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 |
|---|---|---|---|---|---|---|
4,400 | 35,402,074 | `unique` in data.frame.describe() not work [python][pandas] | <p>Hi it's something fundamental but I can't fix it... <code>unique()</code> shows unique values in each column, but <code>describe()</code> shows NaN. Why... Any help's appreciated. thanks</p>
<pre><code>import numpy as np
import pandas as pd
train = pd.read_csv('train.csv', header=0)
# works:
train['Pclass'].uniqu... | <p>The <code>describe</code> method for numeric columns doesn't list the number of unique values, since this is usually not particularly meaningful for numeric data, the <code>describe</code> method for string columns does:</p>
<pre><code>import pandas as pd
df = pd.DataFrame({'string_column': ['a', 'a', 'b'], 'numeri... | python|pandas|unique|describe | 3 |
4,401 | 35,465,176 | Visible Deprecation warning...? | <p>I have some data that Im reading from a h5 file as a numpy array and am doing some analysis with. For context, the data plots a spectral response curve. I am indexing the data (and a subsequent array I have made for my x axis) to get a specific value or range of values. Im not doing anything complex and even the lit... | <p>Previous questions on this warning:</p>
<p><a href="https://stackoverflow.com/questions/33098765/visibledeprecationwarning-boolean-index-did-not-match-indexed-array-along-dimen">VisibleDeprecationWarning: boolean index did not match indexed array along dimension 1; dimension is 2 but corresponding boolean dimension... | arrays|numpy|warnings|h5py | 18 |
4,402 | 28,622,619 | InvalidBSON on MongoDB import - Pandas | <p>I'm currently working with Pandas (0.14.1) in Python 3.4.2 importing data from a Mongo database using pymongo (2.8). Upon a simple import,</p>
<pre><code>cur = db.collection.find()
df = pd.DataFrame(list(cur))
</code></pre>
<p>I'm getting the following error:</p>
<pre><code>InvalidBSON: 'utf-8' codec can't decode... | <p>I don't believe there is a way of applying encoding on a cursor object while directly loading it into pandas. You may want to use mongoexport to dump your data into a csv first:</p>
<pre><code>mongoexport --host localhost --db dbname --collection name --csv > test.csv
</code></pre>
<p>...and then you load that ... | python|mongodb|python-3.x|pandas|pymongo | 0 |
4,403 | 50,966,435 | How to make the conversion NaN >> [''] to all the elements of a Pandas Dataframe? | <pre><code>import pandas as pd
import numpy as np
df = pd.DataFrame({
'A': [[1, 2, 3, 4], [4, 5, 6, 7, 8], [7, 6, 4], np.nan, [1, 2]],
'B': [[1, 2, 3, 4], [4, 5, 6, 7, 8], [3, 7, 9], np.nan, [4, 5]],
'E': [np.nan, np.nan, np.nan, np.nan, np.nan],
'F': [[2, 2], [4, 4], np.nan, [78, 90], np.nan]
})
# Fi... | <p><strong>UPDATE:</strong></p>
<pre><code>In [136]: df.applymap(lambda x: x if isinstance(x, list) else [])
Out[136]:
A B E F
0 [1, 2, 3, 4] [1, 2, 3, 4] [] [2, 2]
1 [4, 5, 6, 7, 8] [4, 5, 6, 7, 8] [] [4, 4]
2 [7, 6, 4] [3, 7, 9] [] []
... | python|python-3.x|pandas|dataframe|nan | 2 |
4,404 | 50,905,364 | get column name that contains a specific value in pandas | <p>I want to get column name from the whole database (assume the database contains more than 100 rows with more than 50 column) based on specific value that contain in a specific column in pandas.</p>
<p>Here is my code:</p>
<pre><code>import pandas as pd
df = pd.DataFrame({'A':[1,2,3], 'B':[4,5,6], 'C':[7,8,9]})
pos... | <p>Try this</p>
<pre><code>for i in df.columns:
newDf = df.loc[lambda df: df[i] == response]
if(not newDf.empty):
print(i)
</code></pre> | python|python-2.7|pandas | 2 |
4,405 | 9,224,718 | What's the most efficient way to compute the mode in a sliding window over a 2D array in Python? | <p>I have an RGBA image that I need to upscale while keeping it smooth. </p>
<p>The catch is that I need to keep the colors exactly the way they are (background: I'm resizing a map where provinces are color-coded), and so I cannot just perform a resize with bicubic interpolation, because that will also interpolate th... | <p>If you care about a fixed set of colors in your image, the "Palette" image mode would perhaps be more appropriate (at least, if you don't have more than 256 colors in your map).</p>
<p>I would suggest to first convert your image to the "P" mode (Since I'm not really familiar with PIL, I'm not sure, how easy that is... | python|numpy|python-imaging-library|mode|sliding-window | 1 |
4,406 | 6,091,378 | The concept behind scipy.signal.medfilt2d in python | <p>I am trying to understand how scipy.signal.medfilt2d works. After I looked at the scipy documentation, its format is:</p>
<p>scipy.signal.medfilt2d(input, kernel_size=3)</p>
<p>So like if I have a matrix like</p>
<pre><code>1 2 3 7 2 4
3 4 2 2 6 7
1 7 3 1 2 6
3 2 3 4 3 1
2 6 7 8 2 5
3 4 2 2 1 8
</code></pre>
<p>... | <p>As explained in the <a href="http://docs.scipy.org/doc/scipy/reference/tutorial/signal.html#median-filter" rel="nofollow">Scipy documentation</a>, <code>medfilt2</code> is a median filter. Quoting from the documentation,</p>
<pre><code>The sample median is the middle array value in a sorted list of neighborhood val... | python|matrix|numpy|scipy | 2 |
4,407 | 66,596,142 | BertModel or BertForPreTraining | <p>I want to use Bert only for embedding and use the Bert output as an input for a classification net that I will build from scratch.</p>
<p>I am not sure if I want to do finetuning for the model.</p>
<p>I think the relevant classes are BertModel or BertForPreTraining.</p>
<p><a href="https://dejanbatanjac.github.io/be... | <p>You should be using <code>BertModel</code> instead of <code>BertForPreTraining</code>.</p>
<p><code>BertForPreTraining</code> is used to train bert on Masked Language Model (MLM) and Next Sentence Prediction (NSP) tasks. They are not meant for classification.</p>
<p>BERT model simply gives the output of the BERT mod... | deep-learning|nlp|bert-language-model|huggingface-transformers|transformer-model | 5 |
4,408 | 66,605,849 | extract date only from pandas column | <p>I have this column in pandas df:
'''</p>
<pre><code>full_date
2020-12-02T08:11:30-0600
2020-12-02T02:11:50-0600
2020-12-03T08:56:29-0600
</code></pre>
<p>'''</p>
<p>I only need the date, hoping to have this column:
'''</p>
<pre><code>date
2020-12-02
2020-12-02
2020-12-03
</code></pre>
<p>'''</p>
<p>I have tried to f... | <p>In case your column is not a <code>datetime</code> type, you can convert it to that and then use the <code>.dt</code> accessor to get just the date:</p>
<pre><code>>>> df["date"] = df["full_date"].pipe(pd.to_datetime, utc=True).dt.date
>>> print(df)
full_date ... | python|pandas | 1 |
4,409 | 66,597,043 | How can I count frequency by data in dataframe? | <p>I have an issue with groupby in pandsa. My DF looks like below:</p>
<pre><code>time ID
01-13 1
01-13 2
01-14 3
01-15 4
01-15 5
</code></pre>
<p>I need result like below:</p>
<pre><code>time ID
01-13 2
01-14 1
01-15 2
</code></pre>
<p>So basically I need to count frequency ID by data. I treid with t... | <p>One way is:</p>
<pre><code>df.time.value_counts()
</code></pre>
<p>Output:</p>
<pre><code>01-15 2
01-13 2
01-14 1
Name: time, dtype: int64
</code></pre>
<p>Other way, as suggested by reviewer above:</p>
<pre><code>df.groupby(['time']).size().reset_index(name='Frequency')
</code></pre>
<p>Output:</p>
<pre><c... | pandas|dataframe|group-by|count|frequency | 2 |
4,410 | 66,621,907 | Filter date index based on regex in pandas | <p>I have date column with price index like below,</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th style="text-align: left;">Date</th>
<th style="text-align: center;">Price</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: left;">2010-01-01</td>
<td style="text-align: center;">23</td>... | <p>Convert Date column to <code>datetime</code> data type</p>
<pre><code>df['Date'] = pd.to_datetime(df['Date'])
</code></pre>
<p>Filter by month and day</p>
<pre><code>df.loc[(df.Date.dt.month == 12) & (df.Date.dt.day == 31)]
</code></pre>
<p>Output</p>
<pre><code> Date Price
1 2010-12-31 25
3 2013-12-... | python|pandas|nsepy | 2 |
4,411 | 16,410,827 | How to iterate Numpy array and perform calculation only if element matches a criteria? | <p>I want to iterate a numpy array and process only elements match with specific criteria. In the code below, I want to perform calculation only if element is greater than 1.</p>
<pre><code>a = np.array([[1,3,5],
[2,4,3],
[1,2,0]])
for i in range(0, a.shape[0]):
for j in range(0, a.sha... | <p>Method #1: use a boolean array to index:</p>
<pre><code>>>> a = np.array([[1,3,5], [2,4,3], [1,2,0]])
>>> a[a > 1] = (a[a > 1] - 3) * 5
>>> a
array([[ 1, 0, 10],
[-5, 5, 0],
[ 1, -5, 0]])
</code></pre>
<p>This computes <code>a > 1</code> twice, although you co... | python|numpy | 3 |
4,412 | 57,537,474 | How to fix 'AttributeError: 'list' object has no attribute 'shape'' error in python with Tensorflow / Keras when loading Model | <p>Im trying to save my Model in Keras and then load it but when it try to use the loaded Model it trows an Error</p>
