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 |
|---|---|---|---|---|---|---|
5,600 | 48,620,057 | Keras loss weights | <p>I've model with two output layers, age and gender prediction layers. I want to assign different weight values for each output layer's loss. I've the following line of code to do so.</p>
<pre><code>model.compile(loss=[losses.mean_squared_error,losses.categorical_crossentropy], optimizer='sgd',loss_weights=[1,10])
<... | <p>As stated in the book <a href="https://www.manning.com/books/deep-learning-with-python" rel="noreferrer">Deep Learning with Python</a> by François Chollet:</p>
<blockquote>
<p>The mean squared error (MSE) loss used for the age-regression task
typically takes a value around 3–5, whereas the crossentropy loss use... | python|tensorflow|machine-learning|keras | 7 |
5,601 | 48,465,333 | Pandas interpolation /MSE | <p>I know that there is an easy solution for my problem with pandas (hopefully) but I just don't know how to find it. Let's say I've two dataframes: </p>
<pre><code>df1 = pd.DataFrame({'x1': [1, 2, 3], 'y1': [1, 4, 9]})
df2 = pd.DataFrame({'x2': [1.5, 2, 3.1, 3.9], 'y2': [1, 3, 5.5, 8]})
</code></pre>
<p>I want to ca... | <p>If I understand correctly, you need to bring them to a common grid (abscissa) in order to perform the subtraction and the statistics on it.</p>
<pre><code>import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
df1 = pd.DataFrame({'x1': [1, 2, 3], 'y1': [1, 4, 9]})
df2 = pd.DataFrame({'x2': [1.5, 2... | python|pandas | 1 |
5,602 | 48,843,357 | python pandas - select particular values after groupby | <p>I have groupby table:</p>
<pre><code>df.groupby(['Age', 'Movie']).mean()
User Raitings
Age Movie
1 1 4.666667 7.666667
2 4.666667 8.000000
3 2.000000 7.500000
4 2.000000 5.500000
5 3.000000 7.000000
18 1 3.000000 7.500000
... | <p>This gets all of them in one go.</p>
<pre><code>df.groupby('Age').Raitings.idxmin().str[-1]
Age
1 4
18 1
25 4
45 4
60 1
Name: Raitings, dtype: int64
</code></pre>
<p>If you want a function, I'd use <a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.xs.html" rel="nofol... | python|pandas|pandas-groupby|multi-index | 4 |
5,603 | 48,454,312 | Python (Pandas) Error IndexError: single positional indexer is out-of-bounds | <p>Here is the error I can't seem to squash, dropping down my count to be one less than my actual rows fixes it, but that means it can't even read the last row. The error is coming from me attempting to parse data from my .csv I have saved in the same directory.</p>
<p>Here is the code that seems to be causing the iss... | <p>Forgot to add the header row when creating the csv on application start, that resolved all of it.</p>
<pre><code> writer.writeheader()
</code></pre>
<p>That's all it needed.</p> | python|python-3.x|pandas|runtime-error | 0 |
5,604 | 51,845,480 | Error when using tf.get_variable as alternativ for tf.Variable in Tensorflow | <p>Hi I'm new to neural networks and I'm currently working on Tensoflow.
First I did the MNIST tutorial which worked quite well. Now I wanted to deepen the whole by means of an own network for Cifar10 in Google Colab. For this purpose I wrote the following code:</p>
<pre><code>def conv2d(input, size, inputDim, outputC... | <p>I found my mistake.
I forgot the keyword <code>initializer</code>.</p>
<p>the correct line looks like this:</p>
<pre><code>weight = tf.get_variable("weight",initializer=tf.truncated_normal([size, size, inputDim, outputCount], stddev=anpassung))
</code></pre> | python|tensorflow|conv-neural-network|google-colaboratory | 1 |
5,605 | 41,971,322 | Pandas: How to select the minimum value of a series of rows grouped by a key | <p>Suppose I have the followin dataframe:</p>
<pre><code>Key | Amount | Term | Other | Other_2
----+--------+--------+-------+--------
A | 9999 | Short | ABC | 100
A | 261 | Short | ABC | 100
B | 281 | Long | CDE | 200
C | 140 | Long | EFG | 300
C | 9999 | Long | EFG | 300
</... | <p>To get the min value within each key, you can use <code>groupby.apply</code> to create a boolean Series where the min value takes true and other values take false; then you can use the boolean series for subsetting:</p>
<pre><code>df[df.Amount.groupby(df.Key).apply(lambda x: x == x.min())]
# Key Amount Term ... | python|pandas|data-cleaning | 1 |
5,606 | 41,864,024 | Why is pyplot.imshow() changing color channels in arrays | <p>For the life of me I have been struggling with this for the last 24 hours. I am doing a Neural Net--storing images in a 4D-array. The first index of the array is bascially the "sample" aka sample 1,2,3, etc. dimenions 2,3,4 are 128x128 x3 rgb pictures. Now in the process of this, i take the input pictures (which... | <p><code>OpenCV</code> stores color images using the <code>BGR</code> convention while
<code>matplotlib</code> uses the <code>RGB</code> convention.</p>
<p>You should simply flip the channels order when displaying the images using <code>pyplot</code>:</p>
<pre><code> plt.imshow(img[:,:,[2,1,0])
plt.imshow(train[0][:... | opencv|numpy|image-processing|matplotlib | 8 |
5,607 | 64,248,962 | How to calculate number of rows per group in pandas dataframe and add it to original data | <p>I have dataframe df like below</p>
<pre><code>ID COMMODITY_CODE DELIVERY_TYPE DAY Window_start case_qty deliveries.
6042.0 SCGR Live 1.0 15:00 15756.75 7.75
6042.0 SCGR Live 1.0 18:00 15787.75 5.75
6042.0 SCGR ... | <p>You are looking for a <code>transform</code>:</p>
<pre><code>df['window_count'] = df.groupby(['ID','CHAMBER_TYPE','COMMODITY_CODE','DELIVERY_TYPE','DAY'])['ID'].transform('size')
</code></pre>
<p>By the way, there is no <code>'CHAMBER_TYPE'</code> columns in your sample data.</p> | python|pandas|numpy|pandas-groupby|aggregate-functions | 0 |
5,608 | 64,370,308 | Create pandas MultiIndex DataFrame from multi dimensional np arrays | <p>I am trying to insert 72 matrixes with dimensions (24,12) from an np array into a preexisting MultiIndexDataFrame indexed according to a np.array with dimension (72,2). I don't care to index the content of the matrixes (24,12), I just need to index the 72 matrix even as objects for rearrangemnet purposes. It is like... | <p>After a bot of careful research, I found that my question <a href="https://stackoverflow.com/questions/48482256/what-is-the-pandas-panel-deprecation-warning-actually-recommending">has been already answered here</a> (the right answer) and <a href="https://stackoverflow.com/questions/43427189/3-dimensional-numpy-array... | pandas|dataframe|multi-index | 0 |
5,609 | 47,835,879 | What is different between RNN output and rule based output? | <p>I am new in machine learning and i got a question , I am <a href="https://towardsdatascience.com/lstm-by-example-using-tensorflow-feb0c1968537" rel="nofollow noreferrer">following this tutorial</a> , I read about LSTM and RNN. i use the code provided by tutorial and run it , it completed the training and now i gave ... | <p>Your training dataset only has ~180 words, and is achieving a 84.6% (training) accuracy, so it is overfitting quite a bit. Essentially, the model is simply predicting the next most likely word based on the training data.</p>
<p>Usually language models are trained on much larger datasets, such as PTB or the 1B word ... | machine-learning|tensorflow|deep-learning|artificial-intelligence|lstm | 0 |
5,610 | 49,135,821 | Pandas: How to add number of the row within grouped rows | <p>so I have DataFrame:</p>
<pre><code>>>> df2
text
0 0 a
0 1 b
0 2 c
0 3 d
1 4 e
1 5 f
1 6 g
2 7 h
2 8 1
</code></pre>
<p>How do I create another column, which contains counter for each row within an level=0 index?</p>
<p>I have tried the following code (i need to get df['co... | <p>I believe you're looking for <code>groupby</code> along the 0<sup>th</sup> column index + <code>cumcount</code>: </p>
<pre><code>df['counter'] = df.groupby(level=0).cumcount() + 1
df
text counter
0 0 a 1
1 b 2
2 c 3
3 d 4
1 4 e 1
5 f 2
6 ... | python|pandas|dataframe|group-by|pandas-groupby | 2 |
5,611 | 49,088,447 | Input pipeline using TensorFlow Dataset API and Pandas? | <p>I am trying to create a TensorFlow Dataset that takes in a list of path names for CSV files and create batches of training data. First I create a parse function which uses Pandas to read the first n rows. I give this function as argument for the 'map' method in Dataset</p>
<pre><code>def _get_data_for_dataset(file_... | <p>I finally got the answer from <a href="https://stackoverflow.com/questions/49116343/dataset-api-flat-map-method-producing-error-for-same-code-which-works-with-ma/49140725#49140725">Dataset API 'flat_map' method producing error for same code which works with 'map' method</a>
I am posting the full code... | pandas|tensorflow|tensorflow-datasets | 1 |
5,612 | 49,107,912 | How to use for loop on pandas.core.groupby.DataFrameGroupBy in reverse order? | <p>I have a <code>pandas.core.groupby.DataFrameGroupBy</code> object,iter_gb contains customer Itinerary. The sample data looks like. After Itinerary it contains 6 columns, those columns are not important. </p>
<pre><code>Customer_id YEAR Itinerary A1, B1, ...
