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 |
|---|---|---|---|---|---|---|
9,000 | 35,591,081 | FailedPreconditionError while trying to use RMSPropOptimizer on tensorflow | <p>I am trying to use the RMSPropOptimizer for minimizing loss. Here's the part of the code that is relevant:</p>
<pre><code>import tensorflow as tf
#build large convnet...
#...
opt = tf.train.RMSPropOptimizer(learning_rate=0.0025, decay=0.95)
#do stuff to get targets and loss...
#...
grads_and_vars = opt.compute_... | <p>So for people from the future running into similar trouble, I found this post helpful:
<a href="https://stackoverflow.com/questions/33788989/tensorflow-using-adam-optimizer">Tensorflow: Using Adam optimizer</a></p>
<p>Basically, I was running </p>
<pre><code>sess.run(tf.initialize_all_variables())
</code></pre>
... | tensorflow|gradient-descent | 3 |
9,001 | 35,686,774 | Python - Statistical distribution | <p>I'm quite new to python world. Also, I'm not a statistician. I'm in the need to implementing mathematical models developed by mathematicians in a computer science programming language. I've chosen python after some research. I'm comfortable with programming as such (PHP/HTML/javascript).</p>
<p>I have a column of v... | <p>Some parts of your question are unclear. It might help to give the context of what you're trying to achieve, rather than what are the specific steps you're taking. </p>
<p>1) + 3) In a Normal distribution - fitting the distribution, and estimating the mean and standard deviation - are basically the same thing. The ... | python|numpy|scipy|statistics|mixture-model | 1 |
9,002 | 35,561,949 | Pandas: how to test that top-n-dataframe really results from original dataframe | <p>I have a DataFrame, foo:</p>
<pre><code> A B C D E
0 50 46 18 65 55
1 48 56 98 71 96
2 99 48 36 79 70
3 15 24 25 67 34
4 77 67 98 22 78
</code></pre>
<p>and another Dataframe, bar, which contains the greatest 2 values of each row of foo. All other values h... | <p>Obviously you can do that with looping and efficient sorting, but maybe a better way would be:</p>
<pre><code>n = foo.shape[0]
#Test1:
#bar dataframe has original data except zeros for two values:
diff = foo - bar
test1 = ((diff==0).sum(axis=1) == 2) == n
#Test2:
#bar dataframe has 3 zeros on each line
test2 = ((... | python-3.x|pandas|top-n | 0 |
9,003 | 35,658,085 | Data analysis on .json file in Python | <p>I have to analyse a .json file which looks like this:</p>
<pre><code>{"columns":["id","timestamp","offset_freq","reprate_freq"],
"index":[0,1,2,3,4,5,6,7 ...
"data":[[526144,1451900097533,20000000.495000001,250000093.9642499983],[... }
</code></pre>
<p>it has over 600000 indexes so I dont want to show you the ... | <p>to divide 'offset_freq' by 2:</p>
<pre><code>df['offset_freq']/2
</code></pre>
<p>I suggest you read 10 minutes to pandas for a quick pandas primer</p>
<p><a href="http://pandas.pydata.org/pandas-docs/stable/10min.html" rel="nofollow">http://pandas.pydata.org/pandas-docs/stable/10min.html</a></p> | python|json|pandas | 1 |
9,004 | 35,669,105 | comparing column values based on other column values in pandas | <p>I have a dataframe:</p>
<pre><code>import pandas as pd
import numpy as np
df = pd.DataFrame([['M',2014,'Seth',5],
['M',2014,'Spencer',5],
['M',2014,'Tyce',5],
['F',2014,'Seth',25],
['F',2014,'Spencer',23]],columns =['sex','year','name','number'])
print df
</code></pre>
<p>I wo... | <p>Not sure what do you mean by 'most gender ambigious', but you can start from this</p>
<pre><code>>>> dfy = (df.year == 2014)
>>> dfF = df[(df.sex == 'F') & dfy][['name', 'number']]
>>> dfM = df[(df.sex == 'M') & dfy][['name', 'number']]
>>> pd.merge(dfF, dfM, on=['name'])... | python|numpy|pandas | 0 |
9,005 | 35,494,917 | how do I add a 'RowNumber' field to a structured numpy array? | <p>I am using genfromtxt to load large csv files into structured arrays. I need to sort the data (using multiple fields), do some work and then restore the data to the original ordering. My plan is to add another field to the data and put the row number into this field before the first sort is applied. It can then be u... | <pre><code>rowNums = np.zeros(len(csvData),dtype=[('RowID','f8')])
rowNums['RowID']=np.arange(len(csvData))
</code></pre>
<p>The above saves approx half a second per file with the csv files I am using. Very good so far.</p>
<p>However the key thing was how to efficiently obtain a record of the sort order. This is mos... | python|arrays|numpy | 0 |
9,006 | 11,513,731 | List differences between numbers | <p>I have seconds input ordered data from smallest to largest for both times.</p>
<pre><code> start_time[s] = [10, 20, 30, 40, 50, 61, 79, 80]
end_time[s] = [8, 9, 15, 31, 41, 60]
</code></pre>
<p>The lists are not the same sizes as they are generated log file timestamp data</p>
<p>I want to get output for the pos... | <p>This is how i did it</p>
<pre><code>start_time = [10, 20, 30, 40, 50, 61, 79, 80]
end_time = [8, 9, 15, 31, 41, 60]
time_difference = [(min(start_time) - et) for et in end_time if min(start_time) > et]
[2,1]
</code></pre>
<p>Your question maybe a bit ambiguous. I assume you strictly want positive time differenc... | python|numpy|python-2.7 | 0 |
9,007 | 51,093,886 | How to output a range of numbers from a column within a dataframe? | <p>I want to make a loop that will pull a number or range within a dataframe and stop analyzing the string after the word has been found.</p>
<p>For example:</p>
<pre><code> df['size']=['sz 10-13 of jordan 12', 'size 10 adidas',
'size 11 nike air forece 1', 'sz 6-7 jordan 6sz', ‘brand new Sz 11 jor... | <p>Why not just this?:</p>
<pre><code>df['size'] = df['size'].apply(lambda x: x.split()[1])
print(df['size'])
</code></pre>
<p>Output:</p>
<pre><code>0 10-13
1 10
2 11
3 6-7
Name: size, dtype: object
</code></pre>
<p><strong><em>Edit</em></strong>:</p>
<p>Try this:</p>
<pre><code>import re
df[... | python|pandas|dataframe | 1 |
9,008 | 50,781,772 | How to get the maximum value and the position from each row of a matrix | <p>Assume that I have a matrix like this <code>A=np.array([[1,2,3],[5,2,6],[7,1,5]])</code></p>
<p>Then, I want to select the biggest value and the position from each row.</p>
<p>The result should be Value=[3,6,7], Position=[2,2,0].</p>
<p>In Matlab, the code <code>[Value,Position]=max(A);</code> can calculate the c... | <p>Start with <code>argmax</code>, and use the result to index your into your array.</p>
<pre><code>idx = A.argmax(axis=1)
val = A[np.arange(len(A)), idx]
</code></pre>
<p></p>
<pre><code>idx
array([2, 2, 0])
val
array([3, 6, 7])
</code></pre> | python|numpy | 1 |
9,009 | 51,076,475 | How to move to audio file with matching text | <p>I have an audio file which is converted into text by google speech API. I want a new feature like while clicking on the text at the same time audio timing should move to match a place in an audio file?</p>
<p>Please refer this(<a href="http://www.ted.com/talks/reed_hastings_how_netflix_changed_entertainment_and_whe... | <p>Time offset values can be included in your speech recognition results <a href="https://cloud.google.com/speech-to-text/docs/basics#time-offsets" rel="nofollow noreferrer">[1]</a>. By setting the <code>enable_word_time_offsets</code> parameter to <code>True</code> in your request configuration, timestamps will be inc... | python|tensorflow|machine-learning|nlp|google-cloud-platform | 1 |
9,010 | 33,307,342 | Dataframe.isin() giving this error: The truth value of a DataFrame is ambiguous | <p>Can you help with this error: what am I doing wrong with the df.isin function?</p>
<pre><code>cursor = con.cursor()
cursor.execute("""SELECT distinct date FROM raw_finmis_online_activation_temp""")
existing_dates = [x[0] for x in cursor.fetchall()]
if df[df['date'].isin(existing_dates)]:
print "Yes it's in the... | <p><code>df[df['date'].isin(existing_dates)]</code> returns a dataframe. Unlike normal sequences, DataFrames inherit their truthyness from <code>numpy.ndarray</code> which is don't allow you to do a truth check on it (unless it has length 1 -- which is weird).</p>
<p>The solution depends on what you want out of that ... | python|python-3.x|pandas | 6 |
9,011 | 33,472,157 | Efficiently Find Partial String Match --> Values Starting From List of Values in 5 GB file with Python | <p>I have a 5GB file of businesses and I'm trying to extract all the businesses that whose business type codes (SNACODE) start with the SNACODE corresponding to grocery stores. For example, SNACODEs for some businesses could be 42443013, 44511003, 44419041, 44512001, 44522004 and I want all businesses whose codes star... | <p>To increase speed you could pre-build a single regexp matching the lines you need and the read the raw file lines (no csv parsing) and check them with the regexp...</p>
<pre><code>codes = [4451,4452,447,772,45299,45291,45212]
col_number = 4 # Column number of SNACODE
expr = re.compile("[^,]*," * col_num +
... | python|pandas|bigdata|string-matching|data-extraction | 2 |
9,012 | 9,071,084 | Polar contour plot in matplotlib - best (modern) way to do it? | <p><strong>Update:</strong> I've done a full write-up of the way I found to do this on my blog at <a href="http://blog.rtwilson.com/producing-polar-contour-plots-with-matplotlib/" rel="noreferrer">http://blog.rtwilson.com/producing-polar-contour-plots-with-matplotlib/</a> - you may want to check there first.</p>
<p>I'... | <p>You should just be able to use <code>ax.contour</code> or <code>ax.contourf</code> with polar plots just as you normally would... You have a few bugs in your code, though. You convert things to radians, but then use the values in degrees when you plot. Also, you're passing in <code>r, theta</code> to contour when... | python|numpy|matplotlib | 26 |
9,013 | 66,381,877 | How to append a column to a dataframe with values based on condition | <p>I have the following dataframe:</p>
<p>Country is actually the index:</p>
<pre><code> 2014 2015 PopEst
Country
China 8.230121e+12 8.797999e+12 1.367645e+09
United States 1.615662e+13 1.654857e+13 3.176154e+08
J... | <p>Use:</p>
<pre><code>df['Continent'] = df.index.map('ContinentDict')
</code></pre> | python|pandas | 1 |
9,014 | 66,356,000 | Generate a new categorical variable using count() | <p>I have a dataframe similar to this one:</p>
<pre><code>col_a col_b
A 1
B 6
B 3
C 2
C 3
D 6
E 7
F 8
E 8
</code></pre>
<p>I want to create a column c and do a cumulative count of everything larger than 1, 2, 3, 4, 5, 6, 7, and 8.</p>
<p>for exampl... | <pre><code>df['col_c'] = df['col_b'].apply(lambda x: sum(i >= x for i in df['col_b'].tolist()))
</code></pre>
<p>Output:</p>
<pre><code>| | col_a | col_b | col_c |
|---:|:--------|--------:|--------:|
| 0 | A | 1 | 9 |
| 1 | B | 6 | 5 |
| 2 | B | 3 | ... | python|pandas | 1 |
9,015 | 66,535,562 | Apply custom function to pandas DataFrame returns 'DataFrame' object is not callable | <p>Hej,</p>
<p>My first post here. I was trying to find similar problem in here but without a success. Here it goes.