<p>Python Vesion: 3.6.8
Tensorflow Version: 2.0.0-beta1
Keras Version: 2.2.4-tf</p>
<p>Here is my Code:</p>
<pre><code>from __future__ import absolute_import, division, print_function, unicode_litera... | <p>Try using</p>
<pre><code>nmodel.predict(x_test)
</code></pre>
<p>instead of</p>
<pre><code>nmodel.predict([x_test])
</code></pre>
<p>(remove the brackets).</p> | python|python-3.x|numpy|tensorflow|keras | 2 |
4,413 | 57,598,032 | Sort Monthly Abbreviation columns (Jan, Feb, Mar, etc.) in Dataframe (currently sorting alphabetically) | <p>I have a dataframe I've created from stock data. I am counting how many times the 'close > open' by month and by year using a pivot table. If I use the integer for each month my table is in the correct order. If I use the 3-letter abbreviation for each month it sorts alphabetically. How can I get the month abbre... | <p>As WeNYoBen commented, one way to achieve customized ordering of strings is through ordered categorical.</p>
<p>Another thing to note is that you can do numeric operation (such as sum) over boolean (True=1, False=0), therefore <code>np.where(data['Close'] > data['Open'], 1, 0)</code> is really not necessary, <co... | python|pandas|sorting | 1 |
4,414 | 57,683,299 | Increment the max rows of the pandas DataFrame | <p>I have this python function to get financial data from some tickers</p>
<pre><code>def get_quandl_data_df(ticker, start, end, api_key):
import quandl
return quandl.get_table('WIKI/PRICES', qopts={'columns': ['ticker','date', 'open', 'high', 'low', 'close', 'volume']}, ticker = ticker, date = { 'gte': s... | <p>1)Appending the argument paginate=True will extend the limit to 1,000,000 rows.</p>
<pre><code>get_quandl_data_df(ticker, start, end, api_key,paginate=True):
</code></pre> | python|python-3.x|pandas|quandl | 1 |
4,415 | 57,409,679 | pandas groupby aggregate keep equal values | <p>I am trying to build an aggregator which simply returns a value if it is equal to all other values in the variable and NaN if it isn't.</p>
<p>It is ment to keep meta information while aggregating sensory data. </p>
<p>I get a strange key error... </p>
<pre><code>import pandas as pd
import numpy as np
df = pd.Da... | <p>You need to check for the location with <code>iloc</code></p>
<pre><code>import pandas as pd
import numpy as np
df = pd.DataFrame.from_dict({'v1' : [1,1,1,2,2,2],
'v2' : [1,2,3,4,5,6],
'v3' : [1,1,1,2,3,2],
'v4' : [2,2,2,3,3,3]}... | python|pandas | 1 |
4,416 | 57,296,791 | How to display matplotlib numpy.ndarray in tkinter | <p>I want to display numpy.ndarray matplotlib in tkinter. </p>
<p>I tried in backend it works fine, but does not display in tkinter and show the canvas with graph empty.instead the code below display the picture in separate window as pop-up. How can I display it in the canvas and inside the window?</p>
<pre><code> fr... | <p><code>am</code> is <code>numpy.ndarray</code></p>
<pre><code>am = np.zeros_like(daily_returns)
</code></pre>
<p>and it doesn't have <code>am.plot()</code>. </p>
<p>But <code>pandas.DataFrame</code> has it. You have to convert <code>am</code> to <code>DataFrame</code></p>
<pre><code> df = pd.DataFrame(am)
... | python-3.x|matplotlib|tkinter|numpy-ndarray | 1 |
4,417 | 57,482,954 | Grouping and splitting to avoid leakage | <p>I have a pandas <code>dataframe</code> where the data is arranged as follows:</p>
<pre><code> filename label
0 4456723 0
1 4456723_01 0
2 4456723_02 0
3 ab43912 1
4 ab43912_01 1
5 ab43912_03 1
... ... ...
</code></pre>
<p>I want to ... | <p>You can manually select ~80% of the unique file handles randomly.</p>
<pre><code>df = pd.DataFrame({'filename': list('aaabbbcccdddeeefff')})
df['filename'] = df['filename'] + ['', '_01', '_02']*6
</code></pre>
<hr>
<pre><code># Get the unique handles
files = df.filename.str.split('_').str[0]
# Randomly select ~8... | python|python-3.x|pandas | 3 |
4,418 | 73,154,668 | unexpected keyword argument trying to instantiate a class inheriting from torch.nn.Module | <p>I have seen similar questions, but most seem a little more involved. My problem seems to me to be very straightforward, yet I cannot figure it out. I am simply trying to define a class and then instantiate it, but the arguments passed to the constructor are not recognized.</p>
<pre><code>import torch
import torch.n... | <p>You have a typo: it should be <code>__init__</code> instead of <code>__int__</code>.</p> | python|pytorch | 0 |
4,419 | 70,675,292 | Python Same Period Last Year in Pandas with GroupBy | <p>I have following DataFrame:</p>
<pre class="lang-py prettyprint-override"><code>import pandas as pd
import numpy as np
n = 72
dates = list(pd.date_range(start='2016-01-01',periods=n,freq='MS'))
products = ['a','b','c']
countries = ['a','b','c']
df = pd.merge(pd.DataFrame({'product':products}), pd.DataFrame({'coun... | <p>Not sure if this is your expected result. You can try this :</p>
<pre><code>df['last_year'] = df['y'].shift(12)
</code></pre> | python|pandas|group-by|offset|forecasting | 0 |
4,420 | 70,650,161 | how to create column with other dataframe as legend in python (groupby() alike)- pandas library | <p>i want to use df2 as legend to my primary df ('main_df')</p>
<p>what "<strong>code</strong>" need to be ?</p>
<pre><code>main_df = pd.DataFrame({'genre_NAME': ['comedy', 'action', 'horror'], 'genre_id': [nan, nan, nan]})
df2 = pd.DataFrame({'genre': ['comedy', 'horror'], 'id': [0, 1]})
#main_df is not r... | <p>You can access and change a specific value in a dataframe with <code>df.loc[]</code>. So in your case:</p>
<pre><code>for _, line in df2.iterrows():
main_df.loc[main_df.genre_NAME == line.genre, "genre_id"] = line.id
</code></pre> | python|pandas|dataframe|pandas-groupby | 0 |
4,421 | 70,622,747 | How to identify a number that has a correspondent negative in a list? | <p>I am beginner in this journey of Python/VBA so I have a problem I kindly want do ask you.</p>
<p>So I have a list of rows in an excel spreadsheet (also I treat this data with Pandas to reduce the number of rows I have to analyse on Excel)</p>
<p>The fact is that I have dozens of thousands rows that in a specific col... | <p>Assuming you have float in this column, you can compare the rows with the opposite of the next one, and use this information to subset only those rows.</p>
<pre><code># is the next row the opposite value?
m = df['col'].eq(-df['col'].shift())
# drop the matching rows and the next ones
df2 = df.loc[~(m|m.shift(-1))]
... | python|excel|pandas|numpy | 2 |
4,422 | 42,647,710 | Compare Boolean Row values across multiple Columns in Pandas using & / np.where() / np.any() | <p>I have a dataframe that looks like:</p>
<pre><code> a A a B a C a D a E a F p A p B p C p D p E p F
0 0 0 0 0 0 0 0 0 0 0 0 0
1 1 0 0 0 0 0 0 0 0 0 0 0
2 0 1 0 0 0 0 0 0 1 0 0 0
3 0 0 1 ... | <p>You can use <code>~</code> for invert boolean mask with <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.iloc.html" rel="nofollow noreferrer"><code>iloc</code></a> for select by position:</p>
<pre><code>print (~df.iloc[:,6:11].any(1) & df.iloc[:,0:6].any(1))
0 False
1 True
... | python|pandas|numpy|boolean|any | 3 |
4,423 | 42,878,600 | Access a list within Pandas Dataframe apply function without making list global | <p>I have a Pandas dataframe. For each row in this frame I want to make a certain check. If the check yields <code>True</code>, then I want to add certain columns of the dataframe-row to a list-structure.</p>
<p>How can I access a list from within the apply function without the need to create a global list variable? I... | <p><a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.apply.html" rel="nofollow noreferrer"><code>df.apply()</code></a> allows you to pass positional arguments and keywords.</p>
<pre><code>def checkFunction(row, lst):
if (check == True):
lst.append(row)
return row
my_list... | python|pandas|dataframe|apply | 1 |
4,424 | 42,606,387 | why i can't open my files? | <pre><code>import pandas as pd
df=pd.read_csv(r"C:\Users\champion\Desktop\政大資料科學競賽\104.1~106.1\臺北捷運各站出站量統計_201501.csv",encoding='big5')
</code></pre>
<p>I can execute it before.I don't know why python happen OSError.</p>
<p>Is it could be my data which is Chinese?</p>
<p>I browsed a lot of question,but no one can't ... | <p>If u use python 3.6, u can try </p>
<blockquote>
<p>df=pd.read_csv(r"C:\Users\champion\Desktop\政大資料科學競賽\104.1~106.1\臺北捷運各站出站量統計_201501.csv",encoding='big5',engine="python")</p>
</blockquote> | python|pandas | -1 |
4,425 | 30,423,052 | Matplotlib: Import and plot multiple time series with legends direct from .csv | <p>I have several spreadsheets containing data saved as comma delimited (.csv) files in the following format: The first row contains column labels as strings ('Time', 'Parameter_1'...). The first column of data is Time and each subsequent column contains the corresponding parameter data, as a float or integer. </p>
<p... | <p>Here's my minimal working example for the above using genfromtxt rather than loadtxt, in case it is helpful for anyone else.