38915672 2015 B12345
38915672 2012 B12345
3... | <p>It seems you need first reverse <code>DataFrame</code> and then loop:</p>
<pre><code>for i, x in df[::-1].groupby('Itinerary', sort=False):
print (x)
Customer_id YEAR Itinerary
16 38915672 2011 B86451
15 38915672 2012 B86451
14 38915672 2012 B86451
13 38915672 2012 B86451
12... | python|pandas | 2 |
5,613 | 49,057,107 | how to add headers to a numpy.ndarray | <p>I have a numpy.ndarray having dimensions of 23411 x 3.
I would like to add headers to the top of the matrix called: "summary", "age", and "label". In that order. </p>
<p>In:</p>
<pre><code>matrix.shape
</code></pre>
<p>Out:</p>
<pre><code>(23411L, 3L)
</code></pre>
<p>In:</p>
<pre><code>type(matrix)
</code></p... | <p>You can achieve this with <a href="https://pandas.pydata.org/" rel="nofollow noreferrer">pandas</a>.</p>
<pre><code>import pandas as pd
matrix = [...] # your ndarray
matrix = pd.DataFrame(data=matrix, columns=["summary", "age", "label"])
</code></pre> | python|numpy | 4 |
5,614 | 49,308,330 | get the same order after applying group by on data frame | <p>I've a situation where before applying a group by on a data frame, the order was in ascending, however after applying the data frame order gets changed and its changing internally.</p>
<p><strong>here is the code sample:</strong>
<strong>DataFrame:</strong> `</p>
<pre><code>final_day_wise = daily_sales_data.loc [ ... | <p>Is this what you are looking for?</p>
<pre><code>processed_df = df.groupby('col').agg(...).reset_index()
processed_df.sort_values(['col', 'Placement#'], ascending=True, inplace=True)
</code></pre> | python|python-2.7|pandas|pandas-groupby | 0 |
5,615 | 58,679,115 | Pandas - Modify by string values in each cell by using search | <p>I have pandas dataframe include a column a with content like this <code>'<152/abcx>' ,'<42/da>', '<2/kiw>'</code>. What I want to do is based on the content , remove "<",">", and create two new separate column like this column b : <code>152 ; 42; 2</code>, column c: <code>'abcx', 'da', 'kiw'</co... | <p>Try using this code:</p>
<pre><code>df = pd.DataFrame({'a': ['<152/abcx>' ,'<42/da>', '<2/kiw>']})
df = df['a'].str.strip('<>').str.split('/', expand=True)
df.columns = ['columnb', 'columnc']
print(df)
</code></pre>
<p>Output:</p>
<pre><code> columnb columnc
0 152 abcx
1 42 ... | string|pandas | 2 |
5,616 | 58,868,775 | Can't initialize Bokeh dashboard with Select widget | <p>This is my first Bokeh experience, so I apologize if I'm asking a very silly question here. So, here's the code. I have a dataframe that contains scores for several individuals. I want to plot a time series line with years on the x-axis, and scores on y-axis and a select menu to pick a player. </p>
<p>This is what... | <p>Using <em>real Python callbacks</em>, as you have done above, requires running your code as an application on a Bokeh server. That is because web browsers have no knowledge of, nor ability to run, Python code. Real Python callbacks imply there is some actually running Python process that can run the Python callback ... | python|pandas|bokeh | 1 |
5,617 | 58,651,762 | Summarize a column if values in another column are inferior (without for loop) | <h2>The dataframe</h2>
<p>I have dataframe with many items.</p>
<p>The items are identified by a code "Type" and by a weight.</p>
<p>The last column indicates the quantity.</p>
<pre><code>|-|------|------|---------|
| | type |weight|quantity |
|-|------|------|---------|
|0|100010| 3 | 456 |
|1|100010| 1 ... | <p>First sort your values by <code>weight</code> and <code>type</code>, then do a <code>groupby</code> for <code>cumsum</code>, and finally do a merge on index:</p>
<pre><code>df = pd.DataFrame([[100010, 3, 456],[100010, 1, 159],[100010, 5, 735], [100024, 3, 153], [100024, 7, 175], [100024, 1, 759]],columns = ["type",... | pandas|dataframe|pandas-groupby | 2 |
5,618 | 58,741,434 | Filter one data frame based on other data frame in pandas | <p>I have two DataFrames in pandas:</p>
<pre><code>import pandas as pd
df1 = pd.DataFrame({'Name': ["A", "B", "C", "C","D","D","E"],
'start': [50, 124, 1, 159, 12, 26,110],
'stop': [60, 200, 19, 200, 24, 30,160]})
df2 = pd.DataFrame({'Name': ["B", "C","D","E"],
... | <p>To solve your problem, I applied an SQL-like way that mimics the following query:</p>
<pre class="lang-sql prettyprint-override"><code>SELECT
df.Name, df.start_x AS start, df.stop_x AS stop
FROM (
SELECT
df1.Name, df1.start AS start_x, df1.stop AS stop_x,
df2.start AS start_y, df2.stop AS stop... | python|pandas|dataframe | 1 |
5,619 | 58,705,732 | How to fill empty cell value in pandas with condition | <p>My sample dataset is as below. Actuall data till 2020 is available.</p>
<pre><code> Item Year Amount final_sales
A1 2016 123 400
A2 2016 23 40
A3 2016 6
A4 2016 10 100
A5 2016 5 200
A1 2017 123 400
A2 2017 2... | <p>It looks like you want to fill forward where there is missing data.</p>
<p>You can do this with 'fillna', which is available on pd.DataFrame objects.</p>
<p>In your case, you only want to fill forward for each item, so first group by item, and then use fillna. The method 'pad' just carries forward in order (hence ... | python-3.x|pandas|dataframe|row | 2 |
5,620 | 70,277,650 | Is there any other way (to combine values of one column into different groups), instead of using 'df.replace( )' several times in the below problem? | <p>In :
char_df['Loan_Title'].unique()</p>
<p>Out:
array(['debt consolidation', 'credit card refinancing',
'home improvement', 'credit consolidation', 'green loan', 'other',
'moving and relocation', 'credit cards', 'medical expenses',
'refinance', 'credit card consolidation', 'lending club',
'debt consolidation loan', ... | <p>IIUC (it is hard to read) you could try the following:</p>
<pre class="lang-py prettyprint-override"><code>import pandas as pd
# will use regex pattern as keys and replace string as value
patterns = {
r'dept consolidation.*': 'dept_consolidation',
r'personal.*': 'personal_loan',
r'credit card.*': 'credi... | python|pandas|machine-learning|data-science|feature-engineering | 2 |
5,621 | 70,123,729 | Add hours to year-month-day data in pandas data frame | <p>I have the following data frame with hourly resolution</p>
<pre><code>day_ahead_DK1
Out[27]:
DateStamp DK1
0 2017-01-01 20.96
1 2017-01-01 20.90
2 2017-01-01 18.13
3 2017-01-01 16.03
4 2017-01-01 16.43
... ...
8756 2017-12-31 25.56
8757 2017-12-31 11.02
8758 2017-12-31 7.... | <p>Use <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.cumcount.html" rel="nofollow noreferrer"><code>GroupBy.cumcount</code></a> for counter with <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_timedelta.html" rel="nofollow noreferrer"><code>t... | python|pandas|dataframe | 1 |
5,622 | 70,058,518 | InvalidArgumentError: Incompatible shapes: [29] vs. [29,7,7,2] | <p>so I'm new right here and in Python also. I'm trying to make my own network. I found some pictures of docs and cats 15x15 and unfortunatly couldn't make this basic network...</p>
<p>So, these are libraries which I'm using</p>
<pre><code> from tensorflow.keras.models import Sequential
from tensorflow.keras imp... | <p>You need to <strong>flatten</strong> your prediction somewhere, otherwise you are outputing an image (29 samples of size 7x7 with 2 channels), while you simply want a flat 2 dimensional logits (so shape 29x2). The architecture you are using is somewhat odd, did you mean to have flattening operation <strong>before</s... | tensorflow|keras|neural-network|google-colaboratory | 0 |
5,623 | 56,318,354 | TensorFlow: Why should my image tensor shape equal my label tensor shape? | <p>I wish to do semantic segmentation using TensorFlow 1.12. I create a dataset using <code>from_generator()</code>, where my generator is as follows:</p>
<pre><code>def train_sample_fetcher():
return sample_fetcher()
def val_sample_fetcher():
return sample_fetcher(is_validations=True)
def sample_fetcher(is_... | <p><code>tf.stack</code> operation tries to merge N rank K tensors into one rank (K+1) tensors. In other words, it tries to join a sequence of tensors along new axis and therefore, other axis of tensors should be the same. </p>
<p>Can simply return a pair <code>yield rgb, onehots</code> out of your generator.</p> | python|tensorflow|dataset|tensor | 1 |
5,624 | 56,246,970 | How to apply a binomial low pass filter to data in a NumPy array? | <p>I'm supposed to apply a "binomial low pass filter" to data given in a NumPy <code>numpy.ndarray</code>.</p>
<p>However, I wasn't able to find anything of the sort at <a href="https://docs.scipy.org/doc/scipy/reference/signal.html" rel="nofollow noreferrer">https://docs.scipy.org/doc/scipy/reference/signal.html</a> ... | <p>A binomial filter is a <a href="https://en.wikipedia.org/wiki/Finite_impulse_response" rel="nofollow noreferrer">FIR filter</a> whose coefficients can be generated by taking a row from <a href="https://en.wikipedia.org/wiki/Pascal%27s_triangle" rel="nofollow noreferrer">Pascal's triangle</a>. A quick way ("quick" a... | python|numpy|filter|scipy|binomial-coefficients | 4 |
5,625 | 55,683,523 | separating a .txt file into two columns using pandas | <p>I have a text file of a script and is ordered like this:</p>
<pre><code>0 "character one" "dialogue for character one."
