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 |
|---|---|---|---|---|---|---|
354,300 | 61,504,200 | Convert String to Integer within Column in Dataframe (5 Star Rating = 5) | <p>I want to convert a column containing strings of reviews such as 5.0 out of 5 stars to an integer. </p>
<pre><code>0 5.0 out of 5 stars
1 2.0 out of 5 stars
2 5.0 out of 5 stars
3 5.0 out of 5 stars
4 5.0 out of 5 stars
5 5.0 out of 5 stars
6 4.0 out of 5 stars
7 5.0 out of 5 stars
8 5.0 ... | <p>Try splitting the string and converting the first element to float:</p>
<pre><code>df['StarRatingNumeric'] = df.StarRating.apply(lambda r: float(r.split()[0]))
</code></pre>
<p>or if you need integer data type:</p>
<pre><code>df['StarRatingNumeric'] = df.StarRating.apply(lambda r: int(float(r.split()[0])))
</code... | python|pandas|for-loop | 0 |
354,301 | 61,218,872 | Pandas: Add a column of list of values from other columns based on an index list in another column | <p>This is the original data frame, where group contains list of index values of group which each person belongs to.</p>
<pre><code> Name Group
0 Bob [0, 1]
1 April [0, 1]
2 Amy [2, 3]
3 Linda [2, 3]
</code></pre>
<p>This is what I... | <p>I think you need <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.explode.html" rel="nofollow noreferrer"><code>Series.explode</code></a> + <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.map.html" rel="nofollow noreferrer"><code>Series.map</code></a... | python|pandas | 1 |
354,302 | 61,506,162 | Session crash in Colab due to excess usage of RAM | <p>I'm getting the following runtime error when I tried to create a huge 3D numpy array.
<strong>Your session crashed after using all available RAM.</strong></p>
<p>This is the code that's causing the error,</p>
<pre><code>decoder_output_one_hot = np.zeros((30000, 23, 20000), dtype='float32').
</code></pre>
<p>Why i... | <p>Your are attempting to create an array with 30000 x 23 x 20000 = 13,800,000,000 entries. Each entry is a 32-bit floating point number, so the total number of bytes is 13,800,000,000 x (32 / 8) = 55,200,000,000: in other words, your array would occupy over 50GB in RAM, twice what you have available.</p> | numpy|deep-learning|google-colaboratory|ram | 3 |
354,303 | 61,210,787 | Using pandas to look up the values of one column from the closest match between another column and multiple inputs | <p>I have two data frames one is a reference I am comparing the second data frame to the first column of the reference to find the closest matches and then returning the corresponding item from the second column of the reference data frame. I am trying to find a faster method to do this than what I’m currently doing wh... | <p>This is <code>merge_asof</code>:</p>
<pre><code># convert reference values to float
references['A'] = references['A'].astype('float64')
pd.merge_asof(df, references,
left_on='a', right_on='A',
direction='nearest'
)
</code></pre>
<p>Output:</p>
<pre><code> a A B
... | python|pandas | 2 |
354,304 | 61,433,414 | Which values to use when feeding placeholders in Tensorflow? | <p>In the code below, there are a number of tensor operations and calculations. I'd like to see the results of some of those calculations so I can better understand them. Specifically I'd like to see what h looks like during graph execution using <code>print(Session.Run(h))</code>. However, the calculations are depende... | <p>When you use an interactive session you can just set the values in python x = 57, bypassing the placeholder entirely, then evaluate the rest of the graph however you want. </p> | python|tensorflow | 0 |
354,305 | 61,582,280 | Is there a way in python pandas to do "Text to Columns" by location (not by a delimiter) like in excel? | <p>I'm using Vote History data from the Secretary of State, however the .txt file they gave me is 7 million rows, where each row is a string with 27 characters. The first 3 characters are a code for the county. The next 8 characters are the registration ID, the next 8 characters are the date voted, etc. I can't do text... | <p>Simplest I can make it:</p>
<pre><code>import pandas as pd
sample_lines = ['0010000413707312012026R','0010000413708212012027R','0010000413711062012029','0010004535307312012026D]']
COLUMN_NAMES = ['A','B','C','D','E']
df = pd.DataFrame(columns=COLUMN_NAMES)
for line in sample_lines:
row = [line[0:3], line[3:11... | python|pandas|data-mining | 1 |