I'm sure there are more concise and elegant ways of doing this (I'm always happy to get constructive criticism on how to improve my coding), but it makes sense and works OK:</p>
<pre><code>im... | excel|csv|numpy|matplotlib|import | 2 |
4,426 | 26,467,696 | pandas DataFrame conditional string split | <p>I have a column of influenza virus names within my DataFrame. Here is a representative sampling of the name formats present:</p>
<ol>
<li>(A/Egypt/84/2001(H1N2))</li>
<li>A/Brazil/1759/2004(H3N2)</li>
<li>A/Argentina/126/2004</li>
</ol>
<p>I am only interested in getting out A/COUNTRY/NUMBER/YEAR from the strain n... | <p>So, based on EdChum's recommendation, I'll post my answer here.</p>
<p>Minimal data frame required for tackling this problem:</p>
<pre><code>Index Strain Name Year
0 (A/Egypt/84/2001(H1N2)) 2001
1 A/Brazil/1759/2004(H3N2) 2004
2 A/Argentina/126/2004 2004
</code></pre>
... | python|pandas | 1 |
4,427 | 26,590,359 | Compare Dictionaries for close enough match | <p>I am looking for a good way to compare two dictionaries which contain the information of a matrix. So the structure of my dictionaries are the following, both dictionaries have identical keys:</p>
<pre><code>dict_1 = {("a","a"):0.01, ("a","b"): 0.02, ("a","c"): 0.00015, ...
dict_2 = {("a","a"):0.01, ("a","b"): 0.01... | <p>Easiest way I could think of:</p>
<pre><code>keylist = dict_1.keys()
array_1 = numpy.array([dict_1[key] for key in keylist])
array_2 = numpy.array([dict_2[key] for key in keylist])
if numpy.allclose(array_1, array_2):
print('Equal')
else:
print('Not equal')
</code></pre> | python|python-2.7|numpy|dictionary | 6 |
4,428 | 39,112,689 | Can I create a new column based on when the value changes in another column? | <p>Let s say I have this <code>df</code></p>
<pre><code>print(df)
DATE_TIME A B
0 10/08/2016 12:04:56 1 5
1 10/08/2016 12:04:58 1 6
2 10/08/2016 12:04:59 2 3
3 10/08/2016 12:05:00 2 2
4 10/08/2016 12:05:01 3 4
5 10/08/2016 12:05:02 3 6
6 10/08/2016 12:05:03 1 3
7 10/08/201... | <p>You can use the <em>shift-cumsum</em> pattern.</p>
<pre><code>df['C'] = (df.A != df.A.shift()).cumsum()
>>> df
DATE_TIME A B C
0 10/08/2016 12:04:56 1 5 1
1 10/08/2016 12:04:58 1 6 1
2 10/08/2016 12:04:59 2 3 2
3 10/08/2016 12:05:00 2 2 2
4 10/08/2016 12:05:01 3 4 ... | python|pandas|uniqueidentifier | 5 |
4,429 | 19,590,966 | Memory error with large data sets for pandas.concat and numpy.append | <p>I am facing a problem where I have to generate large DataFrames in a loop (50 iterations computing every time two 2000 x 800 pandas DataFrames). I would like to keep the results in memory in a bigger DataFrame, or in a dictionary like structure.
When using pandas.concat, I get a memory error at some point in the loo... | <p>This is essentially what you are doing. Note that it doesn't make much difference from a memory perspective if you do conversition to DataFrames before or after.</p>
<p>But you can specify dtype='float32' to effectively 1/2 your memory.</p>
<pre><code>In [45]: np.concatenate([ np.random.uniform(size=2000 * 1000).a... | python|python-2.7|numpy|pandas | 6 |
4,430 | 33,651,668 | How to add leading zero formatting to string in Pandas? | <p><strong>Objective:</strong> To format <code>['Birth Month']</code> with leading zeros</p>
<p>Currently, I have this code:</p>
<pre><code>import pandas as pd
import numpy as np
df1=pd.DataFrame.from_items([('A', [1, 2, 3]), ('B', [4, 5, 6])])
df1['Birth Year']= np.random.randint(1905,1995, len(df1))
df1['Birth Mon... | <p>Cast the dtype of the series to <code>str</code> using <a href="http://pandas.pydata.org/pandas-docs/version/0.17.0/generated/pandas.Series.astype.html#pandas.Series.astype" rel="noreferrer"><code>astype</code></a> and use vectorised <a href="http://pandas.pydata.org/pandas-docs/version/0.17.0/generated/pandas.Serie... | python|string|numpy|pandas|dataframe | 9 |
4,431 | 33,534,657 | How to find index value from meshgrid in numpy | <p>I have the following problem: I have two surface equations, and I am looking at what point they are zero. So I have the following:</p>
<pre><code>b = np.arange(0,2,0.1)
k = np.arange(0,50,1)
b,k = np.meshgrid(b,k)
</code></pre>
<p>with these I produce <code>z1</code> and <code>z2</code>, massive formulas, but they... | <p>In this case, your "is close to zero" expression <code>(-0.1<z1)&(z1<0.1)</code> is an array of booleans. To find the indices of the <code>True</code> items you simply need to use <a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.nonzero.html" rel="nofollow"><code>nonzero()</code></a>.</p... | python|numpy|intersection | 1 |
4,432 | 22,734,763 | Using Pandas To Find the Number Of Periods Since the Rolling High | <p>I am using the rolling_max function in Pandas:</p>
<p><a href="http://pandas.pydata.org/pandas-docs/stable/computation.html#moving-rolling-statistics-moments" rel="nofollow">http://pandas.pydata.org/pandas-docs/stable/computation.html#moving-rolling-statistics-moments</a></p>
<p>How would I find the number of peri... | <p>starting with:</p>
<pre><code>>>> ts
A 10
B 10
C -5
D -15
E -9
F -8
G -13
H -9
I -15
J -21
dtype: int64
</code></pre>
<p>you may do:</p>
<pre><code>>>> rmlag = lambda xs: np.argmax(xs[::-1])
>>> pd.rolling_apply(ts, func=rmlag, window=3, min_periods=0).astype(i... | python|pandas | 3 |
4,433 | 13,572,576 | using 'OR' to select data in pandas | <p>I have a dataframe of values and I would like to explore the rows that are outliers. I wrote a function below that can be called with the <code>groupby().apply()</code> function and it works great for high or low values but when I want to combine them together i generate an error. I am somehow messing up the boolean... | <p>You need to use <code>|</code> instead of <code>or</code>. The <code>and</code> and <code>or</code> operators are special in Python and don't interact well with things like numpy and pandas that try to apply to them elementwise across a collection. So for these contexts, they've redefined the "bitwise" operators <... | python|pandas | 8 |
4,434 | 62,301,720 | How to multiply the numbers 0 to 150 by the same value? | <p>I have a Problem with my Python exercise.</p>
<p>Here is the part of my code:</p>
<pre><code>import numpy as np
import scipy as sc
import matplotlib.pyplot as plt
import math as m
import loaddataa as ld
dataListStride = ld.loadData("../Data/Fabienne")
indexStrideData = 0
strideData = dataListStride[indexStrideDa... | <p>If your list is a numpy array then you can literally just multiply it by 0.01 to get the result, but if you want to keep it as a python list for some reason, then a simple list comprehension solves this quite easily.</p>
<pre><code>new_ret = [0.01 * value for value in resultsHorizontal]
</code></pre>
<p>`</p> | python|list|numpy|matplotlib | 0 |
4,435 | 62,395,085 | Why does one need Google's WaveNet model to generate audio if it already takes audio as input? | <p>I've spent a lot of time trying to understand the <a href="https://arxiv.org/pdf/1609.03499.pdf" rel="nofollow noreferrer">Google's WaveNet work</a> (also used in their DeepVoice model), but still confused about some very basic aspects. I'm referring to <a href="https://github.com/Rayhane-mamah/Tacotron-2/blob/maste... | <p>The generative networks typically operate on conditional probability of getting <code>new_element</code> given <code>old_element(s)</code>. In math terms:</p>
<p><a href="https://i.stack.imgur.com/shSF1.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/shSF1.png" alt="conditional probability"></a><... | tensorflow|deep-learning|conv-neural-network|speech-recognition|text-to-speech | 1 |
4,436 | 51,526,800 | Setting up my decision tree python | <pre><code>import pandas as pd
import numpy as np
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from matplotlib import pyplot as plt
import seaborn as sns
from sklearn.tree import export_graphviz
import graphviz
import pydotplus
import ... | <p>I think this is a simple typo/mishearing. Scipy.misc does not have a function called <code>inread</code>. The function is called <code>imread</code>. Replace <code>scipy.misc.inread(path)</code> with <code>scipy.misc.imread(path)</code></p> | python|pandas|scipy|decision-tree|pydot | 0 |
4,437 | 51,341,568 | What is the difference between s[i] and s.iloc[i] in pandas series? | <p>The similar question has been asked <a href="https://stackoverflow.com/questions/45983801/pandas-iloc-vs-direct-slicing">here</a>, but I don't think it has a very perfect answer. If I am slicing a series rather than a data frame, what is the difference between these two? </p>
<p><code>s[i]</code> vs <code>s.iloc[i]... | <p>Indexing (using <code>[..]</code>) into a series (and a dataframe) acts as sort of a Swiss Army knife; it has to support a variety of use-cases, and these use-cases are not always compatible or efficient. Using <code>Series[...]</code> requires Pandas to check the datatype of both the object you passed and the curre... | python|pandas|slice | 2 |