1 "character two" "dialogue for character two."
2 "character one" "dialogue for character one again"
...
etc
</code></pre>
<p>My problem is that I want to analyze this text and need it to be in ... | <p>I believe you need <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html" rel="nofollow noreferrer"><code>read_csv</code></a> with parameters <code>sep</code> and <code>names</code> for new columns names, because in <code>pandas 0.24.2</code> get:</p>
<blockquote>
<p>FutureWarnin... | pandas|csv|export-to-csv|data-cleaning | 2 |
5,626 | 55,878,165 | create 2 lines plot by dates of Trump's tweets | <p>I have series of Trump's tweets sources by date:</p>
<pre><code>trump_tweets_sources.groupby([' created_at ', ' source '])[' source '].count()
</code></pre>
<p><a href="https://i.stack.imgur.com/y12Cf.png" rel="nofollow noreferrer">enter image description here</a></p>
<p>I want to create 2 lines plot descri... | <p>Use <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.unstack.html" rel="nofollow noreferrer"><code>Series.unstack</code></a> for <code>DataFrame</code> and then use <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html" rel="nofollow noreferrer"... | python|pandas|dataframe|matplotlib | 0 |
5,627 | 55,990,477 | Creating a DataFrame by passing a NumPy array, with a datetime index and labeled columns: | <p>On running a code, getting a following error:-</p>
<p><strong>'list' object is not callable</strong></p>
<pre><code>dates = pd.date_range('20190101', periods=6)
dates
df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD'))
</code></pre>
<p>Expected output:-</p>
<pre><code>In [8]: df
Out[8]: ... | <p>Your code is perfectly correct. If you execute it right after restarting your kernel it is bound to run. The error you are getting refers to the <code>list</code> function you have used to convert <code>string</code> 'ABCD' to list. Most likely, you have used list as an <code>object</code> name somewhere else, and t... | python|pandas | 0 |
5,628 | 64,744,068 | Remove row from arbitrary dimension in numpy | <p>I have a function, <code>remrow</code> which takes as input an arbitrary numpy nd array, <code>arr</code>, and an integer, <code>n</code>. My function should remove the last row from <code>arr</code> in the <code>n</code>th dimension. For example, if call my function like so:</p>
<p><code>remrow(arr,2)</code></p>
<p... | <p>Construct an indexing tuple, consisting of the desired combination of slice(None) and slice(None,-1) objects.</p>
<pre><code>In [75]: arr = np.arange(24).reshape(2,3,4)
In [76]: idx = [slice(None) for _ in arr.shape]
In [77]: idx
Out[77]: [slice(None, None, None), slice(None, None, None), slice(None, None, None)]
In... | python|numpy|indexing|numpy-ndarray | 2 |
5,629 | 64,940,267 | How to access specific rows in a pandas multiindex dataframe | <p>I'm working with what I think is a pandas multi-index data frame. Is there any way to make sure? My data looks like this.</p>
<pre><code> cinc Outcome
Side 1 2 1 2
WarNum
1 0.146344 0.029989 1 2
4 0.152565 0.056853 ... | <p>To check if your df has <a href="https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html" rel="nofollow noreferrer"><code>Multiindex</code></a>, you can do this:</p>
<pre><code>isinstance(war_cinc.index, pd.MultiIndex)
</code></pre>
<p>This will return <code>True</code>.</p>
<p>To check for <code>hiera... | python|python-3.x|pandas|dataframe | 1 |
5,630 | 64,952,877 | How can I add comma into existing value in DataFrame? | <p>I scraped a DataFrame from one website but during scraping I lost comma in values, so it looks like below:</p>
<pre><code>name price
x 100
y 89
z 123584
</code></pre>
<p>Now I have to modify values in column "price" by adding comma in each value on the second place counting by right. T... | <p>We can slice your string and add the commas:</p>
<pre><code>df['price'].str[:-2] + ',' + df['price'].str[-2:]
</code></pre>
<pre><code>0 1,00
1 ,89
2 1235,84
Name: price, dtype: object
</code></pre>
<hr />
<p>Or we can use <code>str.cat</code> with the <code>sep</code> argument:</p>
<pre><code>df['pr... | python|python-3.x|pandas|dataframe|number-formatting | 1 |
5,631 | 64,984,092 | Pandas Dataframe calculate Time difference for each group and Time difference between two different groups | <p>I have created a dataframe like that:</p>
<pre><code>import pandas as pd
d = {'Time': ['01.07.2019, 06:21:33', '01.07.2019, 06:32:01', '01.07.2019, 06:57:33', '01.07.2019, 07:24:33','01.07.2019, 08:26:25', '01.07.2019, 09:12:44']
,'Action': ['Opened', 'Closed', 'Opened', 'Closed', 'Opened', 'Closed']
,'Nam... | <p>Start by making datetime from string, then some groupbys and diffs:</p>
<pre><code>df["Time"] = pd.to_datetime(df["Time"])
df["d1"] = df.groupby("Name")["Time"].diff().shift(-1).fillna("")
df["d2"] = (
df.groupby((df["Action"] == &qu... | python|pandas|dataframe | 3 |
5,632 | 64,827,154 | Python/Pandas time series correlation on values vs differences | <p>I am familiar with Pandas Series corr function to compute the correlation between two Series, so example:</p>
<pre><code>import pandas as pd
import numpy as np
A = pd.Series(np.random.randint(1,101,50))
B = pd.Series(np.random.randint(1,101,50))
A.corr(B)
</code></pre>
<p>This willl compute the correlation in the ... | <p>I guess the more pythonic way, through pandas, would be to use <code>df.pct_change()</code>:</p>
<p>Suppose A and B are time series:</p>
<pre><code>A.pct_change().corr(B.pct_change())
</code></pre> | python|pandas|numpy|statistics|correlation | 1 |
5,633 | 64,667,734 | Pretty print a dictionary matrix in Python | <p>Giving a dictionary of dictionary:</p>
<pre><code>dict_2d = collections.defaultdict(dict)
dict_2d['A']['A'] = 1
dict_2d['A']['B'] = 0.19
...
dict_2d['Z']['A'] = 0.76
...