354,306 | 61,466,920 | Standard deviation of time series data on two columns | <p>I have a data frame with two-columns of data for a day with a time series index. The sample data is in 1-minute and I want to create a 5-minute data frame where a 5-minute interval will be flagged false when the standard deviation of the 5 samples in the respective 5-minute is not deviating by 5% of the mean of the ... | <p>You can use the resample method of the pandas data frames, for that the dataframe most be index with a time stamp. Here an example:</p>
<pre><code>import pandas as pd
import numpy as np
dates = pd.date_range('1/1/2020', periods=30)
df = pd.DataFrame(np.random.randn(30,2), index=dates, columns=['X','Y'])
df.head()
... | python|pandas|statistics|time-series|standard-deviation | 1 |
354,307 | 61,387,313 | Python: Finding time taken for each event in dataframe based on condition | <p>I have a df with two columns, timestamp & eventType.</p>
timestamp is ordered in chronological order, and eventType can be either ['start', 'change', 'end', resolve].</p></p>
<pre><code>['start', 'change'] denotes the start of an event
['end','resolve'] denotes the end of an event
createdTime action... | <p>Better late than never?</p>
<pre><code>df['createdTime'] = pd.to_datetime(df.createdTime)
starts = ['start', 'change']
ends = ['end','resolve']
prev_status = 'end'
spans = []
for i in range(len(df)):
curr_status = df.actionName[i]
if curr_status in starts and prev_status in starts:
pass
elif c... | python|pandas|dataframe|time-series | 0 |
354,308 | 61,255,108 | Python numpy ravel function not flattening array | <p>I have an array of arrays called x and I am trying to do ravel on it but the result is the same x. It is not flattening anything. I have also tried the function flatten(). Can someone explain me why is this happening?</p>
<pre><code>x = np.array([np.array(['0 <= ... < 200 DM', '< 0 DM', 'no checking accoun... | <pre><code>In [455]: x = np.array([np.array(['0 <= ... < 200 DM', '< 0 DM', 'no checking account'], dtype=object),
...:
...: np.array(['critical account/ other credits existing (not at this bank)',
...: 'existing credits paid back duly till now'], dtype=object),
...: ... | python|numpy | 0 |
354,309 | 61,319,533 | Pandas installation with all dependencies | <p>i am new to python. i am using python 3.7 and installed pandas using pip. when i checked for pandas version i found all the dependencies are not installed . so i read somewhere anaconda installation will install all the dependent packages. so i have installed anaconda still when i search for python version it shows ... | <p>The process I follow:</p>
<ol>
<li>Install anaconda from this link <a href="https://www.anaconda.com/distribution/" rel="nofollow noreferrer">https://www.anaconda.com/distribution/</a></li>
<li>Add the Anaconda program directory to your Path environment variable. See here > <a href="https://www.quora.com/How-can-I-... | python|pandas | 0 |
354,310 | 61,529,476 | Why does a specific numpy implementation of the Gauss-Jacobi method significantly reduce iterations? | <p>When implementing the Gauss Jacobi algorithm in python I found that two different implementations take a significantly different number of iterations to converge.</p>
<p>The first implementation is what I originally came up with</p>
<pre class="lang-py prettyprint-override"><code>import numpy as np
def GaussJacobi... | <p>Your second implementation does <code>N</code> <em>actual</em> iterations per increment of <code>k</code>, since the assignment to <code>x</code> already covers the <strong>entire</strong> vector. Its “advantage” thus increases with problem size.</p> | python|numpy|linear-algebra | 1 |
354,311 | 61,556,827 | Parsing a list of lists to a data frame in pandas | <p>I have list of lists. Below is how my list looks like, I want to parse it into a data frame with continuation of values with columns = A,B,C</p>
<pre><code>[ A B C
0 1 2 3
1 1 2 3
2 1 2 3
3 1 2 3
A B C
0 4 5 6
1 4 5 6
2 4 5 6
3 4 5 6
]
</code></pre>
<... | <p>Try this:</p>
<pre><code>import pandas as pd
list1=[]
count=0
while count<len(myList):
list2= myList[count]
list1.append(list2)
#print(list2)
count+=1
df = pd.concat(list1)
print(df)
</code></pre>
<p><code>myList</code> : List which contains sub-lists</p>
<p><code>list2</code> : First it takes ... | python|pandas|list|dataframe | 0 |
354,312 | 61,415,589 | Get value of 'cell' in one dataframe based on another dataframe using pandas conditions | <p>Given DF1:</p>