I have a few separate pandas DataFrames where at least one column contains dictionary i.e.</p>
<pre><code>fiscalYear | prodID | position
2020 | 123 | {'description': 'Customer Operations', 'code... | <p>Unpacking a dict into columns is as simple as returning a series from <code>apply</code>:</p>
<pre><code>df
fiscalYear prodID position
0 2020 123 {'description': 'Customer Operations', 'code':...
1 2020 456 {'description': 'Support', 'code': '... | python|pandas|dataframe|dictionary|typeerror | 0 |
9,016 | 66,630,517 | Sort Pandas DataFrame using multi-criteria weighting | <p><strong>Overview</strong></p>
<p>For the following Pandas DataFrame I can sort the data using <code>sort_values</code> (I used "Clust Length" here) but this will not allow me to sort using a multi-criteria decision-making approach. I tried adding a list to <code>sort_values</code> with <code>by=[col1,col2... | <p>You can rank each column separately, sum ranks row-wise and order descending:</p>
<pre class="lang-py prettyprint-override"><code>df['order'] = df.rank().sum(axis=1)
df.sort_values('order', inplace=True, ascending=False)
</code></pre> | python|pandas | 1 |
9,017 | 16,405,153 | Offset a mask within a larger array | <p>I've got two numpy arrays: data, and a mask. The mask and the data are not the same size, so I imagine them like a canvas and a stamp. How can I stamp my canvas at different locations?</p>
<pre><code>import numpy as np
import matplotlib.pyplot as plt
# Make a canvas
canvas = np.zeros( 2500 ).reshape( 50, 50 )
# M... | <p>First crop out the rectangle (with the same size as stamp) from the canvas.</p>
<pre><code># Draw on the canvas
canvas[x_offset : x_offset + stamp.shape[0],
y_offset : y_offset + stamp.shape[1]][stamp] = 10
</code></pre> | numpy|matplotlib | 2 |
9,018 | 57,578,705 | highlight all rows that have nan values in panda frame | <p>I’ve been trying to highlight all rows that have nan values with the code below:</p>
<pre><code>Pd.style.applymap(lambda x: ['background-color: light green' if Pd.isnull() else ''])
</code></pre>
<p>But this seems doesn’t work. Is there a way to do it?</p> | <p>The Styler object has a <code>highlight_null</code> function:</p>
<pre><code>df.style.highlight_null('lightgreen')
</code></pre>
<p><a href="https://i.stack.imgur.com/9Rhne.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/9Rhne.png" alt="Styling with highlight_null"></a></p>
<p>If you want to ch... | python|pandas|dataframe | 1 |
9,019 | 57,475,889 | Could I use BERT to Cluster phrases with pre-trained model | <p>I found it was a failure that I had used Gensim with GoogleNews pre-trained model to cluster phrases like:</p>
<ul>
<li>knitting</li>
<li>knit loom</li>
<li>loom knitting</li>
<li>weaving loom</li>
<li>rainbow loom</li>
<li>home decoration accessories</li>
<li>loom knit/knitting loom</li>
<li>...</li>
</ul>
<p>I a... | <p>You can feed a phrase into the pretrained BERT model and get an embedding, i.e. a fixed-dimension vector. So BERT can embed your phrases in a space. Then you can use a clustering algorithm (such as k-means) to cluster the phrases. The phrases do not need to occur in the training corpus of BERT, as long as the words ... | tensorflow|nlp|pytorch|gensim|word2vec | 0 |
9,020 | 57,646,377 | assigning data to existing column in a for loop | <p>I want to assign data to a column of the data frame using for loop and a function but I got the common warning: </p>
<blockquote>
<p>"SettingWithCopyWarning: A value is trying to be set on a copy of a
slice from a DataFrame"</p>
</blockquote>
<p>I have a data frame and three columns of date(year, month and da... | <p>Instead of a <code>for</code> loop, use the pandas <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.apply.html" rel="nofollow noreferrer">apply</a> method, which is much faster.</p>
<p>If I understand correctly your code, you want something like this:</p>
<pre><code>df['new_col'... | python|pandas|dataframe | 2 |
9,021 | 57,661,987 | Minimum difference of Numpy arrays | <p>I have two 3-dimensional Numpy arrays of the same size. Their entries are similar, but not quite the same. I would like to shift one array in all three space dimensions, so that the difference between both arrays is minimal. </p>
<p>I tried to write a function with arguments
- list of lengths I like to shift the ar... | <blockquote>
<p>This gives an error message regarding roll = np.roll(...) It says
"slice indices must be integers or have an index method".</p>
</blockquote>
<p>np.roll requires an integer for the <code>shift</code> parameter. np.zeros creates an array of floats. Specify an integer type for <code>x0</code>:</p>
<... | python|numpy-ndarray|scipy-optimize | 0 |
9,022 | 43,616,882 | pandas 3D plot for multiple dataframes | <p>My goal is to plot something similar to the top graph of the following <a href="http://matplotlib.org/examples/mplot3d/wire3d_zero_stride.html" rel="nofollow noreferrer">link</a>.</p>
<p>I have several txt files, every one of them corresponding to a different sample.
Currently, I have my data loaded as pandas dataf... | <p>Here's one approach, using <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.melt.html" rel="nofollow noreferrer"><code>melt</code></a> and <a href="http://matplotlib.org/mpl_toolkits/mplot3d/api.html?highlight=axes3d#module-mpl_toolkits.mplot3d.axes3d" rel="nofollow noreferrer"><code>Axes3D</cod... | python|pandas|matplotlib|plot|mplot3d | 3 |
9,023 | 43,626,484 | How to append and set value in one command using Python? | <p>I have the following dataframe (df):</p>
<pre><code> SERV_OR_IOR_ID IMP_START_TIME IMP_CLR_TIME IMP_START_TIME_BIN IMP_CLR_TIME_BIN
0 -1447310116 23:59:00 00:11:00 47 0
1 1673545041 00:00:00 00:01:00 0 0
2 -743717696 ... | <p>You can use this solution, only necessary index values has to be unique:</p>
<pre><code>#first filter only values for repeating
l = df['IMP_CLR_TIME_BIN'] - df['IMP_START_TIME_BIN']
l = l[l > 0]
print (l)
3 10
dtype: int64
#repeat rows by repeating index values
df1 = df.loc[np.repeat(l.index.values,l.values... | python|pandas|dataframe|append|setvalue | 1 |
9,024 | 43,647,483 | Pandas plotting two columns with series defined by value in third column | <p>Hi I have a pandas dataframe that looks like</p>
<pre><code>deflector wFlow aContent DO Difference
64 3 127.5 10 2.007395
65 3 127.5 3 1.163951
66 3 127.5 5 1.451337
67 3 127.5 7 1.535639
68 3 24.0 10 ... | <p>You can <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.pivot.html#pandas-dataframe-pivot" rel="noreferrer"><code>pivot</code></a> the data and use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html#pandas-dataframe-plot" rel="noreferrer"><code>p... | python|pandas|matplotlib|plot | 6 |
9,025 | 43,859,710 | Filter out extra headers in middle of table | <p>I am attempting to import a very large data file. It is a text file structured like</p>
<pre><code>***** Information about Data ***********
Information about data
Information about Data
Information about Data
Information about Data
Col1 Col2
1.0 1.0
1.0 1.0
1.0 1.0
1.0 ... | <p>Assuming your file is named <code>test.txt</code></p>
<ul>
<li>read in entire file as a string</li>
<li><p><code>split</code> on <code>'\n*'</code></p>
<pre><code> new line
\
1.0 1.0
***** Information about Data ***********
\
followed by astricks
</code></pre></li>
<li><p><code>rsplit</... | python-2.7|pandas|numpy | 0 |
9,026 | 72,998,005 | How to create lists based on index or the A column in a dataframe? | <p>I have this dataframe :</p>
<pre><code> A B C
1 14 100
1 15 101
1 16 102
2 17 103
2 18 104
3 19 105
3 20 106
... ... ...