dict_2d['Z']['Z'] = 1
</code></pre>
<p>Could you advise an elegant way to print such a square matrix?</p>
<p>For instance:</p>
<ol>
<li>is there a... | <p>Not sure exactly how your print should look like however I tried writing some code. I would recommend using <code>tabulate</code> or <code>pandas</code> as it makes it way easier.</p>
<pre><code>df = pd.DataFrame(dict_2d)
print(df)
# A Z
# A 1.00 0.76
# B 0.19 NaN
# Z NaN 1.00
</code></pre>
<p>I w... | python|pandas|dictionary | 1 |
5,634 | 64,847,271 | Use two columns as inputs - Pandas | <p>I'm trying to create a new column that comes from the calculation of two columns. Usually when I need to do this but with only one column I use <code>.apply()</code> but now with two parameters I don't know how to do it.</p>
<p>With one I do the following code:</p>
<pre><code>from pandas import read_csv, DataFrame
... | <p>Disclaimer: avoid <code>apply</code> if possible. With that in mind, you are looking for <code>axis=1</code>, but you need to rewrite the function like:</p>
<pre><code>df['new'] = df.apply(lambda x: myFunc(x['colA'], x['colB']),
axis=1)
</code></pre>
<p>which is essentially equivalent to:</p>
<... | python|pandas | 2 |
5,635 | 40,033,190 | Pandas Grouping - Values as Percent of Grouped Totals Not Working | <p>Using a data frame and pandas, I am trying to figure out what each value is as a percentage of the grand total for the "group by" category</p>
<p>So, using the tips database, I want to see, for each sex/smoker, what the proportion of the total bill is for female smoker / all female and for female non smoker / all f... | <p>You can add another grouped by process after you get the <code>sum</code> table to calculate the percentage:</p>
<pre><code>(df.groupby(['sex', 'smoker'])['total_bill'].sum()
.groupby(level = 0).transform(lambda x: x/x.sum())) # group by sex and calculate percentage
#sex smoker
#Female No 0.622350... | python|pandas|dataframe|aggregate|aggregation | 10 |
5,636 | 39,998,594 | Many small data table I/O for pandas? | <p>I have many table (about 200K of them) each small (typically less than 1K rows and 10 columns) that I need to read as fast as possible in pandas. The use case is fairly typical: a function loads these table one at a time, computes something on them and stores the final result (not keeping the content of the table in... | <p>IMO there are a few options in this case:</p>
<ol>
<li><p>use HDF Store (AKA PyTable, H5) as <a href="https://stackoverflow.com/questions/39998594/many-small-data-table-i-o-for-pandas#comment67274829_39998594">@jezrael has already suggested</a>. You can decide whether you want to group some/all of your tables and s... | pandas|io | 1 |
5,637 | 44,339,072 | Difference between initializing a Tensorflow Variable using np.zeros and using tf.zeros | <p>Are there some differences between initializing a tensorflow Variable using np.zeros and using tf.zeros?</p>
<p>For example, if we take a look at the MNIST softmax tutorial (
<a href="https://github.com/tensorflow/tensorflow/blob/r1.1/tensorflow/examples/tutorials/mnist/mnist_softmax.py" rel="nofollow noreferrer">h... | <p>For most experiments it almost does not matter. You can also provide python list of lists <code>[[0, 0, ...], ...]</code>. The difference you see in your <code>eval</code> is because <a href="https://www.tensorflow.org/api_docs/python/tf/zeros" rel="nofollow noreferrer">tf.zeros</a> by default uses <code>float32</co... | variables|tensorflow|initialization | 2 |
5,638 | 69,616,471 | Hugginface Bert Tokenizer build from source due to proxy issues | <p>I encountered something similar to this: <a href="https://stackoverflow.com/questions/59701981/bert-tokenizer-model-download">BERT tokenizer & model download</a></p>
<p>The link above is about downloading the Bert model itself, but I would only like to use the Bert Tokenizer.</p>
<p>Normally I could do it like t... | <p>You would need to 1) download vocabulary and configuration files (<code>vocab.txt</code>, <code>config.json</code>), 2) put them into a folder and 3) pass folder's path to <code>BertTokenizer.from_pretrained(<path>)</code> function.</p>
<p>Here is the download location of <code>vocab.txt</code> for different t... | python|tokenize|huggingface-transformers | 2 |
5,639 | 69,660,556 | pd.DataFrame cuts the decimals' mantissa | <p>Need to convert list of values with long mantissa into pd.DataFrame without precision lost:</p>
<p><a href="https://i.stack.imgur.com/Nl05Q.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/Nl05Q.png" alt="enter image description here" /></a></p>
<p>My list of decimals:</p>
<p><a href="https://i.sta... | <p>The values are stored with the correct precision, they are just truncated when you display them.</p>
<p>You can change the precision used by pandas when displaying datarames by using <code>pd.set_option('display.precision', num_decimal_digits)</code>.</p>
<p>For example</p>
<pre><code>df = pd.DataFrame([0.123456789]... | pandas|dataframe|decimal|mantissa | 0 |
5,640 | 69,561,023 | Combine value strings of two dataframes in corresponding locations within each dataframe | <p>I am looking for python code to paste value strings of two dataframes with identical column names, dimensions and datatype (Strings).</p>
<p>I want to create a new dataframe by pasting each element of DF1 & DF2 together which is in the corresponding location within each dataframe and have a "," delimit... | <p>Use <code>pd.concat</code>:</p>
<pre><code>out = pd.concat([df1.stack(), df2.stack()], axis=1) \
.apply(lambda x: ','.join(x), axis=1) \
.unstack()
</code></pre>
<p>Output:</p>
<pre><code>>>> out
A B C
0 z,f x,h c,j
1 v,t b,y n,u
2 a,u s,i d,o
</code></pre> | python|pandas|dataframe | 2 |
5,641 | 41,067,917 | Tensorflow: InvalidArgumentError for placeholder | <p>I tried to run the following code in Jupyter Notebook, however I got the <code>InvalidArgumentError</code> for the <code>placeholder</code>.</p>
<p>But when I wrote a Python script and ran it in command window, it worked. I want to know how can I ran my code in the Notebook successfully, thanks.</p>
<ul>
<li>OS: U... | <p>Your <code>raw_data</code> is float64 (default numpy float type) whereas your placeholder is float32 (default tensorflow float type). You should explicitly cast your data to float32</p> | tensorflow | 1 |
5,642 | 54,125,497 | Expected 2D array, got scalar array instead error in pandas regression | <p>I'm taking an error when i try to calculate regression in pandas. Here is the code:</p>
<pre><code>import pandas as pd
import matplotlib.pyplot as plt
df=pd.DataFrame({"haftalar":[1,2,3,4,5,6,7],
"degerler":[6.11,5.66,5.30,5.32,5.25,5.37,5.28]})
haftalar=df[['haftalar']]
degerler=df[['degerler']]
... | <p>Try:</p>
<pre><code>tahmin=lr.predict([[8]])
</code></pre>
<p>More often, you may have data in <code>numpy</code> arrays like so:</p>
<pre><code>import numpy as np
x_test = np.array([8])
</code></pre>
<p>Now the error message tells you what to do:</p>
<pre><code>tahmin=lr.predict(x_test.reshape(-1, 1))
</code><... | python|python-3.x|pandas|scikit-learn|sklearn-pandas | 0 |
5,643 | 53,869,191 | When converting to CSV UTF-8 with python, my date columns are being turned into date-time | <p>When I run the following code </p>
<pre><code>import glob,os
import pandas as pd
dirpath = os.getcwd()
inputdirectory = dirpath
for xls_file in glob.glob(os.path.join(inputdirectory,"*.xls*")):
data_xls = pd.read_excel(xls_file, sheet_name=0, index_col=None)
csv_file = os.path.splitext(xls_file)[0]+"... | <p>Nothing is "going wrong" per se. You simply need to provide a custom <code>date_format</code> to <a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_csv.html" rel="nofollow noreferrer"><code>df.to_csv</code></a>:</p>
<blockquote>
<p>date_format : string, default None
... | python|excel|pandas | 5 |
5,644 | 53,932,989 | Output file for every input file in a folder | <pre><code>import pandas as pd
import glob
import csv
files=glob.glob('*.csv')
for file in files:
df=pd.read_csv(file)
a=sum(df.iloc[: , 0])
with open ('output.txt','w') as f:
f.write("sum of the first column is "+ str(a))
f.close()
</code></pre>
<p>I would like to write output files for ev... | <p>You can create a separate output file for each input file and prefix/suffix it with the name of the input file - </p>
<pre><code>import pandas as pd
import glob
import csv
files=glob.glob('*.csv')
for file in files:
df=pd.read_csv(file)
a=sum(df.iloc[: , 0])
output_file_name = "output_" + file
with ... | python|python-3.x|pandas | 0 |
5,645 | 66,269,739 | De-Duplicate in Pandas based off of multiple rules | <p>I want to de-dupe rows in pandas based off of multiple criteria.</p>
<p>I have 3 columns: name, id and nick_name.</p>
<p>First rule is look for duplicate id's. When id's match, only keep rows where name and nick_name are different as long as I am keeping at least one row.</p>
<p>In other words, if name and nick_name... | <p>We can split this into 3 boolean condtions to filter your initial dataframe by.</p>
<pre><code>#where name and nick_name match, keep the first value.
con1 = df.duplicated(subset=['name','nick_name'],keep='first')
# where ids are duplicated and name is not equal to nick_name
con2 = df.duplicated(subset=['id'],keep... | python|pandas | 1 |
5,646 | 66,198,363 | Extract first column and last column of a Numpy array and populate on one dimension array | <p>I am a novice on Numpy.</p>
<p>I have extracted a CSV file and with the condition to populate the array below</p>
<pre class="lang-py prettyprint-override"><code>data = [(2006, 'Total Live-Births', 38317) (2007, 'Total Live-Births', 39490)
(2008, 'Total Live-Births', 39826) (2009, 'Total Live-Births', 39570)
(2010... | <p>Given your data as follows:
Just sticking to <code>numpy</code> you could do something like:</p>
<pre class="lang-py prettyprint-override"><code>import numpy as np
data = np.genfromtxt('live-births.csv', delimiter=',', skip_header=1, dtype=str)
# keep only the 'Total Live-Births' values
live_births = data[data[:, 1... | numpy | 2 |
5,647 | 65,954,374 | pandas mapping list to dict items for new column | <p>i have df like:</p>
<pre><code>col_A
[1,2,3]
[2,3]
[1,3]
</code></pre>
<p>and dict like:</p>
<pre><code>dd = {1: "Soccer", 2: "Cricket", 3: "Hockey"}
</code></pre>
<p>how can i create a new column col_B like:</p>
<pre><code>col_A col_B
[1,2,3] ["Soccer", "Cricket&... | <p>You can use list comprehension with <code>if</code> for filter out not matched values:</p>
<pre><code>df['sports'] = df['col_A'].map(lambda x: [dd[y] for y in x if y in dd])
</code></pre>
<p>Or replace to <code>None</code> if no match:</p>
<pre><code>df['sports'] = df['col_A'].map(lambda x: [dd.get(y, None) for y in... | python|pandas|list|dictionary | 3 |
5,648 | 52,871,044 | Count duplicates in rows per column in pandas DataFrame | <p>I have a very long table like below:</p>
<pre><code> A B C D .......
0 au br gt uy
1 cd gq gt uy
2 fg br gt ml
3 kl br gt wx
</code></pre>
<p>..............</p>
<p>I would like to count and to print duplicates per column like:</p>
<pre><code>A 0
B 2
C ... | <p>Subtract length of DataFrame with <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.nunique.html" rel="noreferrer"><code>nunique</code></a>:</p>
<pre><code>df = len(df) - df.nunique()
print (df)
A 0
B 2
C 3
D 1
dtype: int64
</code></pre>
<p>Or use<a href="http://pandas.pyd... | python|pandas|dataframe|duplicates | 5 |
5,649 | 52,484,199 | Series' object has no attribute 'decode in pandas | <p>I am trying to decode utf-8 encoded text in python. The data is loaded to a pandas data frame and then I decode. This produces an error: <strong>AttributeError: 'Series' object has no attribute 'decode'</strong>. How can I properly decode the text that is in pandas column? </p>
<pre><code>>> preparedData.head... | <p>I could be wrong but I would guess that what you have are byte strings rather than strings of bytes strings <code>b"XXXXX"</code> instead of <code>"b'XXXXX'"</code> as you've posted in your answer in which case you could do the following (you need to use the string accessor):</p>
<pre><code>preparedData['text'] = p... | python|pandas | 7 |
5,650 | 52,736,221 | Pandas NaN value causing trouble when change values depending on other columns | <p>why do pandas NaN values sometime typed as numpy.float64, and sometimes float?