4,438 | 48,198,319 | Groupby Pandas generate multiple fields with condition | <p>I have a pandas dataframe as such:</p>
<pre><code>df = pandas.DataFrame( {
"Label" : ["A", "A", "B", "B", "C" , "C"] ,
"Value" : [1, 9, 1, 1, 9, 9],
"Weight" : [2, 4, 6, 8, 10, 12} )
</code></pre>
<p>I would like to group the data by 'Label' and generate 2 fields.</p>
<ul>
<li>The First field, 'neww... | <p>Use <code>groupby.apply</code>, you can do:</p>
<pre><code>df.groupby('Label').apply(
lambda g: pd.Series({
"newweight": g.Weight[g.Value == 1].sum(),
"weightvalue": g.Weight.mul(g.Value).sum()
})).fillna(0)
# newweight weightvalue
#Label
#A 2.0 38.0
#B 14.0 14... | python|pandas|pandas-groupby | 4 |
4,439 | 48,715,480 | Pick the row and column of an DateFrame cell where the value true | <p>In a DataFrame I have to find a specific value. If it exists I need the cell coordinates (row/column). Currently I get only the row and struggle with the column. </p>
<pre><code>values = [[100.0, 127.0], [17.0, 24.13], [151.13, 0.0]]
df = pd.DataFrame(np.concatenate(values).reshape(3,2))
# df:
# 0 1... | <p>By using <code>np.where</code></p>
<pre><code>s,v=np.where(df==17)
df.columns[v]
Out[244]: Int64Index([0], dtype='int64')
df.index[s]
Out[245]: Int64Index([1], dtype='int64')
</code></pre> | python-3.x|pandas|dataframe | 2 |
4,440 | 48,636,600 | what is best way to use trained tensorflow in java | <p>I am coding UI by <code>javaFx</code> in <code>eclipse</code>
because I can only use java, no python, no c. </p>
<p>Now I try to use trained <code>tensorflow</code> file in this UI. (this <code>tensorflow</code> file is under python)</p>
<p>I am looking for several ways (<code>API</code>, <code>jython</code>, <co... | <p>I think there are two ways for that,</p>
<ol>
<li><code>tensorflow-serving</code> which uses <code>grpc</code> to connect from java to the serving server, making it independent of any language. Look <a href="https://www.tensorflow.org/serving/" rel="nofollow noreferrer">here</a> for details.</li>
<li>Use <code>tens... | java|python|tensorflow|jython | 0 |
4,441 | 48,487,245 | What to expect from deep learning object detection on black and white pictures? | <p>With TensorFlow, I want to train an object detection model with my own images based on ssd_inception_v2_coco model. The problem I have is that all my pictures are black and white. What performance can I expect? Should I try to colorize my B&W pictures first? Or at the opposite, should I try to retrain base netwo... | <p>I wouldn't go through the trouble of colorizing if you are planning on using a pretrained model. I would expect that explicitly colorizing your images as a pre-processing step would help very little (if at all) since in theory the features that a colorizing network learns can also be learned by the detection network... | tensorflow|deep-learning|object-detection | 2 |
4,442 | 48,726,418 | k nearest neighbors with cross validation for accuracy score and confusion matrix | <p>I have the following data where for each column, the rows with numbers are the input and the letter is the output.</p>
<pre><code>A,A,A,B,B,B
-0.979090189,0.338819904,-0.253746508,0.213454999,-0.580601104,-0.441683968
-0.48395313,0.436456904,-1.427424032,-0.107093825,0.320813402,0.060866105
-1.098818173,-0.99916169... | <p>I think your model does not get trained properly and because it only has to guess one value it doesn't get it right. May I suggest switching to KFold or StratifiedKFold. LOO has the disadvantage that for large samples it becomes extemely time consuming. Here is what happened when I implemented StratifiedKFold with 3... | python|pandas|machine-learning|scikit-learn|cross-validation | 3 |
4,443 | 71,074,196 | Pandas Multi Index Division | <p>I have a Multi Index dataframe that looks like</p>
<pre><code> Mid
Strike Expiration Symbol
167.5 2022-02-11 AAPL170 5.4
170 2022-02-11 AAPL170 3.1
2022-02-18 AAPL170 4.525
2022-02-25 AAPL170 5.25
2022-03-04 AAPL170 6.00
172.5 2022-02-11 AAPL172 1.... | <p>One way using <code>pandas.DataFrame.groupby</code> with <code>pct_change</code>:</p>
<pre><code>new_df = df.groupby(level=0).pct_change(-1) + 1
print(new_df)
</code></pre>
<p>Output:</p>
<pre><code> Mid
Strike Expiration Symbol
167.5 2022-02-11 AAPL170 NaN
170.0 202... | python|pandas|dataframe|multi-index | 0 |
4,444 | 51,804,671 | How to set the variables of LSTMCell as input instead of letting it create it in Tensorflow? | <p>When I create a tf.contrib.rnn.LSTMCell, it creates its <strong>kernel</strong> and <strong>bias</strong> trainable variables during initialisation.</p>
<p>How the code looks now:</p>
<pre><code>cell_fw = tf.contrib.rnn.LSTMCell(hidden_size_char,
state_is_tuple=True)
</code></pre>
<p>What ... | <p>I subclassed the LSTMCell class, and changed its <em>init</em> and <em>build</em>
methods so that they accept given variables. If variables are given in init
within build, we wouldn't use <em>get_variable</em> anymore, and would use the given kernel and bias variables.</p>
<p>There might be cleaner ways to do it th... | tensorflow|lstm | 2 |
4,445 | 51,572,338 | Getting:"ModuleNotFoundError: No module named 'tensorflow'", only when running from the command line | <p>I'm trying to run the Transformer (speech2text) model on windows (till I get my linux machine).
when I'm running the entire command from the cmd:</p>
<p>"python transformer_main.py --data_dir=$DATA_DIR --model_dir=$MODEL_DIR --params=$PARAMS"</p>
<p>I'm getting an error :"ModuleNotFoundError: No module named 'tens... | <p>Most likely you are simply using a different interpreter from your command line than from your PyCharm project. This will happen, for example, if you have set up your PyCharm project using a fresh conda environment.</p>
<p>To see which one you are using on the command-line, simply run <code>where python</code>. The... | python|tensorflow | 0 |
4,446 | 51,757,759 | Converting PNG image file into row of pixels for saving in dataframe | <p>I am working on an image dataset for classification. I want to store all the pixel values of the image in a single row in the pandas dataframe.
I am able to convert the image into a matrix and then into an array but when I'm saving this array, it is getting saved in the columns. </p>
<p>I used </p>
<pre><code>img ... | <p>Use <code>pd.DataFrame</code> on a list images to put each image on a separate row. Since there is only one image here, adding <code>[]</code> is enough depending on the output you want.</p>
<pre><code>df = pd.DataFrame([img])
</code></pre>
<p>will give</p>
<pre><code> 0 1 2 3 4
0 1.0 1.0 1.0 1.0 1.0... | python|pandas|numpy|image-processing | 3 |
4,447 | 51,959,497 | Replace missing values of a sequence in a column for each id of pandas dataframe | <p>I have a dataset:</p>
<pre><code>dt = {'id': [120,120,120,120,120,121,121,345], 'day': [0, 1,2,3,4,0,2,0], 'value': [[0.3,-0.5,-0.7],[0.5,3.4,2.7],[0.45,3.4,0.7],[0.25,0.4,0.7],[0.15,0.34,0.17],[0.35,3.4,2.7],[0.5,3.44,2.57],[0.5,0.34,0.37]]}
df = pd.DataFrame(data=dt)
day id value
0 0 120 [0.3, -0.5, -... | <p>Using <a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.MultiIndex.from_product.html" rel="nofollow noreferrer"><code>pd.MultiIndex.from_product</code></a>:</p>
<pre><code>idx = pd.MultiIndex.from_product([df.id.unique(), np.arange(5)], names=['id', 'day'])
out = (df.set_index(['id', 'day'])
... | python|pandas|dictionary|dataframe|padding | 1 |
4,448 | 41,710,501 | Is there a way to have a dictionary as an entry of a pandas Dataframe in python? | <p>Something like: </p>
<pre><code>d={'a':1, 'b'=2}
data=pandas.DataFrame()
data['new column'] = d
data['new column'][0]
</code></pre>
<p>where the last command will return the dictionary d?</p> | <p>You can wrap the dictionary in a list, so that the dictionary will be treated as an element instead of an iterable:</p>
<pre><code>d={'a':1, 'b': 2}
data=pd.DataFrame()
data['new column'] = [d]
data['new column'][0]
# {'a': 1, 'b': 2}
</code></pre> | python|pandas|dataframe | 6 |
4,449 | 64,596,588 | splitting pandas column containing list of dicts | <p>I have a pandas column, with each cell in the column containing a list of dicts with color attributes of each photo, such as:</p>
<pre><code>[{'color': 'black', 'confidence': 1.0}, {'color': 'brown', 'confidence': 0.72}, {'color': 'gray', 'confidence': 0.62}, {'color': 'other', 'confidence': 0.52}, {'color': 'red', ... | <pre><code>a = [{'color': 'black', 'confidence': 1.0}, {'color': 'brown', 'confidence': 0.72}, {'color': 'gray', 'confidence': 0.62}, {'color': 'other', 'confidence': 0.52}, {'color': 'red', 'confidence': 0.01}, {'color': 'blond', 'confidence': 0.01}, {'color': 'white', 'confidence': 0.0}]
c= []
co = []
for d in a:
... | python|pandas | 1 |
4,450 | 64,433,604 | rearrange columns of multidimensional arrays efficiently | <p>I'm fairly certain this problem has an almost trivial solution, but for the life of me I can't seem to figure it out right now, nor find anything online, so bear with me please.</p>
<p>I have a 3D array of size (n x m x m), called v (think of it as n (m x m)-matrices.)