<pre><code>Title | Origin | %
Analyst Referral 3
Analyst University 10
Manager University 1
</code></pre>
<p>and DF2:</p>
<pre><code>Title | Referral | University
Analyst
Manager
</code></pre>
<p>I'm trying set the values inside DF2 ... | <p>just use pivot, no need for logic:</p>
<pre><code>s = """Title|Origin|%
Analyst|Referral|3
Analyst|University|10
Manager|University|1"""
df = pd.read_csv(StringIO(s), sep='|')
df.pivot('Title', 'Origin', '%')
Origin Referral University
Title
Analyst 3.0 10.0
Manager ... | python|pandas | 1 |
354,313 | 61,475,874 | Convert a sparse matrix to dataframe | <p>I have a sparse matrix that stores computed similarities between a set of documents. The matrix is an ndarray.</p>
<pre><code> 0 1 2 3 4
0 1.000000 0.000000 0.000000 0.000000 0.000000
1 0.000000 1.000000 0.067279 0.000000 0.000000
2 ... | <p>Convert the dataframe to an array:</p>
<pre><code>x = df.to_numpy()
</code></pre>
<p>Get a list of non-diagonal non-zero entries from the sparse symmetric distance matrix:</p>
<pre><code>i, j = np.triu_indices_from(x, k=1)
v = x[i, j]
ijv = np.concatenate((i, j, v)).reshape(3, -1).T
ijv = ijv[v != 0.0]
</code></p... | pandas|numpy|sparse-matrix | 2 |
354,314 | 61,363,873 | Pandas group by max value all columns except datetime | <p>I am having an issue with a dateframe I have created. It has multiple columns along with the 2 columsn im trying to group by and its a date time. </p>
<p>the table is as follows- </p>
<pre><code>product number color solddate price
TV 123 green 20/04/2020 50
TV 123 green 19/04/2020 100
</... | <p>I think this <a href="https://stackoverflow.com/questions/47360510/pandas-groupby-and-aggregation-output-should-include-all-the-original-columns-i">post</a> might be relevant. </p>
<p>Also, this method might be useful ( came across this <a href="https://stackoverflow.com/questions/23394476/keep-other-columns-when-d... | python|pandas | 0 |
354,315 | 61,290,287 | How can I slice a PyTorch tensor with another tensor? | <p>I have:</p>
<pre><code>inp = torch.randn(4, 1040, 161)
</code></pre>
<p>and I have another tensor called <code>indices</code> with values:</p>
<pre><code>tensor([[124, 583, 158, 529],
[172, 631, 206, 577]], device='cuda:0')
</code></pre>
<p>I want the equivalent of:</p>
<pre><code>inp0 = inp[:,124:172,... | <p>Here you go (EDIT: you probably need to copy tensors to cpu using <code>tensor=tensor.cpu()</code> before doing following operations):</p>
<pre><code>index = tensor([[124, 583, 158, 529],
[172, 631, 206, 577]], device='cuda:0')
#create a concatenated list of ranges of indices you desire to slice
indexer = np.r_... | python|numpy|pytorch|tensor | 2 |
354,316 | 61,341,838 | Vectorisation of coordinate distances in Numpy | <p>I'm trying to understand Numpy by applying vectorisation. I'm trying to find the fastest function to do it. </p>
<pre><code>def get_distances3(coordinates):
return np.linalg.norm(
coordinates[:, None, :] - coordinates[None, :, :],
axis=-1)
coordinates = np.random.rand(1000, 3)
%timeit get_distan... | <p>You're not calling <code>np.vectorize()</code> correctly. I suggest referring to <a href="https://numpy.org/doc/stable/reference/generated/numpy.vectorize.html" rel="nofollow noreferrer">the documentation</a>.</p>
<p>Vectorize takes as its argument <em>a function</em> that is written to operate on <em>scalar</em> ... | python|numpy|vectorization | 1 |
354,317 | 61,434,497 | Pandas pivot table gives "FutureWarning: Sorting because non-concatenation axis is not aligned" | <p>I have the following DataFrame:</p>
<pre><code>df = pd.DataFrame({'category':['A', 'B', 'C', 'C'],
'bar':[2, 5, float('nan'), float('nan')]})
</code></pre>
<p>And then I have just one line of code, where I'm trying to apply two aggregation functions on a column in my DataFrame, grouped by value... | <p>I was able to replicate the issue on pandas 0.25.1, the waning is related to <code>pandas.core.reshape.pivot.py</code> that includes the following statement</p>
<pre><code># line 56
return concat(pieces, keys=keys, axis=1)
</code></pre>
<p>Concat is causing the warning. <code>pieces</code> is a list of dataframes ... | python|pandas|warnings|concat | 1 |
354,318 | 61,320,589 | Combining Two Pandas Series gives TypeError: 'DataFrame' object is not callable | <p>I am trying to combine two pandas series, values from one dataset need to be added to another one.