n
</code></pre>
<p>and I would like this output for any number up to n for the whole dataframe :</p>
<pre><code>l1 = [14... | <p>Try:</p>
<pre><code>l1, l2, l3 = df.groupby('A')['B'].apply(list).values
</code></pre>
<p>Unpacking will only work only if there are three unique values in df['A'], so you probably wouldn't want to unpack the values and just index the resulting DataFrame from:</p>
<pre><code>>>> df.groupby('A')['B'].apply(l... | python|pandas|list|dataframe | 5 |
9,027 | 72,902,211 | fill new Columns with Open price at certain times | <p>Hoping someone can help me here... I'm absolutely lost!</p>
<p>Here is my dataframe: a 1 minute export of the DAX fom Metatrader5. I am wanting to add 2 new columns. "Hourly Open" and "Market Open".</p>
<pre><code> Open High Low Close Vol
Date_Tim... | <p>I can only do it on ideal data. Namely, on the opening date (9:30 for me, you apparently have it at 8:00, this will need to be corrected) there should be a candle, if it is not there, then there will be empty values. The same applies to the hour, if there is no candle, for example, exactly at 13:00, then there will ... | python|pandas|dataframe | 0 |
9,028 | 73,128,929 | Slice pandas dataframe column by start and end values | <p>For example, I have a dataframe that looks like this:</p>
<pre><code>0 -- end
1 QQQQ
2 GEO
3 DEF
4 ABC
5 -- start
6 -- end
7 apple
8. -- start
</code></pre>
<p>Is it possible to dynamically slice the column by the '-- end' & '-- start'. Meaning, I want to work... | <p>You could try as follows:</p>
<pre><code>data = {'column': {0: '-- end',
1: 'QQQQ',
2: 'GEO',
3: 'DEF',
4: 'ABC',
5: '-- start',
6: '-- end',
7: 'apple',
8: '-- start'}}
df = pd.DataFrame(data)
exclude_lst = ['-- start','-- end']
# get False for members of exclude_lst, True for the rest
bools = ~d... | python|pandas|dataframe|group-by | 0 |
9,029 | 70,653,975 | Python - Iterate through multiple dataframes and append data to a new dataframe | <p>I have 3 pandas dataframes. I would like to append one row from each in each iteration to an existing dataframe.</p>
<p>Example shown below:</p>
<pre><code>DF1 =
col1 col2 col3
a a a
d d d
g g g
</code></pre>
<pre><code>DF2=
col1 col2 col3
b b b
e e e
h h h
</code></pre>... | <p>Concatenate them, using the <code>keys</code> argument to associate an index with rows from each original dataframe, then swap the index levels and sort the dataframe by this index.</p>
<pre><code>df1 = pd.DataFrame([["a", "a", "a"], ["d", "d", "d"], ["... | python|pandas|for-loop | 1 |
9,030 | 70,526,857 | Concat multiple CSV's with the same column name | <p>Im having trouble with concatting these pandas dataframes as I keep getting a error saying <code>pandas.errors.InvalidIndexError: Reindexing only valid with uniquely valued Index objects</code> I am also trying to make my code less clunky and run smoother. I was also wondering if there was a way to get multiple page... | <h3>What happens?</h3>
<p>As mentioned @Zach Young <code>data</code> is already holding all the rows you like to convert into <strong>one</strong> dataframe. So it is not an issue of <code>pandas</code> it is more an issue on how collecting the information.</p>
<h3>How to fix?</h3>
<p>An approach based on the code in y... | python-3.x|pandas|dataframe|concatenation|export-to-csv | 2 |
9,031 | 70,612,009 | Group Multiple columns while performing multiple aggregations in pandas | <p>I would like to group by multiple columns and perform several different aggregations. Grouping by type and date and taking average of en, en2, stat1 and stat2.</p>
<p><strong>Data</strong></p>
<pre><code>type en en2 date stat1 stat2
aa 40 80 1/1/2021 1 1
aa 20 20 1/1/2021 2 ... | <pre><code>grouped = final[['date', 'type', 'en',
'en2','stat1','stat2']].groupby(['date', 'type'],
as_index=False, dropna=False).sum()
</code></pre> | python|pandas|numpy | 1 |
9,032 | 43,006,542 | How does a numpy function handle a logical if operator for the axis argument? | <p>I stumbled onto this on accident, but can't make sense of what is going on. I am doing a K-means clustering assignment with images and trying to vectorize the code to make it run as fast as possible. I came up with the following code:</p>
<pre><code>image_values =np.array( [[[ 0.36302522 0.51708686 0.20952381]
... | <p>I am not entirely sure I fully understood what you're trying to do. Here's what I assume; You have one single image with RGB values and you would like to cluster the pixels within this image. Each centroid will thus define one value for each color channel respectively. I assume that each row in your <code>means</cod... | python|arrays|numpy | 1 |
9,033 | 26,954,327 | One liner for matrix-wise operations in python numpy (coming from MATLAB environment) | <p>Each column of a matrix should sum to 1. In MATLAB I would write for a matrix <code>mat</code> </p>
<pre><code>> mat = rand(5)
mat =
0.2017 0.3976 0.0318 0.2750 0.2225
0.0242 0.1222 0.1369 0.2883 0.3395
0.0390 0.4260 0.2395 0.1462 0.2816
0.0351 0.1851 0.... | <p>This operation can be written very concisely in numpy:</p>
<pre><code>import numpy as np
mat = np.random.rand(5, 5)
mat /= mat.sum(0)
mat.sum(0) # will be array([ 1., 1., 1., 1., 1.])
</code></pre> | python|matlab|python-2.7|numpy|matrix | 2 |
9,034 | 25,332,065 | numpy meshgrid of dynamic shape | <p>I am trying to use numpy meshgrid to generate some arrays. So, I have a nd array. Let us call it data and it can have an arbitrary shape and I am trying to generate some indices array as follows:</p>
<pre><code>shape = data.shape
x = np.meshgrid[1,x-1 for x in shape]
</code></pre>
<p>I know the syntax looks crazy... | <p>Going off your last example, you can do something like this:</p>
<pre><code>x = np.meshgrid(*[np.arange(1, x) for x in shape])
</code></pre>
<p>You need to explicitly create a list of the values you want to pass to <code>meshgrid</code>. If you want each one to start at 1, you need to put the 1 in each call to <c... | python|numpy | 9 |
9,035 | 39,414,370 | Is there a way to prevent pandas to_json from adding \? | <p>I am trying to send a pandas dataframe to_json and I am having some issues with the date. I am getting an addtional \ so that my records look like <code>Updated:09\/06\/2016 03:09:44</code>. Is it possible to not have this additional \ added? I am assuming that it is an escape character of some sort but I haven't... | <p>The JSON output you obtained is indeed correct and is the right behavior.</p>
<p>Allowing <code>\/</code> helps when embedding JSON in a <code><script></code> tag, which doesn't allow <code></</code> inside strings. Hence, in JSON <code>/</code> and <code>\/</code> are equivalent.</p>
<p>One workaround wo... | python|json|pandas|to-json | 5 |
9,036 | 39,262,088 | How to fetch a batch of data using tf.train.batch() according to some conditions? | <p>I have a function 'read_and_decode_Train' which read and decode one single image and label from a TFRecords dataset. Then I use the tf.train.batch() function to serialized a BATCH_SIZE of images and labels to the images_batch and labels_batch. The code is as bellows:</p>
<pre><code>image, label = read_and_decode_Tr... | <p>It might be easier for you to instead split your data into 3 separate <code>TFRecords</code> files; one for training, one for validation, and one for testing.</p> | tensorflow | 0 |
9,037 | 39,200,444 | deduplicate records in multiple CSV files with varying columns | <p>I have multiple CSV files in a directory. Some contain more columns (which would be OK to drop).</p>
<p>Is there an elegant way to deduplicate records between these CSV files and reduce columns to a common set of columns?</p>
<p>Currently, I will use python / pandas to accomplish this. I will load all the files in... | <p>I do not known how to make it simpler. I have an equal script done for some data of mine. It just runs twice, first to determine the min / max cols in all documents and finally to rewrite the csv files in an new folder, to keep the original data.</p>
<p>I am just using the csv lib from python.