And I wish to rearrange the columns in each mat... | <p>Use the iterator as a ranged-array for advanced-indexing for a vectorized way -</p>
<pre><code>I = np.arange(v.shape[0])[:,None]
V[I,:,np.arange(len(idxs[0]))] = v[I,:,idxs]
</code></pre>
<p>Another with simply indexing into <code>v</code> to directly get <code>V</code> -</p>
<pre><code>V = v[np.arange(v.shape[0])[:... | python|numpy|optimization|linear-algebra | 2 |
4,451 | 64,395,296 | Pandas data frame not exporting to excel properly | <p>I am not being able to produce a csv file with all the data from the scraper.</p>
<p>When I test one item, it works properly, the exported csv has all the columns and one row with the corresponding value.</p>
<p>when I try to apply the csv to all the code, it just doesn't work.</p>
<p>Can someone tell me what am I d... | <p>I guess it's because <code>alldata</code> is empty - you never filled it with scraped data.</p>
<p>Try adding</p>
<pre><code> try:
soup = BeautifulSoup(requests.get(soup.iframe['src']).content, 'html.parser')
number = soup.find(text=lambda t: t.strip().startswith('Item no.')).find_next('div').get_... | python|pandas|dataframe|web-scraping | 0 |
4,452 | 64,324,685 | Why my PCA is not invariant to rotation and axis swap? | <p>I have a voxel (np.array) with size 3x3x3, filled with some values, this setup is essential for me. I want to have rotation-invariant representation of it. For this case, I decided to try PCA representation which is believed to be <a href="https://stats.stackexchange.com/questions/239069/is-pca-invariant-to-orthogon... | <p>Firstly, your <code>pca</code> function is not correct, it should be</p>
<pre><code>def pca(x):
x -= np.mean(x,axis=0)
cov = np.cov(x.T)
e_values, e_vectors = np.linalg.eig(cov)
order = np.argsort(e_values)[::-1]
v = e_vectors[:,order]
return x @ v
</code></pre>
<p>You shouldn't tr... | python|numpy|math|pca|voxel | 4 |
4,453 | 64,577,657 | Appending values to an array for every iteration | <p>I am trying to store the indices inside my test array inside two other arrays.</p>
<pre><code>train_idx = np.zeros(3,dtype='object')
test_dx = np.zeros(3,dtype='object')
array = np.array([1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1])
test = np.array(np.array_split(array, k))
[array([1, 0, 0, 1, 0]) array([1, 1, 1, 0]) ar... | <p>Define test_index and train_index as empty list:</p>
<pre><code>test_index = []
</code></pre>
<p>Then use append method:</p>
<pre><code>test_index.append(test[i])
</code></pre> | python|arrays|numpy | 0 |
4,454 | 64,530,885 | TFX Pipeline Error While Executing TFMA: AttributeError: 'NoneType' object has no attribute 'ToBatchTensors' | <p>Basically I only reused code from <a href="https://github.com/tensorflow/tfx/blob/master/tfx/examples/iris/iris_utils_native_keras.py" rel="nofollow noreferrer">iris utils</a> and <a href="https://github.com/tensorflow/tfx/blob/master/tfx/examples/iris/iris_pipeline_native_keras.py" rel="nofollow noreferrer">iris pi... | <p>So in the end I was mistaken about the evaluator component, or more appropriately if I address the TFMA instead. it indeed uses the serving input function defined in serving signatures. According to <a href="https://www.tensorflow.org/tfx/guide/evaluator" rel="nofollow noreferrer">this link</a>, the default signatur... | python-3.x|tensorflow|tensorflow-serving|tensorflow-model-analysis | 1 |
4,455 | 47,643,264 | Pandas rank method dense but skip a number | <p>I have a sample data set that i'm trying to rank based on the values in the column 'HP':</p>
<pre><code>import pandas as pd
d = {
'unit': ['UD', 'UD', 'UD' ,'UC','UC', 'UC','UA','UA','UA','UB','UB','UB'],
'N-D': [ 'C1', 'C2', 'C3','Q1', 'Q2', 'Q3','D1','D2','D3','E1','E2','E3'],
'HP': [24, 24, 24,7,7,7,7,7,7,5,... | <p>Use a combination of <code>groupby</code> and <code>sort_values</code></p>
<pre><code>g = df.sort_values(
['HP', 'unit'], ascending=False
).groupby(['HP', 'unit'], sort=False)
df.assign(rank=g.ngroup().add(1).groupby(df.HP).transform('first'))
HP N-D unit rank
0 24 C1 UD 1
1 24 C2 UD 1
... | python|pandas|rank | 5 |
4,456 | 47,592,512 | Adding column in pandas with several conditions based on other columns in dataframe | <p>Firstly, I apologise if this is already somewhere on StackOverflow, I searched for an hour after experimenting myself for an hour and couldn't find it. I'm sure there must be an elegant (and probably elementary) solution.</p>
<p>I have the following data frame:</p>
<pre><code> Admit Gender Dept Freq
0 A... | <p>You can divide column by <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.div.html" rel="nofollow noreferrer"><code>div</code></a> with <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.core.groupby.GroupBy.transform.html" rel="nofollow noreferrer"><code>transform</cod... | python|pandas|dataframe|conditional|match | 1 |
4,457 | 49,045,210 | How to checkwhether an index in a tensorarray has been initialized? | <p>Is it in anyway possible to check whether an index in a TensorArray has been initialized?</p>
<p>As I understand TensorArrays can't be initialized with default values.
However I need a way to increment the number on that index which I try to do by reading it, adding one and then writing it to the same index.
If the... | <p>The only option as I see it is creating an initialization loop where every index is set to 0. This eliminates the problem but may not be an ideal way.</p> | tensorflow|python-3.5 | 0 |
4,458 | 48,926,511 | TensorFlow Assign | <p>I am trying to write a custom version of an RNN and would like to just store the state and last output of the cells in variables but it is not working. My guess is that TensorFlow sees the storing of the values unnecessary and does not execute it. Here is a snippet that illustrates the problem.</p>
<p>For this ex... | <p>Make it dependent on an operation that will be run, in this example I'll use the cost function, but use whatever makes sense:</p>
<pre><code>with tf.control_dependencies(cost):
tf.assign(last_output, cell_output)
</code></pre>
<p>Now the assign operation will be required in order for <code>cost</code> to be comp... | python|tensorflow|assign | 0 |
4,459 | 49,330,080 | NumPy 2D array: selecting indices in a circle | <p>For some rectangular we can select all indices in a 2D array very efficiently:</p>
<pre><code>arr[y:y+height, x:x+width]
</code></pre>
<p>...where <code>(x, y)</code> is the upper-left corner of the rectangle and <code>height</code> and <code>width</code> the height (number of rows) and width (number of columns) o... | <p>You could define a mask that contains the circle. Below, I have demonstrated it for a circle, but you could write any arbitrary function in the <code>mask</code> assignment. The field <code>mask</code> has the dimensions of <code>arr</code> and has the value <code>True</code> if the condition on the righthand side i... | python|arrays|numpy|indexing | 16 |
4,460 | 58,901,094 | Select corresponding values to a instant t in columns | <p>I work in Python and i have a pandas Dataframe with an evolution of steps at differents months :</p>
<pre><code>+----+-------+------+
| Id | Month | Step |
+----+-------+------+
| a | 1 | a_1 |
| a | 4 | a_2 |
| a | 6 | a_3 |
| b | 1 | a_1 |
| b | 2 | a_4 |
+----+-------+------+
</code... | <p>Use <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.pivot_table.html" rel="nofollow noreferrer"><code>DataFrame.pivot_table</code></a>:</p>
<pre><code>new_df=df.pivot_table(index='Id',columns='Month',values='Step',aggfunc=''.join).add_prefix('Month_').rename_axis(columns=None)
print(new_d... | python|pandas | 1 |
4,461 | 58,743,756 | How to read images from a list | <p>Hi I have a set of images stored as list items and I want to read the images one by one and perform some operations on it. I cannot figure out how to iterate through each image item in the list. I can read them explicitly from a folder using <code>cv2.imread</code> but I want to make use of the list element in which... | <p><code>list</code> is itself a iterator just iterate over it.</p>
<pre><code>for file in align:
# code here...
</code></pre> | python|list|numpy|opencv|image-processing | 0 |
4,462 | 58,936,694 | Is it possible to apply a function to one column when reading a data file? | <p>Is there a way to apply directly a Series operation (buid in function or custom) when building a dataframe from a file (in a pythonic way)?</p>
<p>I would like to change the following:</p>
<pre><code># import data frame containing a custom timestamp column (ex: _2019_11_19_15_10_35_)
df1 = pd.read_csv('mydatafile.... | <p>Well you can assign another Timestamp column, erasing the previous one:</p>
<pre class="lang-py prettyprint-override"><code>df1 = pd.read_csv('mydatafile.csv').assign(
newcol='newval',
Timestamp=lambda df: pd.to_datetime(df['Timestamp'], format='_%Y_%m_%d_%H_%M_%S_'))
</code></pre> | python|pandas|dataframe|series | 1 |
4,463 | 70,059,491 | bumping to good business day when generating range | <p>I am using pandas pd.bdate_range() to generate a range of dates given a start and end, but it seems to not work as expected.</p>
<p>What I am ultimately after is quarterly dates over a start and end date, but I want the dates to be valid business days.</p>
<pre><code>start = '2015-06-01'
end = '2019-06-01'
dates = ... | <p>You can take your existing Series and increment to the next business day like so</p>
<pre class="lang-py prettyprint-override"><code>from pandas.tseries.offsets import BDay
start = '2015-06-01'
end = '2019-06-01'
dates = pd.bdate_range(start,end,freq='MS')[::3]
new_dates = dates.map(lambda x : x + 0*BDay())
</code>... | python-3.x|pandas|date | 1 |
4,464 | 70,347,700 | Generate DF from attributes of tags in list | <p>I have a list of revisions from a Wikipedia article that I queried like this:</p>
<pre><code>import urllib
import re
def getRevisions(wikititle):
url = "https://en.wikipedia.org/w/api.php?action=query&format=xml&prop=revisions&rvlimit=500&titles="+wikititle
revisions = [] ... | <p>An "easy" way without using regex would be splitting the string and then parsing:</p>
<pre><code>for rev_string in revisions:
rev_dict = {}
# Skipping the first and last as it's the tag.
attributes = rev_string.split(' ')[1:-1]
#Split on = and take each value as key and value and convert ... | python|python-3.x|pandas|wikipedia|mediawiki-api | 2 |
4,465 | 70,088,916 | Format Pandas date array with mixed formats | <p>I'm trying to unify dates in a column as they come in different formats; current date entries:</p>
<p>[... '18-Aug-21' '16-Aug-21' '17-Aug-21'
'22-Aug-21' '21-Aug-21' '20-Aug-21' '19-Aug-21' '23-Aug-21' '24-Aug-21'
'25-Aug-21' '28-Aug-21' '26-Aug-21' '27-Aug-21' '31-Aug-21' '30-Aug-21'
'29-Aug-21' '06 Sep 2021' '07 ... | <p>I recommend <a href="https://pypi.org/project/python-dateutil/" rel="nofollow noreferrer"><code>dateutil</code></a> for this:</p>
<pre class="lang-py prettyprint-override"><code>import dateutil
DF_all["SALES_DATE"] = DF_all["SALES_DATE"].apply(dateutil.parser.parse)
</code></pre>
<p>Output:</p>
<... | python|pandas|dataframe|format | 1 |
4,466 | 56,348,725 | Pandas - Replace values based on index and not in index | <p>Here is the sample code.</p>
<pre><code>import pandas as pd, numpy as np
df = pd.DataFrame(np.random.randint(0,100,size=(10, 1)), columns=list('A'))
</code></pre>
<p>I have a list dl=[0,2,3,4,7]</p>
<p>At the index positions specified by list, I would like to have column A as "Yes".</p>
<p>The following code wo... | <h3><code>np.where</code></h3>
<p>I'm making an assumption that there is a better way to do both <code>'Yes'</code> and <code>'No'</code> at the same time. If you truly just want to fill in the <code>'No'</code> after you've already got the <code>'Yes'</code> then refer to <a href="https://stackoverflow.com/a/5634890... | python|pandas|dataframe | 6 |
4,467 | 56,288,089 | Unexpected behaviour from applying np.isin() on a pandas dataframe | <p>While working <a href="https://stackoverflow.com/a/56286723/565489">on an answer to another question</a>, I stumbled upon an unexpected behaviour:</p>
<p>Consider the following DataFrame:</p>
<pre class="lang-py prettyprint-override"><code>df = pd.DataFrame({
'A':list('AAcdef'),
'B':[4,5,4,5,5,4],
'E':... | <p>I think in DataFrame are mixed numeric with integer solumns, so if loop by rows get <code>Series</code> with mixing types, so numpy coerce the to <code>strings</code>.</p>
<p>Possible solution is convert to array and then to <code>string</code> values in <code>cond</code>:</p>
<pre><code>cond = [[4],[5]]
print(df... | python|pandas|numpy | 2 |
4,468 | 56,047,379 | Problem with Tensorflow iterator returning tuples | <p>I want to iterate over a TF dataset in order to convert the obtained data to numpy tensors. Being new to tensorflow, this is what my code looks like</p>
<pre><code> def convert_dataset_to_pytorch(self, dataset):
sess = tf.Session(config=self.config)
iterator = dataset.make_one_shot_iterator()
exampleT... | <p>Not sure why you are creating a pytorch tensor in tensorflow when all you want is a numpy tensor. To answer your question (mentioned below)</p>
<blockquote>
<p>iterate over a TF dataset in order to convert the obtained data to
numpy tensors.</p>
</blockquote>
<h3>Sample Code:</h3>
<pre><code>import numpy as n... | python|numpy|tensorflow|tuples|tensorflow-datasets | 2 |
4,469 | 56,280,602 | Matplotlib / Seaborn legend changes style upon adding labels | <p>I'm plotting some of my data from a pandas df using seaborn. Almost everything plots nicely using the following code:</p>
<pre class="lang-py prettyprint-override"><code>import pandas as pd
import seaborn as sns
sns.set(style='whitegrid', palette='muted')
legend = ["Hue 1", "Hue 2"]
order = ["A", "B"]
ax = sns.v... | <p>You could try to draw custom patches for your legend. I haven't tested this but I think that it should work.</p>
<pre><code>from matplotlib.patches import Patch
palette=sns.color_palette('muted')
bluepatch = Patch(
facecolor=palette[0],edgecolor='k',label='Hue 1'
)
orangepatch = Patch(
facecolor=palette[1]... | python|pandas|matplotlib|seaborn | 1 |
4,470 | 55,766,304 | Coloring cells in pandas according to their relative value | <p>I would like to color the cells of a (python) pandas dataframe according to wether their value is in the top 5%, top 10%, ..., last 10%, last 5% of the data in this column. </p>
<p>According to this post <a href="https://stackoverflow.com/questions/28075699/coloring-cells-in-pandas">Coloring Cells in Pandas</a>, on... | <p>Try this:</p>
<pre><code>df = pd.DataFrame(np.arange(100).reshape(20,-1))
def colorme(x):
c = x.rank(pct=True)
c = np.select([c<=.05,c<=.10,c>=.95,c>=.90],['red','orange','yellow','green'])
return [f'background-color: {i}' for i in c]
df.style.apply(colorme)
</code></pre>
<p><a href="http... | python|python-3.x|pandas | 0 |
4,471 | 55,893,511 | "Dependency was not found" for tfjs in Vue/Webpack project with yarn | <p>I'm trying to use TensorFlow.js for array operations in a JavaScript project. I'm importing it in my Vue component with <code>import * as tf from '@tensorflow/tfjs';</code></p>
<p>It appears <code>yarn install tensorflow</code> requires Python 2.7, so I instead used <code>yarn add tensorflow/tfjs</code> to install ... | <p>Thanks to a friend, I have a solution:</p>
<p><code>yarn remove @tensorflow/tfjs</code></p>
<p>and</p>
<p><code>yarn add @tensorflow/tfjs</code></p>
<p>I guess it got in a weird state when the first attempt to install tensorflow failed. (It's depressing how often the answer is "turn it off and on again"...)</p> | javascript|tensorflow|vue.js|webpack | 1 |
4,472 | 55,885,970 | Matplotlib, legends are not appearing in the histogram | <p>to describe my problem i am providing you with small dataset as an example: so imagine following dataset:</p>
<pre><code>import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
df = pd.DataFrame({'name':['a', 'b', 'c', 'a', 'b', 'c'], 'val':[1,5,3,4,5,3]} )
</code></pre>
<p>I am creating simple hist... | <p>You only plot once on one series, that's why <code>plt</code> only picks one label for legend. If you don't have a lot of names, try:</p>
<pre><code>def plot_bar_x():
index = np.arange(len(df['name']))
plt.figure()
for name in df.name.unique():
tmp_df = df[df.name == name]
plt.bar(tmp_df... | python|pandas|matplotlib|histogram|legend | 1 |
4,473 | 55,606,347 | Append 2 pandas dataframes with subset of rows and columns | <p>I have 2 dataframes like this</p>
<pre><code>df = pd.DataFrame({"date":["2019-01-01", "2019-01-02", "2019-01-03", "2019-01-04"],
"A": [1., 2., 3., 4.],
"B": ["a", "b", "c", "d"]})
df["date"] = pd.to_datetime(df["date"])
df_new = pd.DataFrame({"date":["2019-01-02", "2019-01-03"... | <p>Use <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.concat.html" rel="nofollow noreferrer"><code>concat</code></a> and remove duplicates by <code>date</code> column by <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop_duplicates.html" rel="nofollow nor... | python|pandas | 3 |
4,474 | 64,780,469 | tf.keras "All layer names should be unique." but layer names are already changed | <p>I am trying create an ensemble of lstm. Below is my implementation of one lstm:</p>
<pre><code>def lstm_model(n_features, n_hidden_unit, learning_rate, p, recurrent_p):
model = keras.Sequential()
model.add(Masking(mask_value=-1, input_shape=(100, n_features)))
model.add(Bidirectional(LSTM(n_hidden_unit, inp... | <p>Not sure if you ever fixed this, or if anyone else will find the same issue; I just spent hours on the same problem and finally found the solution.</p>
<p>When listing layers for renaming, instead of <code>model.layers</code>, you have to use <code>model._layers</code>, otherwise the input name remains unchanged. Ch... | python|tensorflow|keras|keras-layer | 2 |
4,475 | 64,871,055 | Importing all the sql tables into python using pandas dataframe | <p>I have an requirement wherein I would like to import all the tables stored in the sql database into python. I have successfully created a python code for it as follows:</p>
<code>
<pre><code>import pandas as pd
import mysql.connector
from pandas import DataFrame
db = mysql.connector.connect(host="localhost"... | <p>The SQL error come from ' around the table name, It's not the best but if you are working on a local application, you can workaround with the f strings:</p>
<pre><code>with connection.cursor() as pointer:
pointer.execute("use use stock")
pointer.execute("SHOW TABLES")
tables_tuples=[... | python|mysql|pandas | 1 |
4,476 | 64,699,496 | How to compute loss of chained models with Keras? | <p>For testing, I have split a model into two models and I want to compute the loss and apply the gradient to both models like it would be one.</p>
<p>Here are my two simple models:</p>
<pre><code>model1 = tf.keras.models.Sequential([
tf.keras.layers.Dense(10, activation="relu", input_shape=(10,)),
])
mo... | <p>@Begoodpy,
I suggest you combine the 2 models into a single one and train it as you would usually do.</p>
<pre><code>supermodel = keras.Sequential(
[
model1(),
model2(),
]
</code></pre>
<p>If you need more control over the models, try this:</p>
<pre><code>all_vars = model1.trainable_variables + m... | python|tensorflow|keras | 2 |
4,477 | 64,979,047 | Can´t save in a "to_csv" with pandas | <p>I have been trying to sabe some usersnames and passwords at the end of my code with the "to_csv" but it doesn´t save anything at all. The console does not show any message or output file at all. Can´t find whtas the problem.</p>
<pre><code>def registrar():
df = pd.read_csv(r"C:\Users\Usuario\Docum... | <p>You are not getting any message because you missed a break and are entering an infinity loop in your last else condition. Thats why you should always avoid using while true loops.</p>
<p>And also you need to reassign <code>df</code> in order to write it to the csv file.</p>
<p>Change your else for this and should wo... | python|pandas|csv | 0 |
4,478 | 64,629,823 | Accuracy of binary classification on MNIST “1” and “5” get better? | <p>I tried the binary classification using MNIST only number “1” and “5”.