I am getting an error so I have prepared a simple test case following documentation: <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.combine.html" rel="nofollow noreferrer">https://pan... | <p>I have reused the code you have written here additionally with the import line as well:</p>
<pre><code>import pandas as pd
s1 = pd.Series({'falcon': 330.0, 'eagle': 160.0})
s2 = pd.Series({'falcon': 345.0, 'eagle': 200.0, 'duck': 30.0})
s1.combine(s2, max)
</code></pre>
<p>Here is the result:</p>
<pre><co... | python|pandas | 0 |
354,319 | 61,246,705 | Filling Null Values based on conditions on other columns | <p>I want to fill the Null values in the first column based on the value of the 2nd column.
(For example)</p>
<ol>
<li>For "Apples" in col2, the value should be 12 in places of Nan in the col1 </li>
<li>For "Vegies", in col2 the value should be 134 in place of Nan in col1</li>
</ol>
<p>For every description, there ... | <p>**Reupdate</p>
<p>Here, I replicate your DF, and the implementation:</p>
<pre><code>import pandas as pd
import numpy as np
l1 = [12, 134, 23, np.nan, np.nan, 324, np.nan,np.nan,np.nan,np.nan]
l2 = ["Apple","Vegies","Oranges","Apples","Vegies","Sugar","Apples","Melon","Melon","Grapes"]
df = pd.DataFrame(l1, columns... | python|pandas|dataframe|data-cleaning | 1 |
354,320 | 61,546,129 | Selecting rows with a value of False | <p>I have a data set and during validation I have marked the "rejected" rows by masking the bad cell with the boolean False.
I am looking to split that dataframe into two, the good and the bad data. The following code works perfectly for grabbing the good data from the data frame. It selects the rows where that do not... | <p>Answer per @QuangHoang.</p>
<p>Replacing the for-loop with the below:
df[df.eq(False).any(1)]</p>
<p>Reason:
0==False is True</p> | python|pandas | 0 |
354,321 | 61,389,738 | Failed to find data adapter that can handle input: <class 'numpy.ndarray'>, (<class 'list'> c | <p>Below I would like to store the images in my laptop to the variable called X_data by using the function of <code>glob</code> and then split it into training and test set before testing the model.</p>
<pre><code> import cv2
import numpy as np
import tensorflow as tf
from tensorflow.keras import datasets, layers, ... | <p>I see couple of problems in your code:</p>
<ol>
<li>Both <code>train</code> and <code>test</code> are <code>Lists</code>, not <code>Numpy Arrays</code>. Same might be the case with <code>Labels</code> (that part of code is not shared).</li>
<li><p>The part of code, </p>
<p><code>X_data = []
files = glob.glob ("*.... | python|tensorflow|keras | 1 |
354,322 | 61,349,166 | Python - String Formatting (How to Limit Decimal Without the Float Getting Converted Into String) | <pre><code>df = pd.DataFrame(np.random.randn(10).reshape(5,2), index =['a','b','c','d','e'], columns = ['one', 'two'])
convert_decimal = lambda x: '{:.1f}'.format(x)
df = df.applymap(convert_decimal)
df
</code></pre>
<blockquote>
<p>Error: TypeError Traceback (most recent call last)
in
----> 1 abs(df)</p>
... | <p>It looks like you could just cast the lambda computation as a float.</p> | python|pandas | 0 |
354,323 | 61,330,819 | How to add additional arguments to map(pd_read_csv)? | <p>About 2 years ago someone had a very elegant way of reading multiple csv files into one dataframe:
<a href="https://stackoverflow.com/questions/20906474/import-multiple-csv-files-into-pandas-and-concatenate-into-one-dataframe">Import multiple csv files into pandas and concatenate into one DataFrame</a></p>
<pre><co... | <p>You can use <em>itertools.starmap</em>.</p>
<p>This function takes:</p>
<ul>
<li>a function as the first argument,</li>
<li>and runs it on each set of parameters from an iterable (second