<a href="https://docs... | python|csv|pandas|duplicates | 1 |
9,038 | 39,147,492 | Annotate seaborn Factorplot | <p>I would like to visualize 2 boolean informations stored as columns in one seaborn FactorPlot.</p>
<p>Here is my df :</p>
<p><a href="https://i.stack.imgur.com/KTG5j.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/KTG5j.png" alt="enter image description here"></a></p>
<p>I would like to visualiz... | <p>You could make use of <a href="http://matplotlib.org/users/annotations_intro.html" rel="noreferrer"><code>plt.annotate</code></a> method provided by <code>matplotlib</code> to make annotations for the <code>factorplot</code> as shown:</p>
<p><strong>Setup:</strong> </p>
<pre><code>df = pd.DataFrame({'groups':['A',... | python|pandas|matplotlib|seaborn | 9 |
9,039 | 39,220,914 | tensorflow.train.string_producer return nothing | <p>I'm tring to train my own images data using cifar-10 cnn model,the debug information is below .
<a href="https://i.stack.imgur.com/OcuAE.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/OcuAE.png" alt="tensorflow-string-producer"></a></p>
<p>the debug location is :</p>
<p><div class="snippet" dat... | <p>When debugging the source code of cifar-10,i found there is nothing in its queue either. It seems that the content within queue was filled later</p> | string|input|tensorflow|producer | 0 |
9,040 | 29,102,173 | Array indexing inconsistent between script and console Python 2.7? | <p>I am setting up a 3D box, 2x2x3 in this example. I have used a list comprehension to set up the coordinates of the centroids of each 1x1x1 cell in the box (I am using Python 2.7). I then convert that list structure into a numpy array and want to randomly half fill the box.</p>
<pre><code>import numpy as np
tube = ... | <p><code>centroid[zCoord][xyCoord][3]</code> is not a good way to index an array. Sometimes it works, but it isn't the intended mechanism.</p>
<p>One set of brackets gives more control:</p>
<pre><code> if centroid[zCoord, xyCoord, 3] == 0:
centroid[zCoord, xyCoord, 3] = 1
exitFlag=True
</code></pr... | python|arrays|list|python-2.7|numpy | 2 |
9,041 | 29,243,323 | How do I write a python pandas.DataFrame to a file using aligned space chacarcters? | <p>I want to store a pandas.DataFrame to a text file that has the columns aligned using whitespace characters. If this is my sample DataFrame:</p>
<pre><code>In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: df = pd.DataFrame(np.linspace(0,1,9).reshape(3,3))
In [4]: df
Out[4]:
0 1 2
0 ... | <p>How about this</p>
<pre><code>import numpy as np
import pandas as pd
import csv
df = pd.DataFrame(np.linspace(0,1,9).reshape(3,3))
df.to_csv('test.txt',
float_format='%10.3f', sep=" ",
quoting=csv.QUOTE_NONE, escapechar=" ")
</code></pre>
<p>It produces:</p>
<pre><code> 0 1 2
0 0.... | python|pandas | 4 |
9,042 | 33,959,124 | Plot pandas dataframe with subplots (subplots=True): Place legend and use tight layout | <p>I really like pandas to handle and analyze big datasets. So far, I have mostly used matplotlib for plotting but now want to use pandas own plot functionalities (based on matplotlib) since it needs less code and seems to be sufficient for me in most cases. Especially the subplots to have a guick glance at big datafr... | <ol>
<li><p>You can have all the legends in the same place, but you would have to create them in a separate step.</p>
<pre><code># Plotting
df.plot(kind='line', subplots=True, grid=True, title="Sample Data (Unit)",
layout=(4, 3), sharex=True, sharey=False, legend=False,
style=['r', 'r', 'r', 'g', ... | python|pandas|matplotlib | 24 |
9,043 | 22,519,885 | Unexpected behaviour of np.sum which acts as np.ravel | <p>I have a list of list of numbers and I want to sum all the numbers (regardless the list of lists). This should be a piece of cake for np.sum
In fact if we have</p>
<pre><code>a=[[1,2],[3,4]]
np.sum(a)
</code></pre>
<p>returns 10</p>
<p>By the way if we have</p>
<pre><code>a=[[1,2],[3,4,5]]
np.sum(a)
</code></pre... | <p>So I would hazard to guess that the answer here is pretty simple.</p>
<p><code>np.sum</code> will evaluate the two lists and realise that it can't store their values in a normal array. It will therefore make an object array:</p>
<pre><code>In [99]: x = [[1,2],[3,4,5]]
In [100]: np.array(x)
Out[100]: array([[1, 2]... | python|numpy|sum | 4 |
9,044 | 22,565,463 | How do I 'force' python to use a specific version of a module? | <p>I'm new to python so I apologize if this has been answered elsewhere with tags I haven't thought of.</p>
<p>I'm trying to update numpy from the 1.6 version I have now to 1.8. I've installed numpy in my python site-packages when I call numpy it calls the old 1.6 version. I've tried looking for the root to numpy 1.6 ... | <p>You can also insert the directory to the beginning of the path, so you won't need to remove the old one:</p>
<pre><code>sys.path.insert(1, 'C:/Python27/Lib/site-packages')
</code></pre>
<p>That won't work if you've already import your module. You can either import it after the sys.path.insert command, or use impor... | python|python-2.7|numpy|module|updating | 4 |
9,045 | 13,735,096 | python vs octave random generator | <p>More specifically, numpy:</p>
<pre><code>In [24]: a=np.random.RandomState(4)
In [25]: a.rand()
Out[25]: 0.9670298390136767
In [26]: a.get_state()
Out[26]:
('MT19937',
array([1248735455, ..., 1532921051], dtype=uint32),
2,0,0.0)
</code></pre>
<p>octave:</p>
<pre><code>octave:17> rand('state',4)
octave:18>... | <p>Unfortunately the MT19937 generator in Octave does not allow you to initialise it using a single 32-bit integer as <code>np.random.RandomState(4)</code> does. If you use <code>rand("seed",4)</code> this actually switches to an earlier version of the PRNG used previously in Octave, which PRNG is not MT19937 at all, b... | python|random|numpy|octave | 10 |
9,046 | 62,201,219 | Unable to slice year from date column using negative indexing with pandas | <p>I have a simple data set, where we have a Dates column from which I want to extract the year.
I am using the negative index to get the year </p>
<p>d0['Year'] = d0['Dates'].apply(lambda x: x[-1:-5]) </p>
<p>This normally works, however, not on this. A blank column is created.
I sampled the column for some of the... | <p>To extract year from "Dates" column , as <strong>object</strong> type use</p>
<pre><code>da['Year'] = da['Dates'].apply(lambda x: x[-4:])
</code></pre>
<p>If you want to use it as <strong>int</strong> then , you could do following operations after doing the step above</p>
<pre><code>da['Year']=pd.to_numeric(da['... | pandas|datetime|slice | 0 |
9,047 | 62,292,872 | AttributeError: 'str' object has no attribute 'weekday' | <p>I tried running linear regressions in Jupyter and it is throwing me a strange "AttributeError: 'str' object has no attribute 'weekday'" error. Any ideas?</p>
<pre><code>for df in [lr_train, lr_test]:
df['day_of_week'] = df.index.weekday
df['is_weekend'] = df.index.map(lambda x: 1 if x.weekday() > 4 else ... | <p>This means that the object you're attempting to get the <code>.weekday</code> attribute from does not have that attribute. Given the code, it appears that <code>df['day_of_week']</code> is a string and not a <code>datetime.datetime()</code> object.</p>
<p>If you want to see why this is happening, try the following ... | python|pandas|linear-regression | -1 |
9,048 | 62,379,297 | Pandas: Most efficient way of change a value in one of the column based on 2 other columns | <p>What will be the best way to perform operations on a column in Pandas DF based on 2 other columns. One of the columns has the value, while other column has the name of column to fill in data.</p>
<pre><code>value B C1 C2 C3 C4 C5
1 C2 0 0 0 0 0
5 C3 0 0 0 0 0
3 C5 0 ... | <p>We do <code>pivot</code> then <code>update</code> </p>
<pre><code>df.update(df.pivot(columns='B',values='value'))
df
value B C1 C2 C3 C4 C5
0 1 C2 0 1.0 0.0 0 0.0
1 5 C3 0 0.0 5.0 0 0.0
2 3 C5 0 0.0 0.0 0 3.0
</code></pre> | python|pandas | 7 |
9,049 | 62,429,594 | Pandas: If column value is empty then insert value from another column in the same row | <p>I have the following scenario where I need to fill my empty column value with another column value.</p>
<p>my.csv</p>
<pre><code>country newCountry
France Argentina
Uruguay
Germany Ireland
</code></pre>
<p>desired output:</p>
<pre><code>country newCountry
France Argentina
Uruguay Uru... | <p>If possible mutiple spaces use <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.strip.html" rel="nofollow noreferrer"><code>Series.str.strip</code></a>:</p>
<pre><code>df.loc[df['newCountry'].str.strip() == '', 'newCountry'] = df['country']
</code></pre>
<p>Or if there are missi... | python|pandas|csv | 2 |
9,050 | 62,213,117 | Pandas CSV delimiter, special character and insert inside a row question | <p>sorry for asking 2 times but my teacher said i can use pandas now.