But the accuracy isn’t well.. The following program is anything wrong?
If you find something, please give me some advice.</p>
<p>loss: -9.9190e+04</p>
<p>accuracy: 0.5599</p>
<pre class="lang-py prettyprint-override"><code>import tensorflow as tf... | <p>Your y_train and y_test is filled with class labels 1 and 5, but sigmoid activation in your last layer is squashing output between 0 and 1.</p>
<p>if you change 5 into 0 in your y you will get a really high accuracy:</p>
<pre><code>y_train = np.where(y_train == 5, 0, y_train)
y_test = np.where(y_test == 5, 0, y_test... | tensorflow|machine-learning|prediction|mnist | 2 |
4,479 | 65,017,040 | How to convert pandas DataFrame to dictionary for Newick format | <p>I have the following dataset:</p>
<pre><code>import pandas as pd
df = pd.DataFrame([['root', 'b', 'a', 'leaf1'],
['root', 'b', 'a', 'leaf2'],
['root', 'b', 'leaf3', ''],
['root', 'b', 'leaf4', ''],
['root', 'c', 'leaf5', ''],
... | <p>I assumed that every row in your dataframe stands for one complete branch of the tree from the root to the leaves. Based on this, I came up with the following solution. Comments to each step in the algorithm can be found in the code below, but feel free to ask if anything is unclear.</p>
<pre><code>node_to_children ... | python|pandas | 1 |
4,480 | 39,885,928 | Variable shape tensor | <p>I need to get a tensor that is variable shape as I do not know the vector size before hand. So far I tried: </p>
<pre><code>hashtag_len = tf.placeholder(tf.int32)
train_hashtag = tf.placeholder(tf.int32, shape=[hashtag_len])
</code></pre>
<p>but I get the error <code>TypeError: int() argument must be a string, a b... | <p>If you want a VECTOR for sure, you should do the following:</p>
<pre><code>train_hashtag = tf.placeholder(tf.int32, shape=[None])
</code></pre>
<p>This shape describes vector of arbitrary length.</p> | python|tensorflow | 3 |
4,481 | 44,064,299 | How can I concatenate Pandas DataFrames by column and index? | <p>I've got four Pandas DataFrames with numerical columns and indices:</p>
<pre><code>A = pd.DataFrame(data={"435000": [9.792, 9.795], "435002": [9.825, 9.812]}, index=[119000, 119002])
B = pd.DataFrame(data={"435004": [9.805, 9.783], "435006": [9.785, 9.78]}, index=[119000, 119002])
C = pd.DataFrame(data={"435000": [... | <p>You need concat in pairs:</p>
<pre><code>result = pd.concat([pd.concat([A, C], axis=0), pd.concat([B, D], axis=0)], axis=1)
print (result)
435000 435002 435004 435006
119000 9.792 9.825 9.805 9.785
119002 9.795 9.812 9.783 9.780
119004 9.778 9.750 9.743 9.762
119006 9.743 9.74... | python|pandas | 16 |
4,482 | 44,289,277 | Tensorflow: Import failed with "Failed to load the native TensorFlow runtime." | <p>I'm trying to install the CPU version of tensorflow in a virtualenv on Mac OS 10.6.8 (all I've got for now) with Python 3.6, using the package url as described <a href="https://www.tensorflow.org/install/install_mac#python_34_35_or_36" rel="nofollow noreferrer">here</a>. It seems to work fine:</p>
<pre><code>Ms-Mac... | <blockquote>
<p>ImportError: dlopen(/Users/M/Developer/tensorflow/tfvenv/lib/python3.6/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so, 10): Library not loaded: /usr/lib/libc++.1.dylib</p>
</blockquote>
<p>libc++ was not included in MacOS 10.6 because Apple had not switched to Clang and libc++ yet. Th... | python|tensorflow|pip | 1 |
4,483 | 69,594,876 | DataFrame. Split Column to antoher Columns with average by date | <p>There is a dataframe:</p>
<pre><code>d = {'date' : ['2020-02-01', '2020-02-01', '2020-02-01', '2020-02-01', '2020-02-02', '2020-02-02', '2020-02-02'], 'type' : ['Bird', 'Dog', 'Cat', 'Bird', 'Dog', 'Cat', 'Bird'], 'weight' : [1, 2, 3, 4, 5, 6, 7]}
df = pd.DataFrame(d)
</code></pre>
<p>I would like to split the &quo... | <p>Use <code>pivot_table</code> and apply <code>mean</code> to aggregate values those has the same index/column:</p>
<pre><code>out = df.pivot_table(index='date', columns='type', values='weight', aggfunc='mean') \
.rename_axis(columns=None).reset_index()
print(out)
# Output:
date Bird Cat Dog
0 20... | pandas|dataframe | 0 |
4,484 | 69,303,487 | What is the pythonic way to convert day and month in string format to date in python | <p>I have day and month in string format <strong>23rd Sep</strong>, I would like to convert the above string to the current date <strong>23/09/2021</strong>. I achieved that using the below code, what is the more pythonic way to do this.?</p>
<pre><code>from datetime import datetime
# datetime object containing curren... | <p>I don't see the problem:</p>
<pre><code>import pandas as pd
s = '23rd Sep'
pd.Timestamp(s + ' 2021')
Out[1]: Timestamp('2021-09-23 00:00:00')
</code></pre>
<p>If you want it in DMY format:</p>
<pre><code>_.strftime('%d/%m/%Y')
Out[2]: '23/09/2021'
</code></pre> | python|pandas|dataframe | 2 |
4,485 | 41,221,084 | Apply VLOOKUP in pandas and python | <p>I have a csv called 'data.csv' which has:</p>
<pre><code>EmployeeIDNumber
A
B
C
D
</code></pre>
<p>I have another csv called 'basic.csv' which has the same data but is jumbled:</p>
<pre><code>MemberIdentifier
B
A
C
</code></pre>
<p>I want to use PANDAS to create a result sheet which has:</p>
<pre><code>Employee... | <p>There are several ways to do this but the most powerful is the following,</p>
<pre><code>import pandas as pd
df1 = pd.csv_read('data.csv')
df = merge(df1, df2, left_on='EmployeeIDNumber', right_on='MemberIdentifier', how='left')
</code></pre>
<p>Here we are choosing the specific columns we wish to join our DataF... | python|csv|pandas | 1 |
4,486 | 54,061,753 | Creating Estimation Windows based on a Criteria (DataFrame) | <p>I am looking at how I can select a couple of rows (specifically -15 until -5) based on a specific criteria.</p>
<p>We have a list of Events (dates) and a large DataFrame with all BitCoin orders, ordered by Date. In this DataFrame we have a column that marks a row with 'True' if the value in Events is found in the D... | <p>Here is an example. FYI. It's normally easier to answer these when you post some code that generates some test dataset :)</p>
<p>First, here is a dataset. Here we're basically trying to select based on the True values. But we only want 1 before and 1 after, so we shouldn't see any gone. </p>
<pre><code>import pand... | python|pandas|dataframe | 0 |
4,487 | 54,099,431 | Locate rows by particular label, found only in last multi-index level | <p>After performing group-by, my new df has 3 level multindex. I need to access all rows with 'ZEBRA' labels; which is contained in the 3rd level index. I'm trying to use <code>df.loc</code> but unable to do so. I thought of iterating through the labels, but that will have to be a nested loop to make below; which makes... | <p>You can do:</p>
<pre><code>grouped[grouped.index.get_level_values(2) == 'ZEBRA'].reset_index()
A B C D
0 bar one ZEBRA 1
1 bar three ZEBRA 1
2 foo three ZEBRA 1
3 foo two ZEBRA 1
</code></pre>
<p>Alternate way: <code>grouped.query("C == 'ZEBRA'").reset_index()</code></p> | python|python-3.x|pandas | 1 |
4,488 | 66,036,271 | Splitting a tensorflow dataset into training, test, and validation sets from keras.preprocessing API | <p>I'm new to tensorflow/keras and I have a file structure with 3000 folders containing 200 images each to be loaded in as data. I know that <a href="https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image_dataset_from_directory" rel="nofollow noreferrer">keras.preprocessing.image_dataset_from_directory... | <p>I could not find supporting documentation, but I believe <code>image_dataset_from_directory</code> is taking the end portion of the dataset as the validation split. <code>shuffle</code> is now set to <code>True</code> by default, so the dataset is shuffled before training, to avoid using only some classes for the va... | python|tensorflow|keras | 3 |
4,489 | 66,193,817 | Pandas dataframe groupby remove column | <p>I stumble upon with a problem with dataframe.