argument - a list of tuples).</li>
</ul>
<p>I ran the following example:</p>
<pre><code>import itertools as it
# read_csv wra... | python|pandas|csv|concatenation | 0 |
354,324 | 61,249,487 | how to drop duplicates after merging two dataframes? | <p>I have two dataframes ,</p>
<pre><code>A=
ID compponent weight
12 Cap 0.4
12 Pump 183
12 label 0.05
14 cap 0.6
B=
ID compponent_B weight_B
12 Cap_B 0.7
12 Pump_B 189
12 label 0.05
</code></pre>
<p>when i do merge of this two dataframes based on the ID as a key ... | <p>you can create a column with a <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.cumcount.html" rel="nofollow noreferrer"><code>cumcount</code></a> per ID to be able to <code>merge</code> on ID and this new column so like:</p>
<pre><code>dfm = dfA.assign(cc=dfA.groupby(... | python|pandas|dataframe|merge|drop-duplicates | 1 |
354,325 | 61,502,603 | Import Tensorflow without NVIDIA GPU (ImportError: Could not find 'nvcuda.dll') | <p>I have installed the <code>tensorflow</code> package using Anaconda Navigator. When I try to run <code>import tensorflow</code> in a jupyter notebook I get the following error:</p>
<pre><code> OSError Traceback (most recent call last)
D:\ProgrammFiles\Anaconda\lib\site-packages\t... | <p>As discussed in the comments, the problem was only in your installation. Using anaconda-navigator isn't the best way to install <code>tensorflow</code>. My assumption is either <code>tensorflow-base</code> or <code>tensorflow-estimate</code> has a GPU dependency which is the reason why it kept showing the posted err... | python|tensorflow|anaconda | 1 |
354,326 | 61,359,162 | Convert a list of tensors to tensors of tensors pytorch | <p>I have this code:</p>
<pre class="lang-py prettyprint-override"><code>import torch
list_of_tensors = [ torch.randn(3), torch.randn(3), torch.randn(3)]
tensor_of_tensors = torch.tensor(list_of_tensors)
</code></pre>
<p>I am getting the error:</p>
<blockquote>
<p>ValueError: only one element tensors can be conve... | <p>Here is a solution:</p>
<pre><code>tensor_of_tensors = torch.stack((list_of_tensors))
print(tensor_of_tensors) #shape (3,3)
</code></pre> | python|python-3.x|pytorch | 9 |
354,327 | 61,557,367 | Vectorized extraction of submatrices in numpy-array | <p>My goal is to implement a median-filter, which is a function, that replaces each pixel in a (mostly) 2d-array with the median of its surrounding pixels. It can be used to denoise images.</p>
<p>My implementation extracts submatrices from the original matrix, that contain the pixel itself and its neighbors. This ext... | <p>Here is a vectorized solution. However, you can come up with a faster solution by paying attention to memory order of the image array:</p>
<pre><code>from numpy.lib.stride_tricks import as_strided
img_padded = np.pad(img, 1, mode='constant')
sub_shape = (fsize, fsize)
view_shape = tuple(np.subtract(img_padded.shap... | python|numpy|image-processing|computer-vision|vectorization | 3 |
354,328 | 61,467,671 | Finding occurances by comparing 2 columns in dataframe | <p>This is my dataframe:</p>
<pre><code>d = {'id':[1,2,3,4,5,6,7,8],
'col1':['A','A','A','B','B','B','C','D'],
'col2':['C','C','D', 'E', 'F', 'F','G','H'],
'data':['abc','def','ghk','lmn','opq','rst','uvw','xyz']
}
df = pd.DataFrame(d)
</code></pre>
<p>I want to find all values in col2 for each unique ... | <p>If you insist on getting exactly that output, here's one way:</p>
<pre><code>df = df.drop_duplicates(subset=[
'col1', 'col2'
]).drop('id', axis=1).reset_index(drop=True)
df['col1'] = np.where(df.col1.duplicated()==True, '', df.col1)
</code></pre>
<p>Which produces: </p>
<pre><code> col1 col2
0 A ... | pandas | 1 |