Finish output</p>
<p><a href="https://github.com/emanuelOchoa/csv" rel="nofollow noreferrer">files</a></p>
<p><a href="https://i.stack.imgur.com/ID7cK.png" rel="nofollow noreferrer">What they want(pic of the solution)</a></p>
<p><a href="https://i... | <p>At least one of the files you uploaded to github as an encoding issue, but nevertheless - here's a way to read it: </p>
<pre><code>url = "https://raw.githubusercontent.com/emanuelOchoa/csv/master/source.csv"
res = requests.get(url)
content = res.content.decode('utf-8','ignore')
pd.read_csv(StringIO(content), sep ... | python|pandas|csv | 0 |
9,051 | 51,321,354 | Find the maximum of count in a grouped dataframe | <p>I have a dataframe that consists of football data with columns such as player name, club, nationality and rating. I have applied the <code>groupby</code> function to group the data by club and nationality and have calculated the count, min, max and mean.
<a href="https://i.stack.imgur.com/frp31.png" rel="nofollow n... | <p>I think need <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html" rel="nofollow noreferrer"><code>groupby</code></a> by first level of <code>MultiIndex</code> with <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.core.groupby.DataFrameGroupBy.idxmax.html"... | python|pandas|data-science | 1 |
9,052 | 48,428,193 | shifting pandas series for only some entries | <p>I've got a dataframe that has a <code>Time</code> Series (made up of strings) with some missing information:</p>
<pre><code># Generate a toy dataframe:
import pandas as pd
data = {'Time': ['0'+str(i)+':15:45' for i in range(10)]}
data['Time'][4] = 'unknown'
data['Time'][8] = 'unknown'
df = pd.DataFrame(data)
# df... | <p>If you're intent on working with a time series data. I would recommend converting it to a time series, and then forward filling the blanks</p>
<pre><code>import pandas as pd
data = {'Time': ['0'+str(i)+':15:45' for i in range(10)]}
data['Time'][4] = 'unknown'
data['Time'][8] = 'unknown'
df.Time = pd.to_datetime(df.... | python|pandas|dataframe | 1 |
9,053 | 48,283,609 | how to use pyknackhq python library for getting whole objects/tables from my knack builder | <p>I am trying to connect <a href="https://builder.knack.com/" rel="nofollow noreferrer">knack</a> online database with my python data handling scripts in order to renew objects/tables directly into my knack app builder. I discovered <a href="https://pypi.python.org/pypi/pyknackhq" rel="nofollow noreferrer">pyknackhq</... | <p>I haven't worked with that library, but I've written another python Knack API wrapper that should help:
<a href="https://github.com/cityofaustin/knackpy" rel="nofollow noreferrer">https://github.com/cityofaustin/knackpy</a></p>
<p>The docs should get you where you want to go. Here's an example:</p>
<pre><code>>... | python|pandas | 1 |
9,054 | 48,262,754 | Numpy argsort, sorting on first number | <p>I've got the following code:</p>
<pre><code>import numpy as np
a = np.array([["value1", "value2", 3, "value4", "value5"],
["value1", "value2", -10, "value4", "value5"],
["value1", "value2", 31, "value4", "value5"],
["value1", "value2", 5, "value4", "value5"],
["v... | <p>As mentioned in the comments, the values you are sorting are strings. Change</p>
<pre><code>a = a[a[:, 2].argsort()]
</code></pre>
<p>to </p>
<pre><code>a = a[a[:, 2].astype(np.int).argsort()]
</code></pre>
<p>So that they are compared as integers. </p> | python|numpy | 4 |
9,055 | 48,866,886 | Joining tables in python with different lengths based on a key field | <p>So I want to make a join on two tables with a key field which both tables contain, so I can make a side by side comparison. </p>
<p>Table A has 1164 rows and table B has 74 rows. And the common field in Table A is called EmployeeID and the 'same' field in Table B is called UserID. </p>
<p><a href="https://i.stack.... | <p>You can use <a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.merge.html" rel="nofollow noreferrer"><code>merge()</code></a> with the <code>left_on</code> and <code>right_on</code> arguments to specify your key field, then check for NaN values to find which rows are not in a table:</p>... | python|pandas|join|inner-join | 0 |
9,056 | 48,471,794 | Pip3 installing of Tensorflow issue | <p>I'm trying to install python and tensorflow to learn as part of a class I am taking, and am having some issues installing tensorflow. I keep getting the same set of errors:</p>
<pre><code>C:\Users\X\AppData\Local\Programs\Python\Python36-32\python.exe: can't find '__main__' module in 'C:\\'
</code></pre>
<p>or</p>... | <p>your pip3 is broken, because I can clearly see tensorflow distribution on PyPi for python 3.6 <a href="https://pypi.python.org/pypi/tensorflow/1.5.0" rel="nofollow noreferrer">https://pypi.python.org/pypi/tensorflow/1.5.0</a>. Also, I just created a new virtual env for python 3.6 and I am able to <code>pip install</... | python|tensorflow | 0 |
9,057 | 71,083,776 | Finding the difference in value counts by keys in two Dictionaries | <p>I have two sample python dictionaries that counts how many times each key appears in a DataFrame.</p>
<pre><code>dict1 = {
2000 : 2,
3000 : 3,
4000 : 4,
5000 : 6,
6000 : 8
}
</code></pre>
<pre><code>dict2 = {
4000 : 4,
3000 : 3,... | <p>The way you're doing doesn't work because the dicts are not sorted, so <code>k==k2</code> is always evaluated False.</p>
<p>You could use a dict comprehension where you traverse <code>dict1</code> and subtract the value in <code>dict2</code> with the matching key:</p>
<pre><code>diff = {k: abs(v - dict2[k]) for k, v... | python|python-3.x|pandas|dataframe|dictionary | 3 |
9,058 | 70,755,998 | DataFrame .isin for integers | <p>I've created a function - set of conditions, which returns 1 / 0, if the condition is fulfilled or not.</p>
<pre><code>avg_ActivityScore = company['ActivityScore'].median()
min_EmployeeLowerBound = 10
list_LegalFormIDs = [112, 121, 301, 118, 141, 703, 111, 705, 921, 117, 361, 391, 711]
min_CompaniesCount = 10
def f... | <p>Working with scalars in <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.apply.html" rel="nofollow noreferrer"><code>DataFrame.apply</code></a>, so cannot use functions for <code>Series</code>, because <code>df['LegalFormID']</code> is scalar inside function:</p>
<pre><code>def fla... | python|pandas|dataframe | 0 |
9,059 | 70,746,158 | How to assign graph label for graph in pytorch geometric? | <p><strong>Question:</strong> How can we assign a graph-level label to a graph made in PyTorch geometric?</p>
<p><strong>Example</strong>: Let us say we create an undirected graph in PyTorch geometric and now we want to label that graph according to its class (can use a numerical value). How could we now assign a class... | <p>The <code>pyg_graph</code> object has type <code>torch_geometric.data.Data</code>.
Inspecting the <a href="https://pytorch-geometric.readthedocs.io/en/latest/_modules/torch_geometric/data/data.html#Data" rel="nofollow noreferrer">source code of <code>Data</code> class</a>, you can see that it defines the dunder meth... | python|pytorch | 0 |
9,060 | 51,740,976 | Data not appearing on a python plot | <p>I have a dataframe with date as index, floats as columns, filled with mostly NaN and a few floats.</p>
<p>I am plotting this dataframe using :</p>
<pre><code>fig, ax = plt.subplots()
plot(df2[11][2:], linestyle='dashed',linewidth=2,label='xx')
ax.set(xlabel='xx', ylabel='xx', title='xx')
ax.grid()
ax.legend()
</co... | <p>Try</p>
<pre><code>import matplotlib.pyplot as plt
fig = plt.figure()
plt.plot(df2[11][2:], linestyle='dashed',linewidth=2,label='xx')
plt.set(xlabel='xx', ylabel='xx', title='xx')
plt.grid()
plt.legend()
plt.show()
</code></pre> | python|pandas|matplotlib | 1 |
9,061 | 51,699,523 | Error: "CUDNN STATUS NOT INITIALIZED" in keras-based convolutional network | <p>I'm trying to create a convolutional network using keras. However, I'm getting the following error:</p>
<blockquote>
<p>2018-08-05 21:10:44.670676: E
T:\src\github\tensorflow\tensorflow\stream_executor\cuda\cuda_dnn.cc:332]
could not create cudnn handle: CUDNN_STATUS_NOT_INITIALIZED 2018-08-05
21:10:44.6708... | <p>You need to include cudnn in your environment variables(if on windows), if you need to run tensorflow-gpu.</p> | python|tensorflow|machine-learning|keras|cudnn | 1 |
9,062 | 51,865,465 | Error in importing NiftyNet with Tensorflow 1.9 | <p>I installed the package <a href="http://niftynet.io/" rel="nofollow noreferrer"><code>NiftyNet 0.3.0</code></a> with <code>Python 2.7.5</code> on <a href="https://www.centos.org/" rel="nofollow noreferrer"><code>CentOS</code></a> Linux 7.5. <code>Tensorflow 1.9</code> was installed a priori. When I import <code>Nift... | <p>The latest dev branch supports TF 1.9, you can follow these steps to install: <a href="https://github.com/NifTK/NiftyNet/wiki/NiftyNet-FAQ" rel="nofollow noreferrer">https://github.com/NifTK/NiftyNet/wiki/NiftyNet-FAQ</a></p> | python-2.7|tensorflow|niftynet | 0 |
9,063 | 51,806,361 | How to sort a csv file without headers using python? | <p>How can I sort a csv file which is without header using python pandas?