This is so confusing when I want to use function and change values in a dataframe depending on other columns</p>
<p>example:</p>
<pre><code> A B C
0 1 NaN d
1 2 a s
2 2 b s
3 3 c NaN
</code></pre>
<p>I h... | <p>Use <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html" rel="nofollow noreferrer"><code>numpy.where</code></a> or <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.loc.html" rel="nofollow noreferrer"><code>loc</code></a>, for check missing values is used spec... | python|pandas|numpy | 2 |
5,651 | 46,330,595 | Build a dictionary from two DataFrame columns | <p>How to build up a dictionary with a Dataframe.</p>
<p><code>df</code></p>
<pre><code> Vendor Project
0 John Cordoba
1 Saul Pampa
2 Peter La Plata
3 John Federal District
4 Lukas Salta
5 Thomas Rio Grande
6 Peter Rio Salado
7 Pe... | <p>Use</p>
<pre><code>In [1893]: df.groupby('Vendor')['Project'].apply(list).to_dict()
Out[1893]:
{'John': ['Cordoba', 'Federal District'],
'Lukas': ['Salta'],
'Peter': ['La Plata', 'Rio Salado', 'Bahia Blanca'],
'Saul': ['Pampa'],
'Thomas': ['Rio Grande']}
</code></pre>
<p>Or,</p>
<pre><code>In [1900]: {k: g['P... | python|pandas|dictionary|dataframe | 5 |
5,652 | 58,311,504 | filter DataFrame leaving rows where Needle is part of the list present in columnB | <p>I am looking for a filter function on my DataFrame to find rows where my searchTerm is in the list that is in the dataframe row.</p>
<p>I've been digging trough list related filters, they all seem to have a 'list' as needles and a single value in the dataFrame column.</p>
<pre class="lang-python prettyprint-overri... | <p><a href="https://stackoverflow.com/questions/32280556/how-to-filter-a-dataframe-column-of-lists-for-those-that-contain-a-certain-item">How to filter a DataFrame column of lists for those that contain a certain item</a></p>
<p>contains the solution:</p>
<pre><code>df[df['members'].apply(lambda x: 'b' in x)]
</code>... | python|pandas | 0 |
5,653 | 58,460,285 | Why is df.cumsum() giving ValueError: Wrong number of items passed, placement implies 1 | <p>I would like to create a new column called total_amount based on the sum of each amount in each group. I would like the final data set to look like the set below. </p>
<p>company | amount | total_amount</p>
<p>company 1 | 10000 | 10000</p>
<p>company 1 | 20000 | 30000</p>
<p>company 1 | ... | <p>It indicates <code>cumsum</code> returns more than 1 columns. In other words, <code>df.groupby('company').cumsum()</code> is calling <code>cumsum</code> on <code>DataFrameGroupby</code> object, so it returns a dataframe. If the returned dataframe is only 1 column, the assignment still works. However, if the returned... | python|pandas|cumsum | 0 |
5,654 | 69,223,955 | Building a Neural Network for Binary Classification on Top of Pre-Trained Embeddings Not Working? | <p>I am trying to build a Neural Network on top of the embeddings that a pre-trained model outputs. In specific: I have the logits of a base model saved to disk where each example is an array of shape 512 (which originally corresponds to an image) with an associated label (0 or 1). This is what I am doing right now:</p... | <p>You are only printing from the second iteration. The above will effectively print for every <code>200k+1</code> steps, but <code>i</code> starts at <code>0</code></p>
<pre><code>if i % 2000 == 1: # print every 2000 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
... | python|machine-learning|deep-learning|neural-network|pytorch | 0 |
5,655 | 69,102,683 | Python - Join two dataframes based on closest date match and additional column | <p>I have two dataframes that I need to join on two columns, one of which is a date column. However, the dates do not match as shown in below example. I have seen people on other posts using merge_of for similar examples however that will not work here I believe as I also need to join on another non-date column(pty).I ... | <p>My output is a bit different in <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.merge_asof.html" rel="nofollow noreferrer"><code>merge_asof</code></a> method:</p>
<pre><code>df1['alert_dt'] = pd.to_datetime(df1['alert_dt'], dayfirst=True)
df2['inv_dt'] = pd.to_datetime(df2['inv_dt'], dayfir... | python|pandas | 2 |
5,656 | 69,170,906 | Convert a string column to period in pandas preserving the string | <p>I would like to understand if I can convert a <em>string</em> column to a <strong>PeriodIndex</strong> (for instance year), preserving the string (suffix).</p>
<p>I have the following DataFrame:</p>
<pre><code>company date ... revenue taxes
Facebook 2017-01... | <p>Let's say there is a dummy dataframe, similiar with yours:</p>
<pre><code>dictionary = {'company' : ['Facebook', 'Facebook', 'Facebook_Total','Google','Google_Total'],
'date' : ['2019-09-14 09:00:08.279000+09:00 Total',
'2020-09-14 09:00:08.279000+09:00 Total',
... | python|pandas|dataframe|datetime|period | 2 |
5,657 | 68,883,614 | Keras Captcha OCR - How to pass single jpeg image to loaded (trained) model and receive prediction in string? | <p>for the past several hours I was looking all over the internet for an answer to how I can pass a single jpeg image into my pre-trained model (saved and loaded) and receive prediction in string format.</p>
<p>I am using Captcha OCR from this source - <a href="https://keras.io/examples/vision/captcha_ocr/" rel="nofoll... | <p>From the last code snippet and "Since the model is expecting two values (while training we were passing dict with image and image name (answer to captcha)). But now, I would like this model to actually predict the image so I am not able to pass answer/label." statement you have made.</p>
<p>you are trying ... | python|tensorflow|keras|ocr|captcha | 0 |
5,658 | 61,150,879 | Mapping a Dataframe into another using conditionals | <p>I would like to map one dataframe into another, though it is not so simple because I am using 2 conditions to execute the mapping - I will explain them below. Basically, what I am trying to do is given two dataframes, df1 and df2, such that:</p>
<p>df1:</p>
<pre><code>A B Type
Heart Spades Boo
Hea... | <p>Try <code>melt</code> and <code>drop_duplicates</code> on <code>df2</code>. Finally, left <code>merge</code> df1 to the result of <code>melt</code> and <code>drop_duplicates</code></p>
<pre><code>df_final = (df1.merge(df2.melt(['A','B'], var_name='Type', value_name='Verification')
.drop_dup... | python|pandas|dataframe | 2 |
5,659 | 61,072,251 | How can I find Feature importance from sklearn api, after I have converted my categorical variables into dummy variables? | <p>After we have converted our categorical variables to dummy variables for training the model. We tend to find feature importance. But sklearn's model.feature_importance_ object returns feature impotance for every dummy variable, rather than the original categorical variable. How to fix this?</p> | <p>Because the dummy variables are used to train the model, you cannot find the importance of the original categorical variable. It is mathematically impossible thing.</p> | python|pandas|machine-learning|scikit-learn|data-science | 0 |
5,660 | 71,517,905 | Merge with replacement of smaller dataframe to larger dataframe | <p>I have two DataFrames that look like this:</p>
<p>DF1:</p>
<pre><code>index colA colB
id1 0 0
id2 0 0
id3 0 0
id4 0 0
id5 0 0
</code></pre>
<p>DF2:</p>
<pre><code>index colA colB
id3 A3 B3
id4 A4 B4
id6 A6 B6
</code></pre>
<p>I want to infuse val... | <p>Here's one way using <code>concat</code> + <code>drop_duplicates</code>:</p>
<pre><code>out = pd.concat((df1, df2)).reset_index().drop_duplicates(subset=['index'], keep='last').set_index('index').sort_index()
</code></pre>
<p>or use <code>reindex</code> + <code>update</code>:</p>
<pre><code>df1 = df1.reindex(df1.ind... | python|pandas|dataframe|merge | 2 |
5,661 | 71,645,121 | Pandas replace change so values to NaN | <p>I have the the following dataframe:</p>
<pre><code># dictionary with list object in values
details = {
'Name' : ['D', 'C', 'F', 'G','A','N'],
'values' : ['21%','45%','10%',12,14,15],
}
df = pd.DataFrame(details)
</code></pre>
