas | 2 |
20,999 | 60,657,816 | Series markers in pandas dataframe plots | <p>I'm plotting a pandas dataframe which contains multiple time series.
I have more series than the number of colors matplotlib chooses from, so there is ambiguity in mapping legend colors to plots.<br>
I haven't seen any matplotlib examples that assigns markers as a batch across all series and I'm wondering if there's... | <p>A for loop would be sufficient:</p>
<pre><code>df = pd.DataFrame(np.arange(16).reshape(4,-1))
for c,m in zip(df,'oxds'):
df[c].plot(marker=m)
plt.legend()
</code></pre>
<p>Output:</p>
<p><a href="https://i.stack.imgur.com/xMDCj.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/xMDCj.png" al... | python|pandas|matplotlib | 3 |
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