NOTE: The csv file is without headers.</p>
<p>My File:</p>
<pre><code>1,a123,adam,student
2,b345,becky,student
3,c678,charles,teacher
1,d987,dickson,teacher
2,e654,evanston,teacher
</code></pre>
<p>Expected output:</p>
<pre><code>1,a123,adam,... | <p>Use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html" rel="nofollow noreferrer"><code>read_csv</code></a> with parameter <code>names</code> for new columns names of <code>Dataframe</code> and then <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sort_... | python|python-2.7|pandas|pandas-groupby | 2 |
9,064 | 64,205,923 | Use another df to replace column values | <hr />
<p>Hello I have two df such as</p>
<p><strong>df1</strong></p>
<pre><code>Ancient New
Seq1.1 Seq1.1_A
Seq2 Se2.4_3
</code></pre>
<p>and another</p>
<p><strong>df2</strong></p>
<pre><code>COL1 COL2
A Seq1.1
B Plants
C YP_OODDD
D Seq2
</code></pre>
<p>and I would like to replace <code>COL2</code> ... | <p>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>Series</code> created by <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.set_index.html" rel="nofollow noreferrer... | python|pandas|replace | 2 |
9,065 | 64,202,899 | Question about adding a layer after loading pre-trained weights | <p>I have a question about creating a deep neural network with partially loading weights.</p>
<p>Suppose I construct a model as follows (assume a sequence of layers are specified for the model):</p>
<pre><code>model = models.Model(inputs, x, name=model_name)
</code></pre>
<p>And then, I load the weights for the model.<... | <p>Say you have a model loaded with pretrained weights</p>
<pre><code>model.load_weights(weights)
#Set trainable to false to maintain the previous weights
for layer in model.layers:
layer.trainable = False
</code></pre>
<p>Down below you will see some examples, but make sure you consult the <a href="https://www.ten... | tensorflow|keras|deep-learning|tensorflow2.0 | 0 |
9,066 | 47,924,400 | Python Pandas: Assign Last Value of DataFrame Group to All Entries of That Group | <p>In Python Pandas, I have a DataFrame. I group this DataFrame by a column and want to assign the last value of a column to all rows of another column.</p>
<p>I know that I am able to select the last row of the group by this command:</p>
<pre><code>import pandas as pd
df = pd.DataFrame({'a': (1,1,2,3,3), 'b':(20,21... | <p>Use <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.transform.html" rel="noreferrer"><code>transform</code></a> with <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.core.groupby.GroupBy.last.html" rel="noreferrer"><code>last</code></a>:</... | python|pandas|dataframe|group-by|pandas-groupby | 21 |
9,067 | 49,063,601 | pandas colume split by code and concat these data | <p>I'm pandas newbie</p>
<p>for example I have dataframe as below</p>
<pre><code>code time open high low close
1 2 1 1 1 1
2 1 1 1 1 1
2 2 1 1 1 1
</code></pre>
<p>and </p>
<ol>
<li>I want column split by code</li>
<li>I want concat these splited data on index by time... | <p>Use:</p>
<ul>
<li>first reshape by <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.set_index.html" rel="nofollow noreferrer"><code>set_index</code></a> + <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.unstack.html" rel="nofollow noreferrer"><code>unst... | pandas|financial | 1 |
9,068 | 49,286,381 | What is the the best way to modify (e.g., perform math functions) a column in a Dask DataFrame? | <p>I'm a veteran of Pandas DataFrame objects, but I'm struggling to find a clean, convenient method for altering the values in a Dask DataFrame column. For a specific example, I'm trying to multiply positive values in a numpy.float column by -1, thereby making them negative. Here is my current method (I'm trying to c... | <h3>first question</h3>
<p>If something works for string columns and not for numeric-named columns then that is probably a bug. I recommend raising an issue at <a href="https://github.com/dask/dask/issues/new" rel="nofollow noreferrer">https://github.com/dask/dask/issues/new</a></p>
<h3>second question</h3>
<blockq... | python|pandas|dataframe|dask | 0 |
9,069 | 49,067,073 | how to append two or more dataframes in pandas and do some analysis | <p>I have 3 df's:</p>
<pre><code>df1=pd.DataFrame({"Name":["one","two","three"],"value":[4,5,6]})
df2=pd.DataFrame({"Name":["four","one","three"],"value":[8,6,2]})
df3=pd.DataFrame({"Name":["one","four","six"],"value":[1,1,1]})
</code></pre>
<p>I can append one by one but I want to append all the three data frames at... | <p>Use:</p>
<ul>
<li>first <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.concat.html" rel="nofollow noreferrer"><code>concat</code></a></li>
<li>aggregate by <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.core.groupby.DataFrameGroupBy.agg.html" rel="nofollow noreferrer"><... | python|pandas|dataframe|data-analysis | 1 |
9,070 | 49,114,732 | Python numpy floating point array precision | <p>I am trying to solve for SVM optimisation problem using Pegasos mini-batch algorithm (as in Fig 2) from this link: <a href="http://www.cs.huji.ac.il/~shais/papers/ShalevSiSrCo10.pdf" rel="nofollow noreferrer">http://www.cs.huji.ac.il/~shais/papers/ShalevSiSrCo10.pdf</a></p>
<pre><code>#X: m*n matrix with m example... | <p>If you need precision, you should use <code>np.float64</code> (the normal floating point precision <code>double</code>).</p>
<p>If you are using Python 2, you are using integer divition in <code>(1/t)</code>, <code>(1/k)</code>, and <code>(1/l)</code>. Write it as <code>1.0/</code>, to force a floating point divisi... | python|numpy|svm|precision | 0 |
9,071 | 58,617,465 | How to choose which nan in column header | <p>I will like to choose the first nan to be named School while the other nan to be named grade. </p>
<p>Original Column Header: </p>
<pre><code>Name Address nan Class Subject nan
</code></pre>
<p>This is the outcome I want: </p>
<pre><code>Name Address School Class Subject Grade
</code></pre>
<hr>
<p>When i tri... | <pre><code>df.columns = ["Name", "Address", "School", "Class", "Subject", "Grade"]
</code></pre> | python|pandas|dataframe | 1 |
9,072 | 58,811,041 | sklearn normalize() produces every value as 1 | <p>I'm trying to normalize a single feature to [0, 1], but the result I'm getting back is all float values of 1 and is clearly wrong. </p>
<pre><code>import pandas as pd
import numpy as np
from sklearn.preprocessing import normalize
test = pd.DataFrame(data=[7, 6, 5, 2, 9, 9, 7, 8, 6, 5], columns=['data'])
normalize... | <p>This is because the default <code>axis</code> is 1.</p>
<p>Set <code>axis = 0</code>:</p>
<pre><code>normalize(test['data'].values.reshape(-1, 1), axis=0)
</code></pre>
<p>Output:</p>
<pre><code>array([[0.32998316],
[0.28284271],
[0.23570226],
[0.0942809 ],
[0.42426407],
[0.424... | python|pandas|scikit-learn|normalization | 5 |
9,073 | 58,963,513 | TypeError: Cannot convert provided value to EagerTensor. Provided value: 0.0 Requested dtype: int64 | <p>I am trying to train the transformer model available from the tensorflow official models. I am able to train in cpu without any error but when I try gpu I get the following error:</p>
<pre><code>models/official/transformer/v2/transformer.py:143 call *
encoder_outputs = self.encode(inputs, attention_bias, train... | <p>I had the same error with another Keras function. One of my parameters was a float by mistake.</p> | tensorflow|transformer-model | 0 |
9,074 | 70,138,882 | How to assign a unique id for a sequence of repeated column value in pandas dataframe? | <p>I have a dataframe call it dfA,</p>
<pre><code>ID Chronological Label
1 1 None
2 0 ONPEAPLFPH
3 0 JFECJGSQNS
4 1 None
5 1 None
6 0 MGMELTIVTJ
7 1 No... | <p>Try this:</p>
<pre><code>df['unique_id'] = (df['Chronological'].eq(0) |
(df['Chronological'] != df['Chronological'].shift())
).cumsum()
</code></pre>
<p>Output:</p>
<pre><code> ID Chronological Label unique_id
0 1 1 None 1
1 2 ... | python|pandas|dataframe | 2 |
9,075 | 70,280,939 | IndexError: index 0 is out of bounds for axis 0 with size 0? see detail in output1111 | <pre><code>#count the number of fake and real videos
def number_of_real_and_fake_videos(data_list):
header_list = ["file","label"]
lab = pd.read_csv('/content/drive/My Drive/Gobal_metadata.csv',names=header_list)
fake = 0
real = 0
for i in data_list:
temp_video = i.split('/')... | <p>These 2 lines of code are accessing a list index that may not exist</p>