<p>The column value has values in %, however, some were originally saves as string... | <p>There are numeric values, so if use <code>str</code> function get missing values for numeric, possible solution is use <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.replace.html" rel="nofollow noreferrer"><code>Series.replace</code></a> with <code>regex=True</code> for replace by s... | python|pandas|replace|nan | 1 |
5,662 | 71,688,065 | Generic requirements.txt for TensorFlow on both Apple M1 and other devices | <p>I have a new MacBook with the Apple M1 chipset. To install tensorflow, I follow the instructions <a href="https://developer.apple.com/metal/tensorflow-plugin/" rel="noreferrer">here</a>, i.e., installing <code>tensorflow-metal</code> and <code>tensorflow-macos</code> instead of the normal <code>tensorflow</code> pac... | <p>According to this post <a href="https://stackoverflow.com/questions/29222269/is-there-a-way-to-have-a-conditional-requirements-txt-file-for-my-python-applica">Is there a way to have a conditional requirements.txt file for my Python application based on platform?</a></p>
<p>You can use conditionals on your requiremen... | python|macos|tensorflow|apple-m1|requirements.txt | 2 |
5,663 | 71,669,501 | Having trouble calculating mean squared error in sklearn python | <p>I am trying to fit a decision tree regressor to a dataset, and it is working but when I test it out by calculating mean squared error. I get an error that looks like this:</p>
<pre><code>msee = mse(x_test, y_test)
ValueError Traceback (most recent call last)
~\AppData\Local\Temp/ipyke... | <p>from the <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html" rel="nofollow noreferrer">documentation</a>:</p>
<blockquote>
<p>Parameters y_true array-like of shape (n_samples,) or (n_samples,
n_outputs) Ground truth (correct) target values.</p>
<p>y_pred array-like of ... | python|numpy|scikit-learn|decision-tree | 0 |
5,664 | 71,727,478 | Trying to plot a graph with Pandas but only wish to display data from 1 row of a csv between two dates | <p>I have a CSV file of weather data.</p>
<p>I have the file indexed by date (e.g., the date of the reading).</p>
<p>One of my columns is 'Humidity' and contains humidity data.</p>
<p>I wish to use the .plot function but I wish to limit the data set to between two dates.</p>
<p>To discriminate by time I used this to vi... | <p>What about</p>
<pre><code>london[london.loc[start_date : end_date]]['Mean Humidity'].plot(grid=True, figsize=(10,5))
</code></pre> | python|pandas|csv | 0 |
5,665 | 71,629,686 | Python - how to add another "column" to numpy ndarray | <p>I have a 34000,18 dimension numpy array and I have a 34000,1 array that needs to be appended to the first one at the end.</p>
<p><code>X_train.shape</code>
=(34189, 18)</p>
<p><code>type(X_train)</code>
= numpy.ndarray</p>
<p><code>y_train.shape</code>
= (34189,)</p>
<p>my attempt:</p>
<p>Data = np.append(X_train,y_... | <p>An array with <code>ndim == 1</code> is implicitly a row, not a column. The simplest way would be to turn it into a column and use <code>np.concatenate</code>:</p>
<pre><code>np.concatenate((X_train, Y_train[:, None]), axis=1)
</code></pre>
<p>You could do the same with <code>np.append</code>:</p>
<pre><code>np.appe... | python|dataframe|numpy | 0 |
5,666 | 42,449,339 | General way to test restored tensorflow model | <p>I am very, very brand new to Tensorflow, and need to write a script that tests a single example on a model restored from a checkpoint file. </p>
<p>I was wondering if there was a general way to build a test function for a restored model without knowing all the minute details of the model. </p>
<p>Further, in the l... | <p>The <a href="https://www.tensorflow.org/extend/estimators" rel="nofollow noreferrer">Estimator</a> class has a convenient set of utilities such that, if your model is wrapped around an estimator, loading and predicting from it is easy.</p>
<p>Overall, without some kind of coordination, this will be hard.</p> | python-2.7|tensorflow | 0 |
5,667 | 42,392,260 | Return equivalent of `:` from function for indexing array | <p>I have a large array and a function that returns index lists into the array, i.e.,</p>
<pre><code>import numpy
n = 500
a = numpy.random.rand(n)
def get_idx(k):
# More complicated in reality
return range(n) if k > 6 else range(k)
data = a[get_idx(29)]
data = a[get_idx(30)]
# ...
</code></pre>
<p>A ... | <p>Have a look at <a href="https://docs.python.org/3/library/functions.html#slice" rel="nofollow noreferrer"><code>slice</code></a></p>
<pre><code>def get_x():
return slice(2)
a=list(range(100))
a[get_x()]
</code></pre>
<p>will return <code>[0, 1]</code></p>
<h2>UPDATE</h2>
<p>And for your need <code>get_x</co... | python|arrays|numpy | 9 |
5,668 | 42,191,656 | Tensorflow learning XOR with linear function even though it shouldn't | <p>I am implementing a simple network in tensorflow and for pedagogical purposes, I am trying to show that the linear transformation:</p>
<pre><code>yhat = w(Wx + c) + b
</code></pre>
<p>cannot learn XOR. But the problem right now is that with my current implementation, it does! This suggests a bug in the code. Pleas... | <p>tf.argmax(Y,1) will return [0,0,0,0]. This is not what you want.</p> | tensorflow|neural-network | 1 |
5,669 | 69,873,341 | need help assigning custom timeslots to datetime data | <p>I have a datetime data to a minute (sample below)</p>
<pre><code>2021-11-08 00:10:00
2021-11-08 01:10:00
2021-11-08 02:25:00
2021-11-08 03:55:00
2021-11-08 06:55:00
2021-11-08 12:35:00
2021-11-08 16:05:00
2021-11-08 17:10:00
2021-11-08 18:45:00
2021-11-08 19:10:00
2021-11-08 20:25:00
2021-11-08 20:55:00
2021-11-08 2... | <p>Given that the initial time data is in a string format, this is how I would proceed:
Given a dataframe of form:</p>
<pre><code> Time
0 2021-11-08 00:10:00
1 2021-11-08 01:10:00
2 2021-11-08 02:25:00
3 2021-11-08 03:55:00
4 2021-11-08 06:55:00
5 2021-11-08 12:35:00
</code></pre>
<p>Step 1.
Add a timest... | python|pandas|datetime|compare | 0 |
5,670 | 43,165,025 | dropping columns from dataframes | <p>I have 5 dataframes and I want to drop certain columns from them. I tried for loop, something like this -</p>
<pre><code>dataframes =[af,bf,cf,df,ef,ff,gf]
for col in dataframes:
print col.head(1)
col = col.drop(col.columns[[0,2]],axis=1)
print col.head(1)
</code></pre>
<p>I know the approach is wrong. How t... | <p>consider the list of dataframes <code>dataframes</code></p>
<pre><code>dataframes = [pd.DataFrame(dict(A=[1], B=[2], C=[3])) for _ in range(4)]
</code></pre>
<p>Use <code>drop</code> and <code>inplace=True</code></p>
<pre><code>for d in dataframes:
d.drop(['B'], 1, inplace=True)
for d in dataframes:
prin... | python|pandas | 1 |
5,671 | 43,394,148 | Replace strings in entire dataframe if they are present in a list | <p>Thank you for your time visiting my post. I have the following dataframe below:</p>
<pre><code>df1
col1 col2
1 virginia is cold, canada is cold too virginia is cold, canada is cold too
2 florida, virginia, washington are good florida, virginia, washington a... | <pre><code>df1.replace(lst, 'x' * 5, regex=True)
col1 col2
1 xxxxx is cold, canada is cold xxxxx xxxxx is cold, canada is cold xxxxx
2 florida, xxxxx, washington are good florida, xxxxx, washington are good
3 georgia, alabama, xxxxx are xxxxx xx... | python|list|pandas|dataframe|replace | 3 |
5,672 | 50,661,449 | Trying to set a subset of a vector to equal another vector, but everything gets set to 0 | <p>I'm trying to get into Python for statistics, coming from an R background. I've set up a script for cross validation on a dataset I've been working with:</p>
<pre><code>cvIndex = np.remainder(np.arange(dat.shape[0]), 10)
pred = np.arange(dat.shape[0])
for i in range(10):
#get training and test set
trFeatur... | <p>Looks like integer coercion to me. <code>np.arange</code> returns an integer array which you then update in-place. As an in-place operation cannot change an array's dtype the r.h.s. will be converted to int. With your input being probabilities this will be all zeros.</p>
<p>Since you are overwriting all of <code>pr... | python|python-3.x|validation|numpy|numpy-ndarray | 2 |
5,673 | 50,581,526 | Two dataframe columns to row and column index with third column as values | <p>I have a dataframe that looks as follows:</p>
<pre><code> x y error
(1, 1) 1.0 1.0 0.062532
(1.0, 2.0) 1.0 2.0 0.050991
(1.0, 3.0) 1.0 3.0 0.028133
(1.0, 4.0) 1.0 4.0 0.023807
...