I am using this snippet code to generate dataframe after that I group by dataframe based on <strong>‘chr’
Column</strong>.</p>
<pre><code>import pandas as pd
DF = pd.DataFrame({'chr':["chr3","chr3","chr7","chr6","chr1", ... | <p>You can do it while creating your original list like this if there are only two columns:</p>
<pre><code>ans = [pd.DataFrame(y, columns=DF.columns.difference(['chr'])) for x, y in DF.groupby('chr', as_index=False)]
</code></pre>
<p>Alternatively, drop <code>chr</code> from each subDf explicitly:</p>
<pre><code>an... | python|pandas|dataframe | 1 |
4,490 | 52,656,884 | ImportError: No module named pandas_datareader | <p>I am trying to use the pandas data reader library.
I initially tried <code>import pandas.io.data</code> but this threw up an import error, stating I should be using </p>
<blockquote>
<p>from pandas_datareader import data, wb</p>
</blockquote>
<p>instead. Upon trying this I was greeted with </p>
<blockquote>
<... | <p>run this command <code>pip install pandas-datareader</code> and for more info documentation is <a href="https://pandas-datareader.readthedocs.io/en/latest/" rel="nofollow noreferrer">here</a> </p> | python|pandas|importerror | 0 |
4,491 | 52,881,426 | Python Pandas Create Column Based on a Function Requiring to Filter the DataFrame | <p>I have a larger script with multiple functions. In one of those functions I am creating a dataframe and then creating a column applying a separate function.</p>
<p>The function to create dataframe at a high level:</p>
<pre><code>def data(file):
df = pd.DataFrame('A': [1,2,3,4], 'B':[5,5,6,6]
df['C'] = df['B'].... | <p>Using <code>map</code> after <code>groupby.apply</code> (PS: Not recommend using list in column , which will make adjustment harder)</p>
<pre><code>df['C']=df.B.map(df.groupby('B').A.apply(list))
df
Out[872]:
A B C
0 1 5 [1, 2]
1 2 5 [1, 2]
2 3 6 [3, 4]
3 4 6 [3, 4]
</code></pre> | python|pandas | 4 |
4,492 | 52,776,723 | After converting image to binary cannot display it in notebook with matplotlib | <p>I am trying to display the image After coverting the image to binary in python notebook:</p>
<pre><code>resized_img = cv2.cvtColor(char_mask, cv2.COLOR_BGR2GRAY)
resized_img = cv2.threshold(resized_img, 100, 200, cv2.THRESH_BINARY)
#cv2.imwrite('licence_plate_mask3.png', char_mask)
plt.imshow(resized_img)
plt.show... | <p>The error lies in line <code>resized_img = cv2.threshold(resized_img, 100, 200, cv2.THRESH_BINARY)</code>.</p>
<p><code>cv2.threshold()</code> returns two values. The first is the threshold value (float) and the second is the image. </p>
<p>So all the while you have been trying to plot a <code>float</code> value, ... | python|numpy|opencv|matplotlib|imshow | 1 |
4,493 | 52,684,961 | Pandas - Rename only first dictionary match instead of last match | <p>I am trying to use pandas to rename a column in CSV files. I want to use a dictionary since sometimes columns with the same information can be named differently (e.g. mobile_phone and telephone instead of phone).</p>
<p>I want to rename the first instance of phone. Here is an example to hopefully explain more.</p>
... | <p>Here's one solution:</p>
<p><code>df</code>:</p>
<pre><code>Columns: [name, mobile_phone, telephone]
Index: []
</code></pre>
<p>Finding the first instance of phone (left to right) in the column index:</p>
<pre><code>a = [True if ('phone' in df.columns[i]) & ('phone' not in df.columns[i-1]) else False for i i... | python|pandas|dictionary|indexing|python-3.6 | 0 |
4,494 | 52,533,156 | Weight Initialization Tensorflow tf.estimator | <p>Is there a way to adjust the weight initialization in the pre-built tf.estimator?
I would like to use the method after Xavier (<code>tf.contrib.layers.xavier_initializer</code>) or from He. Which method is used by default? I couldn't figure it out from the documentation. </p>
<p>I use the DNNRegressor.</p> | <p><code>DNNRegressor</code> uses <a href="https://www.tensorflow.org/api_docs/python/tf/glorot_uniform_initializer" rel="nofollow noreferrer">glorot_uniform_initializer</a> (aka Xavier uniform), it is hardcoded in the <a href="https://github.com/tensorflow/tensorflow/blob/4dcfddc5d12018a5a0fdca652b9221ed95e9eb23/tenso... | tensorflow|initialization | 2 |
4,495 | 58,538,135 | Keras methods 'predict' and 'predict_generator' with different result | <p>I have trained a basic CNN model for image classification.
While training the model I have used ImageDataGenerator from keras api.
After the model is being trained i used testdatagenerator and flow_from_directory method for testing.
Everything Went well.
Then I saved the model for future use.
Now i am using the same... | <p><code>Keras generator</code> uses <code>PIL</code> for image reading which read images from disk as <code>RGB</code>.</p>
<p>You are using <code>opencv</code> for reading which reads images as <code>BGR</code>. You have to convert your image from <code>BGR</code> to <code>RGB</code>.</p>
<pre><code>img = cv2.imrea... | python-3.x|tensorflow|machine-learning|keras|conv-neural-network | 0 |
4,496 | 58,194,296 | How to convert currency values in DataFrame column? | <p>I've got a dataframe which has columns - </p>
<pre><code>Product Price in AUD Price in BTC Price in USD Date
A 1450.22 0.120 NaN 2019-08-15
B NaN NaN 550 2019-09-12
C NaN 0.18 15... | <p>You can use the <a href="https://pypi.org/project/forex-python/" rel="nofollow noreferrer">forex-python</a> package for that:</p>
<pre class="lang-py prettyprint-override"><code>import pandas as pd
import datetime
from forex_python.converter import CurrencyRates
from forex_python.bitcoin import BtcConverter
data =... | python-3.x|pandas | 1 |
4,497 | 58,383,737 | R keras says array doesnt have the right dimensions for a conv_2d, but it drops a value from the array that was correct | <p>When making a conv neral network keras takes the imput_shape of c(28, 28, 1), but when I run it to be trained it then tells me the input it got was (6000, 28, 28). I understand keras inputs the data size it's self, but why it is dropping the one, then causing it to brake?</p>
<p>Problem line (I think):</p>
<pre><c... | <p>From the documentation of <code>layer_conv_2d</code> :</p>
<p><code>input_shape</code> :- Dimensionality of the input (integer) not including the samples axis. This argument is required when using this layer as the first layer in a model.</p>
<p>So the input shape <code>(28, 28, 1)</code> means 28 rows by 28 colum... | r|tensorflow|keras | 0 |
4,498 | 58,195,826 | How to delete row with timedelta value lower than 0? | <p>How can I delete rows which have values of timedelta < 0:</p>
<pre><code>Index Date/Time id Timedelta
8 2019-09-09 07:31:37.979 2555 0 days 00:40:00.033000
9 2019-09-09 07:32:38.006 2555 0 days 00:01:00.027000
10 2019-09-09 07:32:37.938 2555 -1 da... | <p>Use <a href="http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#boolean-indexing" rel="nofollow noreferrer"><code>boolean indexing</code></a> with <code>'inverse'</code> logic - filter all rows with <code>Timedelta</code> higher or equal <code>0</code>:</p>
<pre><code>df1 = df[df['Timedelta'] >... | python|pandas|timedelta | 1 |
4,499 | 58,514,251 | Making a series from another dataframe columns every nth value | <p>I have a data frame that I am trying to clean. For one of my columns I want to add every other index value (starting from 0) to a separate series. So essentially every other value down the column into its own series. I tried iterating but with no success. How can this be done? <a href="https://i.stack.imgur.com/LXDZ... | <pre><code>import pandas as pd
test_df = pd.DataFrame({'val1': ['a', 'b', 'c', 'd', 'e'], 'val2': ['v', 'w', 'x', 'y', 'z']})
alternating_srs = test_df['val1'].iloc[::2]
</code></pre> | python|pandas | 0 |
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