<pre><code>print("TRAIN: ", "Real:",number_of_real_and_fake_videos(train_videos)[0]," Fake:",number_of_real_and_fake_videos(train_videos)[1])
print("TEST: ", "Real:",number_of_real_and_fake_video... | python|pandas|numpy|index-error | 0 |
9,076 | 70,167,433 | Calculate % change in flat tables | <p>From df1 I would like to calculate the percentage change from, which should give df2. Would you please assist me?</p>
<p>df1</p>
<pre><code>lst=[['01012021','A',10],['01012021','B',20],['01012021','A',12],['01012021','B',23]]
df1=pd.DataFrame(lst,columns=['Date','FN','AuM'])
</code></pre>
<p>df2</p>
<pre><code>lst=[... | <p>Use <code>groupby</code> and <code>pct_change</code>:</p>
<pre><code>df1['%_delta'] = df1.groupby('FN')['AuM'].pct_change()
print(df1)
# Output:
Date FN AuM %_delta
0 01012021 A 10 NaN
1 01012021 B 20 NaN
2 01012021 A 12 0.20
3 01012021 B 23 0.15
</code></pre> | pandas|flat | 2 |
9,077 | 56,343,679 | Append dataframe with column names alone to another dataframe with data | <p>I have 2 dataframes as below.</p>
<p><strong>Dataframe 1 (with only column names and no data):</strong></p>
<pre><code>Name Age Gender
</code></pre>
<p>0 rows * 3 columns</p>
<p><strong>Dataframe 2 (has data with over 1000 rows):</strong></p>
<pre><code>level_1 level_2 level_3
AAA 26 M
BB... | <p>Need same columns names for correct alignment of columns between both DataFrames, so set columns names by from another DataFrame:</p>
<pre><code>dataframe_2.columns = dataframe_1.columns
dataframe_1 = dataframe_1.append(dataframe_2,ignore_index = True)
</code></pre>
<p>Another solution:</p>
<pre><code>dataframe_1... | python|pandas|dataframe | 5 |
9,078 | 56,107,675 | Second Largest Row of Multiple Pandas Columns | <p>I have a Pandas dataframe and would like to take the minimum of multiple 6 columns by row for example in the below table I would like to put in the below 6 rows and get the row min:</p>
<pre><code>+-col1-col2-col3-col4-col5-col6-Min-+
| 1 2 3 4 5 6 2 |
| 6 5 4 3 2 2 3 |
| 7 ... | <p>If there are at least 2 unique values per rows first remove mising values by <code>dropna</code>, get unique values, sorting and select second value by indexing:</p>
<pre><code>df = a1_raw_data.iloc[:, [21, 23, 25, 27, 29, 31]]
a1_raw_data['Min'] = df.apply(lambda row: np.sort(np.unique(row.dropna()))[1], axis=1)
... | python|python-3.x|pandas | 1 |
9,079 | 56,410,093 | Using pandas to combine csv by comparing a key ("id") | <p>I have two CSVs with basically the same content, but spelling mistakes are removed from one, <code>fileA.csv</code>, and <code>fileB.csv</code> gets updated (as in new rows are added) from upstream (a limesurvey installation). How do I "combine" these two files using Pandas by checking the "id" column?</p>
<p>I hav... | <p>I'm presuming spelling mistakes being removed from <code>fileA.csv</code> means you want to keep row in <code>fileA.csv</code>, but add any rows in <code>fileB.csv</code> that do not exist in <code>fileA.csv</code>.</p>
<p>As a general rule you should read in your DataFrames so the <em>index</em> is set to your pri... | python|pandas | 1 |
9,080 | 56,010,938 | Is there a specific order one should choose when chaining conditions in querying? | <p>Imagine we have this dataframe as an example:</p>
<pre><code>df = pd.DataFrame([purchase_1, purchase_2, purchase_3], index=['Store 1', 'Store 1', 'Store 2'])
</code></pre>
<p>If I want to know the names of people who spent more than 3 (euros), what is the difference between these two approaches:</p>
<pre><code>#a... | <p>Do neither of these. It's <a href="http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy" rel="nofollow noreferrer">chained indexing</a>, and can come back to hurt you unexpectedly.</p>
<p>Instead, it's safer to provide both axis labels at once:</p>
<pre><code>df.loc[... | python|pandas | 3 |
9,081 | 55,751,180 | Why does memory usage in Pandas report the same number for integers as for object dtype? | <p>I'm trying to understand the difference in memory usage between integers and string (objects) dtypes in Pandas.</p>
<pre><code>import pandas as pd
df_int = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('ABCD'), dtype=int)
</code></pre>
<p>As expected, this takes around 3.2 KB of memory as each ... | <p>Following the <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.info.html" rel="nofollow noreferrer">docs</a>, use <code>'deep'</code> to get the actual value (otherwise it's an estimate)</p>
<pre><code>df_str.info(memory_usage='deep')
#<class 'pandas.core.frame.DataFrame'>... | python|pandas | 3 |
9,082 | 64,659,330 | Getting the frequency over columns of each item | <p>I have been trying to get the frequency of each ID per day over a period of time.
I have the following dataframe:</p>
<pre><code>data1 = pd.DataFrame({
'Date_Time': [
'2010-01-01', '2010-01-01',
'2010-04-02', '2010-04-01',
'2011-01-01', '2011-01-01',
'2013-01-01', '2014-01-01',
'2014-01-01', '2015-01-01',
'2016-... | <p>You can use <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html" rel="nofollow noreferrer"><code>df.groupby()</code></a> with <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.transform.html" rel="nofollow noreferrer"><code>transform</c... | python|pandas|dataframe | 0 |
9,083 | 64,965,027 | How to get vendors count from dataframe in pandas? | <p>I have this dataframe</p>
<pre><code>area vendors
electronic city zomato
electronic city zomato
electronic city swiggy
Anekal, Electronic City ... | <p>To get the number of unique vendors by area you can simply use</p>
<pre><code>In [10]: df.groupby(['area'])['vendors'].nunique().rename('vendorlist')
Out[10]:
area
Anekal, Electronic C... | python|pandas|dataframe | 0 |
9,084 | 64,993,857 | How to set pandas columns names from a dictionary of lists? | <p>I have a dict like:</p>
<pre><code>actions = {0: [0, 1, 2, 4],
1: [0, 1, 8, 5, 2, 4],
2: [0, 1, 2, 5, 6]}
</code></pre>
<p>And I would like to create a dataframe where the column names where:</p>
<pre><code>state_actions = pd.Dataframe()
Empty DataFrame
Columns: [[0, 1, 2, 4], [0, 1, 8, 5,... | <pre><code>pd.DataFrame(columns=actions.values())
Empty DataFrame
Columns: [[0, 1, 2, 4], [0, 1, 8, 5, 2, 4], [0, 1, 2, 5, 6]]
Index: []
</code></pre> | python|pandas | 1 |
9,085 | 64,774,306 | How to remove double quote from a csv file before reading it? | <p>I am getting the following error:</p>
<blockquote>
<p>pandas.errors.ParserError: '|' expected after '"'</p>
</blockquote>
<p>The reason is because the first line has <code>'"'</code> that shouldn't be there:</p>
<pre><code>"Name|Kind|Color|Price
</code></pre>
<p>I tried the following:</p>
<pre><code>`... | <p>I am not totally sure about your problem but given a csv file like:</p>
<pre><code>"Name|Kind|Color|Price
alex|robot|braun|100$
</code></pre>
<p>then the following code will remove any leading "#" if present:</p>
<pre class="lang-py prettyprint-override"><code>import pandas as pd
import re
pd.DataFr... | python|pandas | 0 |
9,086 | 64,843,410 | Elements from list are overwritten in python | <p>I've been struggling for some days trying to figure out why my items from a python list are overwritten. Basically I have to implement a function that rotates 8 times a matrix.</p>
<pre><code>def rotate_ring(matrix, offset):
dim = len(matrix[0])
print(dim)
last_element = matrix[offset][offset]
for j in range(1 + off... | <p>Please change the function compass filter as follows:</p>
<pre><code>def compass_filter(kernel):
results = []
results.append(kernel)
for i in range(7):
kernel_ = np.array([rotate_matrix(kernel)])
print(kernel_)
results.append(kernel_)
return results
</code></pre> | python|numpy|opencv | 1 |
9,087 | 64,940,159 | Write rows from Postgres Table into a CSV file using Pandas | <p><strong>I want to writerows to the given NAS(mnt) folder from a postgres table, but only after performing some data hygine checks</strong></p>
<p><em>Below is the actual code which is working, but it only selects columns from the table and saves the data into a .txt file</em></p>
<pre class="lang-py prettyprint-over... | <pre><code>import os
import psycopg2
import time
import pandas as pd
# File path and name.
filePath = '/mnt/nasdatavant/datavant/covid_implementation/postgres_files/'
timestr = time.strftime("%Y-%m-%d-%-H%M%S")
fileName = 'covid-' + timestr + '.csv'
# Check if the file path exists.
if os.path.exists(filePat... | python|pandas|postgresql | 1 |
9,088 | 40,286,298 | How to access all flags and get their values using loop in Tensorflow? | <p>I want to write all flags and its values in external file(like txt).