(99.0, 20.0) 99.0 20.0 0.019846
(99.0, 21.0) 99.0 21.0 0.135257
(99.0, 22.0)... | <p>This should work: </p>
<pre><code>df.pivot(index='y', columns='X', values='error')
</code></pre>
<p>You can get more examples on pivot table in pandas <a href="https://pandas.pydata.org/pandas-docs/stable/reshaping.html" rel="nofollow noreferrer">here</a></p> | python|pandas | 0 |
5,674 | 50,595,173 | pandas merge and groupby | <p>I have 2 pandas dataframes which look like below. </p>
<p>Data Frame 1: </p>
<pre><code>Section chainage_from chainage_to Frame
R125R002 10.133 10.138 1
R125R002 10.138 10.143 2
R125R002 10.143 10.148 3
R125R002 10.148 ... | <p>merge the dataframes by <code>Section</code> and filter so that <code>Chainage</code> is in [from & to). </p>
<pre><code>merged = pd.merge_asof(df2, df1, by='Section', left_on='Chainage', right_on='chainage_from')
</code></pre>
<p>groupby & aggregate, passing a dictionary that maps column name & aggreg... | python|pandas | 1 |
5,675 | 50,263,985 | tf.datasets input_fn getting error after 1 epoch | <p>So I am trying to switch to an input_fn() using tf.datasets as described in this <a href="https://stackoverflow.com/questions/48779293/upgrade-to-tf-dataset-not-working-properly-when-parsing-csv">question</a>. While I have been able to get superior steps/sec using tf.datasets with the input_fn() below, I appear to r... | <p>As Robbie suggests in the <a href="https://stackoverflow.com/a/50266170/3574081">other answer</a>, it looks like your old implementation used fixed batch sizes throughout (presumably using an API like <a href="https://www.tensorflow.org/api_docs/python/tf/train/batch" rel="nofollow noreferrer"><code>tf.train.batch()... | tensorflow|google-cloud-ml|tensorflow-datasets | 1 |
5,676 | 62,766,042 | Inputs to eager execution function cannot be Keras symbolic tensors with Variational Autoencoder | <p>I'm trying to implement a custom Variational Autoencoder. The code is shown below</p>
<pre><code>image = Input(shape = (X_train.shape[1]))
label = Input(shape = (Y_train.shape[1]))
inputs = Concatenate()([image, label])
x = Dense(625, activation = 'relu')(inputs)
x = Reshape((25,25,1))(x)
x = LocallyConnected2D(8... | <p>I've been having the same problem for a long time, but managed to figure it out.
The problem is that TF only accepts loss functions which accept <code>(input, output)</code> parameters which are then compared. However, you are computing your (kl_)loss using also <code>mu</code> and <code>sigma</code>, which are basi... | tensorflow|keras | 0 |
5,677 | 62,848,487 | Inserting new fields(columns) to mongoDB with pandas | <p>I have an existing data in MongoDB where Primary Key is set on 'date' with a few fields in it.</p>
<p>And I want to insert a new pandas dataframe with new fields(columns) to the existing data in MongoDB, joining on the 'date' field which exists on the both dataframe.</p>
<p>For example, lets say the this is datafram... | <p>The method you need is <code>update_one()</code> with <code>upsert=True</code> in a loop; you can't use <code>insert_many()</code> for two reasons; firstly your not always inserting; sometime you are updating; secondly <code>update_many()</code> (and <code>insert_many()</code>) only work on a single filter; in your ... | pandas|mongodb|dataframe|pymongo | 1 |
5,678 | 62,688,771 | merge cells in dataframe while converting to html using style object | <p><a href="https://i.stack.imgur.com/iSlvn.png" rel="nofollow noreferrer"> </a></p>
<p>I am trying to merge cells in dataframe while converting to HTML. But however, ending with row lines. Is it possible to merge only specific cells in a dataframe while converting to HTML or can we give header option to multiple colum... | <p>I was unable to do this. But I tried to add caption to table as an option and its very feasible than this.</p>
<pre><code>styles = [dict(selector="th", props=th_props), dict(selector="td", props=th_props), dict(selector="caption", props=[("text-align", "center"), ... | html|pandas|dataframe|styles|cell | 0 |
5,679 | 54,566,209 | LSTM's expected hidden state dimensions doesn't take batch size into account | <p>I have this decoder model, which is supposed to take batches of sentence embeddings (batchsize = 50, hidden size=300) as input and output a batch of one hot representation of predicted sentences:</p>
<pre><code>class DecoderLSTMwithBatchSupport(nn.Module):
# Your code goes here
def __init__(self, em... | <p>When you create the <code>LSTM</code>, the flag <code>batch_first</code> is not necessary, because it assumes a different shape of your input. From the docs:</p>
<blockquote>
<p>If True, then the input and output tensors are provided as (batch,
seq, feature). Default: False</p>
</blockquote>
<p>change the LSTM... | python-3.x|lstm|pytorch|batchsize | 1 |
5,680 | 71,128,122 | Pandas - How to convert timedelta64[ns] to HH:MM:SS | <p>I have a doubt how can I convert all column from Pandas (Python) from timedelta64[ns] like: <code>2 days 03:29:05</code> to: <code>51:29:05</code>.</p>
<pre><code>**PLEASE, CONSIDER time COLUMN AS A timedelta64[ns]**
d = {'id': [1123, 2342], 'time': ['2 days 03:29:05', '1 days 01:57:53']}
df = pd.DataFrame(data=d)... | <p>I don't think that there is a direct way. But you could use a custom function on top of <a href="https://pandas.pydata.org/docs/reference/api/pandas.Series.dt.total_seconds.html" rel="nofollow noreferrer"><code>total_seconds</code></a>:</p>
<pre><code>def sec_to_format(s):
h,s = divmod(int(s),3600)
m,s = div... | python|pandas | 1 |
5,681 | 60,758,957 | Using generator in Python to feed into Keras model.fit_generator | <p>I am learning how to use generator in Python and feed it into Keras model.fit_generator.</p>
<pre><code>from keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Dense, Conv2D, Flatten
import pandas as pd
import os
import cv2
class Generator:
def __init__(self,path):
... | <p>I think answers to your questions can be found in Keras documentation. </p>
<p>In terms of the input shape, <code>Conv2D</code> layers expects 4-dimensional input, but you explicitly reshape to <code>(28,28,1)</code> in your generator, so 3 dimensions. On the Conv2D info and the input format, see <a href="https://k... | python|tensorflow|machine-learning|keras|deep-learning | 1 |
5,682 | 60,683,901 | Tensorboard smoothing | <p>I downloaded the CSV files from tesnorboard in order to plot the losses myself as I want them Smoothed.</p>
<p>This is currently my code:</p>
<pre><code>import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('C:\\Users\\ali97\\Desktop\\Project\\Database\\C... | <p>If you are working with <code>pandas</code> library you can use the function <code>ewm</code> (<a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.ewm.html" rel="noreferrer">Pandas EWM</a>) and ajust the <code>alpha</code> factor to get a good approximation of the smooth function fro... | python|tensorflow|tensorboard | 8 |
5,683 | 72,728,922 | ERROR: Input to reshape is a tensor with 2381440 values, but the requested shape requires a multiple of 200704 | <p>I am trying to learn the CNN model in deep learning and I am using a <code>Cats-vs_Dogs</code> dataset to begin with. I am following a video tutorial and the steps are the same, although the dataset is different and all the other solutions have vastly varied code that I am not able to understand. Can someone tell me... | <p>The error is because the target size is (244, 244) and the input shape given in the model is (224, 224, 3). You can either change the target size to (224, 224) or change the input shape to (244, 244, 3).</p>
<p>Change the <code>input_shape</code> to <code>(244, 244, 3)</code></p>
<pre><code>Conv2D(filters=32,kernel_... | python|tensorflow|machine-learning|keras|deep-learning | 0 |
5,684 | 72,550,969 | Interpolating a time series with interp1d using only numpy | <p>I want to plot a time series with numpy and matplotlib, using markers for the exact points, and interpolation. Basically this (data is dummy, but functionality is the same, note that distance between time-points may vary):</p>
<pre class="lang-py prettyprint-override"><code>import numpy as np
from scipy.interpolate ... | <p>The data type of any element in <code>T</code> is <code>np.datetime64</code> and not <code>np.timedelta64</code>.</p>
<p>Thus, convert the dtype of all elements of T to <code>np.timedelta64</code> by creating a numpy array with datatype <a href="https://numpy.org/doc/stable/reference/generated/numpy.dtype.kind.html... | python|numpy|matplotlib|scipy|interpolation | 1 |
5,685 | 72,538,834 | How to do a nested loop with 2 columns for pandas dataframe with counter? | <p>I am using python and pandas. I have a bunch of unstructured survey data.</p>
<p>I have a dataframe:</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th>Type</th>
<th>Activity</th>
</tr>
</thead>
<tbody>
<tr>
<td>Sport</td>
<td>rowing</td>
</tr>
<tr>
<td>Sport</td>
<td>Surfing</td>
</tr>
<t... | <p>It's best to avoid loops as much as possible, here we replaced the 2 loops with a dictionary <code>activity_type</code> that maps every activity to its type.</p>
<p>NOTE: the <code>df1</code> is the Type, Activity <code>DataFrame</code></p>
<pre class="lang-py prettyprint-override"><code>import pandas as pd
from col... | python|pandas|loops | 0 |
5,686 | 59,755,671 | How to use a trained TensorFlow model in a plain Javascript webapp | <p>I've been following this tutorial:
<a href="https://docs.annotations.ai/workshops/object-detection/6.html" rel="nofollow noreferrer">https://docs.annotations.ai/workshops/object-detection/6.html</a></p>
<p>And got to step 6, once I get to the webapp example it's done in ReactJS and I can't figure out how to convert... | <p>You can use the script for TensorFlow js & coco SSD</p>
<pre><code><!-- Load TensorFlow.js. This is required to use coco-ssd model. -->
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"> </script>
<!-- Load the coco-ssd model. -->
<script src="https://cdn.jsdelivr.net/npm/@t... | javascript|tensorflow | 0 |
5,687 | 59,671,362 | Downloading blocks of data from census, how to wrtie to multiple csvs to not exceed memory | <p>Suppose I have a list of api keys I am downloading from the census data</p>
<p>Example:</p>
<pre><code>variable_list = [
'B08006_017E',
'B08016_002E',
'B08016_003E',
'B08016_004E',
...