How can I get automatically all the contents inside <code>tf.flag</code>?
is there any built-in function? or is there easy way e.g. by using loop?</p>
<p>for example,</p>
<pre><code>tf.flags.DEFINE_string("device","/gpu:0", "select device")
tf.fl... | <p>For tensorflow 1.5 you can use <code>tf.app.flags.FLAGS.flag_values_dict()</code> they have changed the flags library one more time</p> | python|tensorflow | 27 |
9,089 | 44,167,418 | Expand category in a column to column name in pandas | <p>I'm trying to expand (not sure if it is the right word) some categorical
data into columns using pandas.</p>
<p>Let's say I have the following data frame:</p>
<pre><code>df = pandas.DataFrame({'name': ['john', 'john', 'louis', 'louis'],
'day':['a', 'b', 'a', 'b'], 'oranges':[10, 23, 15, 5]... | <p>You have already got the desired output, you need to format the column names</p>
<pre><code>df = df.pivot('name', columns='day')
df.columns = ['_'.join(col).strip() for col in df.columns.values]
df = df.reset_index()
name apple_a apple_b oranges_a oranges_b
0 john 5 4 10 23
1 l... | python|pandas | 3 |
9,090 | 44,338,623 | Horizontal stacked bar chart in python giving multiple charts in Jupyter Notebook | <p>I am trying to make a stacked horizontal bar chart with a specified size, title, and legend location in Jupyter Notebooks. When I use other Stack Overflow solutions I get several graphs printed out instead of just one. Here's a simplified example:</p>
<pre><code>import pandas as pd
import matplotlib.pyplot as plt
a... | <p>You can assign the return object from the <code>plot</code> to the variable <code>ax</code>. Then do what you wanted.</p>
<pre><code>a = [3,5,4,2,1]
b = [3,4,5,2,1]
c = [3,5,4,6,1]
df = pd.DataFrame({'a' : a,'b' : b, 'c' : c})
ax = df.plot.barh(stacked=True);
ax.figure.set_size_inches(6,6)
ax.set_title("My ax ti... | python|pandas|matplotlib|jupyter-notebook | 4 |
9,091 | 69,438,751 | Apply Custom Function and write to CSV every N rows | <p>I have a large dataframe and am trying to apply a custom function to one of the columns. However, as the function is a GET request to a website, it is rather slow, and the apply function breaks after an hour or so.</p>
<p>As such, my current thinking is to break the dataframe up into subsamples of N rows each, apply... | <p>You can try to open only a chunk of the dataframe at a time with the following :</p>
<pre><code>for chunk in pd.read_excel(your_file, chunksize=number_of_rows_at_a_time):
chunk
# chunk is a sub dataframe containing chunksize number of rows
</code></pre>
<p>Though, the best option is still to find a way to no... | python|pandas | 0 |
9,092 | 69,638,483 | Why do Keras Tuners' Fixed hyperparameters produce different results from static values? | <p>My hypertuning results are quite different even though my model should effectively be using the same parameters, depending on whether I use <code>hp.Fixed(key, value)</code> or just <code>value</code> (where <code>value</code> is say, an Int). I've verified that repeated runs of each test produce the same results fo... | <p>Ok so after doing some more digging I discovered (specifically through how <a href="https://reposhub.com/python/deep-learning/keras-team-keras-tuner.html#:%7E:text=hp%20%3D%20HyperParameters()%0A%23%20This%20will%20override%20the%20%60learning_rate%60%20parameter%20with%20your%0A%23%20own%20selection%20of%20choices%... | tensorflow|keras|hyperparameters | 1 |
9,093 | 41,203,959 | Conditionally format Python pandas cell | <p>I am trying to color, highlight, or change fond of Python pandas DataFrame based on the value of the cell. e.g. if the cells on each rows are bigger than the cell in the first column of that row, then highlight the cell as red (or any other color), otherwise leave it as it is. </p>
<p>I wrote a for loop here:</p>
... | <p>From <a href="http://pandas.pydata.org/pandas-docs/stable/style.html" rel="noreferrer">the style docs:</a></p>
<blockquote>
<p>You can apply conditional formatting, the visual styling of a
DataFrame depending on the data within, by using the DataFrame.style
property.</p>
</blockquote>
<pre><code>import panda... | python|pandas|dataframe|conditional-formatting|pandas-styles | 38 |
9,094 | 54,134,593 | Finding max value by Row and displaying its column name correspodingly | <p>I have the following array , which consists of 4 columns and 2 rows. I wish to find the maximum value in the second row and return the corresponding value name . In other words my output should be :521 (100 has the 99 value aswell but i need to return the first value ).</p>
<p>ive tried this : (student_ids is the f... | <p>Input: </p>
<pre><code>array : [[521 597 624 100] [ 99 73 97 99]]
</code></pre>
<p>First, find the index of <code>max</code> in second row like this,</p>
<pre><code>idx = np.argmax(arr[1])
</code></pre>
<p>Then, extract the element from first row of that index,</p>
<pre><code>print(arr[0][idx])
</code></pre>
<... | python|arrays|numpy | 0 |
9,095 | 54,093,248 | iOS Firebase ML Kit Simple Audio Recognition "Failed to create a TFLite interpreter for the given model" | <p>I have been trying to implement the <a href="https://www.tensorflow.org/tutorials/sequences/audio_recognition" rel="nofollow noreferrer">Simple Audio Recognition</a> Tensorflow sample in iOS using the <a href="https://firebase.google.com/docs/ml-kit/ios/use-custom-models?authuser=0" rel="nofollow noreferrer">Firebas... | <p>I posted this same question in the firebase quickstart iOS repository, And i got the following response <a href="https://github.com/firebase/quickstart-ios/issues/614#issuecomment-453218055" rel="nofollow noreferrer">DecodeWav op is never supported by TensorFlowLite</a>. So at present Tensorflow Lite does not suppor... | python|ios|firebase|tensorflow|firebase-mlkit | 0 |
9,096 | 53,844,459 | Drop duplicates but keep rows having largest value in a given column per group | <p>I have a DF like this:</p>
<pre><code> Name Gender Age Level
Pikachu Male 4 8
Charmander Female 5 7
Charmander Female 5 7
Squirtle Male 3 6
Squirtle Male 3 9
Squirtle ... | <p>Check with <code>sort_values</code>+<code>drop_duplicates</code></p>
<pre><code>df=df.sort_values('Level').drop_duplicates(['Name','Gender','Age'],keep='last')
df
Name Gender Age Level
2 Charmander Female 5 7
0 Pikachu Male 4 8
4 Squirtle Male 3 9
5 Squirtle Fe... | python|pandas|dataframe|group-by | 3 |
9,097 | 66,231,322 | Determining the count and percentage of numbers in a string (pandas) | <p>I have a column in a dataset df which contains strings like these</p>
<pre><code>Webs
https://www.mhouse.com/107462464135489/posts/please-lets-be-guidedun-is-where-the-code/142970213918047/
https://www.msed.com/IKONINIBWANASEEDMARCH2020.html
https://www.msed.com/
https://carrice.com/jen/stat/1241025420562178050?lang... | <p>You can count the number of digits in a string using <code>Series.str.count</code> with a regular expression. Additionally, you can get the length of each string in a series with <code>Series.str.len()</code>. Once you do that, calculating the percentage is straight forward!</p>
<pre><code>df["digit_count"... | python|regex|pandas | 5 |
9,098 | 66,246,168 | Pandas - how to build and expanding window dataframe from a series | <p>I'm not allowed to use any df.expanding().apply() solutions but need to go through an approach as the following.
Therefore, given a pd.Series such as</p>
<pre><code>2008-12-31 1.4174
2009-01-01 1.4184
2009-01-02 1.4098
2009-01-05 1.4000
2009-01-06 1.3882
2009-01-07 1.4079
2009-01-08 1.4045
2009-... | <p>You can use <code>shift</code> and construct the data frame with <code>for</code> loop:</p>
<pre><code>pd.DataFrame({
i: s.shift(i).shift(-i) for i in range(1,6)
})
</code></pre>
<p>Output:</p>
<pre><code> 1 2 3 4 5
Date
2008-1... | python|pandas|apply|rolling-computation | 0 |
9,099 | 66,125,362 | Programmatically picking an inequality operator | <p>I'm trying to perform actions based on input from a config file. In the config, there will be specifications for a signal, a comparison, and a value. I'd like to translate that comparison string into a choice of inequality operator. Right now, this looks like</p>
<pre class="lang-py prettyprint-override"><code>de... | <p>You can use the <code>operator</code> module to get functions equivalent to each of the operators.</p>
<pre><code>import operator
funcs = {
'<': operator.lt,
'<=': operator.le,
'=': operator.eq,
'>': operator.gt,
'>=': operator.ge,
'!=': operator.ne
}
def compute_mask(self, sign... | python|pandas|dataframe | 3 |
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