]
</code></pre>
<p>Now given memory constraints for putting this data onto one csv file. I want to create a way in which I place ... | <p>It looks like you're already chunking the variable_lists here:</p>
<pre class="lang-py prettyprint-override"><code>for var_list in all_variable_lists:
list_of_dfs.append(download_all_data(var_list, 2012))
</code></pre>
<p>Just make sure each <code>var_list</code> has only 100 items. Then chunk the csv writing ... | python|pandas|census | 1 |
5,688 | 59,616,436 | How to reset initialization in TensorFlow 2 | <p>If I try to change parallelism in TensorFlow 2 after initializing a <code>tf.Variable</code>,</p>
<pre><code>import tensorflow as tf
_ = tf.Variable([1])
tf.config.threading.set_inter_op_parallelism_threads(1)
</code></pre>
<p>I get an error</p>
<blockquote>
<p>RuntimeError: Inter op parallelism cannot be modif... | <p>This is achievable in a "hacky" way. But I'd recommend doing this the right way (i.e. by setting up config at the beginning).</p>
<pre><code>import tensorflow as tf
from tensorflow.python.eager import context
_ = tf.Variable([1])
context._context = None
context._create_context()
tf.config.threading.set_inter_op_... | python|tensorflow|pytest|tensorflow2.0 | 3 |
5,689 | 59,818,185 | memory error reading big size csv in pandas | <p>My laptops memory is 8 gig and I was trying to read and process a big csv file, and got memory issues, I found a solution which is using <strong>chunksize</strong> to process the file chunk by chunk, but apperntly when uisng chunsize the file format vecoe <strong>textreaderfile</strong> and the code I was using to p... | <p>You have to iterate over the chunks:</p>
<pre><code>csv_length = 0
for chunk in pd.read_csv(fileinput, names=['sentences'], skiprows=skip, chunksize=10000):
csv_length += chunk.count()
print(csv_length )
</code></pre> | python|pandas|csv|chunking | 1 |
5,690 | 59,551,405 | rolling function does not print all values | <p>I am trying to understand rolling function on pandas on python here is my example code </p>
<pre><code># importing pandas as pd
import pandas as pd
# By default the "date" column was in string format,
# we need to convert it into date-time format
# parse_dates =["date"], converts the "date" column to date-time... | <p><code>...</code> just means that <code>pandas</code> isn't showing you all the rows, that's where the 'missing' ones are.</p>
<p>To display all rows:</p>
<pre><code>with pd.option_context("display.max_rows", None):
print (df.close.rolling(3, win_type ='triang').sum())
</code></pre> | python|pandas | 1 |
5,691 | 61,858,321 | Plot specific months from whole data set? | <p>From a whole data set, I need to plot the maximum & minimum temperatures for just the months of January and July. Column 2 is the date, and columns 8 and 9 are the 'TMAX' and 'TMIN.' This is what I have so far:</p>
<pre><code>napa3=pd.read_csv('MET 51 Lab #10 data (Pandas, NAPA).csv',usecols=[2,8,9])
time2=pd.t... | <p>The problem can raise because the dates are saved as an "object" or a string.
However, I can't see that you have created dataframe?! you do read_csv but you do not make dataframe out of that:</p>
<pre><code>dnapa3 = pd.DataFrame(napa3)
</code></pre>
<p>then repeat converting your time data and check:</p>
<pre><co... | python|pandas|datetime | 0 |
5,692 | 61,885,775 | Rolling mean over the last n-days with if statement | <p>I have the following dataframe:</p>
<pre><code>entry_time_flat route_id time_slot duration n_of_trips
2019-09-02 00:00:00 1_2 0-6 10 29
2019-09-04 00:00:00 3_4 6-12 15 10
2019-09-06 00:00:00 1_2 ... | <p>I am not complete sure if I understood your goal here but I'll try:</p>
<pre><code>import pandas as pd
data = {'entry_time_flat': ['2019-09-02 00:00:00', '2019-09-04 00:00:00', '2019-09-06 00:00:00', '2019-09-06 00:00:00'], 'route_id': ['1_2', '3_4', '1_2', '1_2'], 'time_slot': ['0-6', '6-12', '0-6', '18-20'], 'du... | python|pandas|pandas-groupby|rolling-computation | 0 |
5,693 | 61,831,227 | How to solve Import object_detection/protos/image_resizer.proto but not used protobuf compilation | <p>I have an issue when I try to compile Protobuf to use TensorFlow Object Detection API</p>
<p>In jupyter I tried to lauch object_detection_tutorial</p>
<p>I got this error:</p>
<p>jupyter protobuf error:<br />
<a href="https://i.stack.imgur.com/YLtAl.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/... | <p>Haven't tried to build the latest version of the api, but perhaps that's a bug? Try removing line 5 from input_reader.proto:</p>
<pre><code>//import "object_detection/protos/image_resizer.proto";
</code></pre>
<p>It looks as though it isn't used, perhaps they forgot to remove it.</p> | python|tensorflow | 4 |
5,694 | 57,736,228 | Errors when load selected raw data into dataframe | <p>I want to select three groups of json data and load them into a dataframe but i got the errors "string indices must be integers". Can anyone kindly tell me what is the reason for it?</p>
<p>The code is as following and i have also attached the screenshot:</p>
<pre><code>for currency in data:
if '/BTC' in currency... | <p>Looks like <code>currency</code> is a key in <code>data</code> dictionary here:</p>
<pre><code>for currency in data:
if '/BTC' in data[currency]['symbol']:
change_daily=data[currency]['percentage']
name=data[currency]["symbol"]
price = data[currency]['last']
df_binance.append({"N... | python|pandas|data-analysis|cryptocurrency | 0 |
5,695 | 57,990,852 | catplot(kind="count") is significantly slower than countplot() | <p>I am working on a fairly large dataset (~40m rows). I have found that if I call <strong>sns.countplot()</strong> directly then my visualisation plots really quickly:</p>
<pre><code>%%time
ax = sns.countplot(x="age_band",data=acme)
</code></pre>
<p><a href="https://i.stack.imgur.com/k4qwV.png" rel="nofollow norefe... | <p>There is a lot of overhead in <code>catplot</code>, or for that matter in <code>FacetGrid</code>, that will ensure that the categories are synchronized along the grid. Consider e.g. that you have a variable you plot along the columns of the grid for which not every age group occurs. You would still need to show that... | python|pandas|matplotlib|seaborn | 1 |
5,696 | 54,956,068 | Rename a column values in pandas | <p>I have a dataset with column named region. Sample values are
Eg. <code>region_1, region_2, region_3</code> etc.</p>
<p>I need to replace these values to
Eg. <code>1,2,3</code>, etc.</p>
<p>Any specific function to deal with this easy transformation?</p>
<p>Thanks</p> | <p>I believe you need split with select second value and if necessary convert to integers:</p>
<pre><code>df.region = df.region.str.split('_').str[1].astype(int)
</code></pre>
<p>Or use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.str.extract.html" rel="nofollow noreferrer"><code>extra... | python|pandas | 0 |
5,697 | 54,809,462 | xarray and netCDF file with empty variables and 0-dimensional object dataframe | <p>I'm trying to convert some .nc files into <code>pandas</code> dataframes using <code>xarray</code>.</p>
<p>Here's one of the netCDF files:</p>
<p><a href="ftp://l5ftl01.larc.nasa.gov/MISR/MIL2ASAE.003/2017.08.31/MISR_AM1_AS_AEROSOL_P006_O094165_F13_0023.nc" rel="nofollow noreferrer">ftp://l5ftl01.larc.nasa.gov/MIS... | <p>Your dataset seems to be setup with a hierarchy of <a href="http://docs.h5py.org/en/stable/high/group.html" rel="nofollow noreferrer">groups</a>. Xarray's <a href="http://xarray.pydata.org/en/stable/generated/xarray.open_dataset.html#xarray.open_dataset" rel="nofollow noreferrer"><code>open_dataset</code></a> functi... | python|pandas|netcdf|python-xarray | 3 |
5,698 | 54,911,436 | How to implement CRelu in Keras? | <p>I'm trying to implement CRelu layer in Keras</p>
<p>One option that seems work is to use Lambda layer:</p>
<pre><code>def _crelu(x):
x = tf.nn.crelu(x, axis=-1)
return x
def _conv_bn_crelu(x, n_filters, kernel_size):
x = Conv2D(filters=n_filters, kernel_size=kernel_size, strides=(1, 1), padding='same'... | <p>I don't think there is a significant difference between the two implementations speed-wise. </p>
<p>The Lambda implementation is the simplest actually but writing a custom Layer as you have done usually is better, especially for what regards model saving and loading (<strong>get_config</strong> method). </p>
<p>Bu... | python|tensorflow|keras|deep-learning | 1 |
5,699 | 49,759,626 | Is it possible to Skip Blank Lines in a Dataframe? If Yes then how I can do this | <p>I am trying to run this code</p>
<pre><code>num = df_out.drop_duplicates(subset=['Name', 'No.']).groupby.(['Name']).size()
</code></pre>
<p>But when I do I get this error:</p>
<pre><code>ValueError: not enough values to unpack (expected 2, got 0)
</code></pre>
<p>If we think about my dataframe(df_out) as an exce... | <p>Consider using <code>df.dropna()</code>. It is uses to remove rows that contains NA. See <a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.dropna.html" rel="nofollow noreferrer">https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.dropna.html</a> for more information... | python|pandas|dataframe | 3 |
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