354,329 | 61,310,920 | Counting number of consecutive more than 2 occurences | <p>I am beginner, and I really need help on the following:</p>
<p>I need to do similar to the following but on a two dimensional dataframe <a href="https://stackoverflow.com/questions/37934399/identifying-consecutive-occurrences-of-a-value/61310753#61310753">Identifying consecutive occurrences of a value</a> </p>
<p... | <p>Try:</p>
<pre class="lang-py prettyprint-override"><code>import pandas as pd
df=pd.DataFrame({0:[1,0,1,0,0,1,1,1], 1:[0,1,1,0,1,1,1,0], 2: [1,0,1,1,0,0,1,1]})
out_2_df=((df.diff(axis=0).eq(0)|df.diff(periods=-1,axis=0).eq(0))&df.eq(1)).sum(axis=0)
>>> out_2_df
[3 5 4]
</code></pre> | python|pandas|dataframe | 1 |
354,330 | 68,731,821 | How to sample data from Pandas Dataframe based on value count from another column | <p>I have a dataframe of about 400,000 observations. I want to sample 50,000 observations based on the amount of each state that's in a 'state' column. So if there is 5% of all observations from TX, then 2,500 of the samples should be from TX, and so on.</p>
<p>I tried the following:</p>
<pre><code>import pandas as p... | <p>Weights modify the probability of any one row to be selected, but can’t provide strict guarantees on counts of given values, as you want. For that you would need <code>.groupby('state')</code>:</p>
<pre><code>>>> rate = df['state'].value_counts(normalize=True)
>>> rate
TX 0.5
NY 0.3
CA 0.2... | python|pandas|sample | 3 |
354,331 | 68,469,118 | Appropriate concat for dataframes of different size | <p>I have some dataframes of different row size (columns are the same) and I want to calculate the sum of each column in the last row and then combine them in one dataframe.</p>
<p>I'm using this function:</p>
<pre><code>def day_sum(tot_df):
week_days = ['MO', 'TU', 'WE', 'TH', 'FR', 'SA', 'SU', 'Total']
week = []
for... | <p>Instead of your code just use:</p>
<pre><code>df.loc[len(df)] = ['sum'] + df[df.columns[1:]].sum(0).tolist()
</code></pre> | python|pandas | 0 |
354,332 | 68,767,795 | Adding timestamps to pandas series | <p>I'm trying to write a code that will count a number of records in a .csv file in a single hour. So, for example:</p>
<pre><code> data = pd.read_csv('2021-07-30.csv', parse_dates=['time'], infer_datetime_format=True)
datafiltr = data[data.lane == "Lane 4 Op2"]
datafiltr['time'] = pd.to_datetime(dat... | <p>have you tried with resampling, but at least one of your time sample should be of max time to consider till there</p>
<pre><code>df.set_index('time').resample('H').agg('count')
</code></pre>
<p>out:</p>
<pre><code> 1 2
0
2021-08-13 13:00:00 1 1
2021-08-13 14:00:00 1 1
2021-08-13 15:00:00 1 1
2021-0... | python|pandas|csv|matplotlib | 0 |
354,333 | 68,556,051 | Fast Style Transfer tensorflow Python | <p>I follow this link to try machine learning - real time Fast Style Transfer <a href="https://www.youtube.com/watch?v=LWlbFVtPiwo&ab_channel=CODEMENTAL" rel="nofollow noreferrer">https://www.youtube.com/watch?v=LWlbFVtPiwo&ab_channel=CODEMENTAL</a>.</p>
<p>However, in my python it shows GPU available: False D... | <p>GPU is recommended in computation intensive deep learning problems and you need both "Source" and "Style" image either in cloud or local storage to get a Fast Style transfer image.Attaching <a href="https://www.tensorflow.org/hub/tutorials/tf2_arbitrary_image_stylization" rel="nofollow noreferr... | python|tensorflow|jupyter-notebook | 0 |
354,334 | 68,601,912 | Fastest way to append a row to an existing data frame? | <p>I know this question has been asked many a time, but none of the solutions already posted on this site is ideal.</p>
<p>I have tested various methods found here, and timed them using IPython, I will post the results below:</p>
<pre class="lang-py prettyprint-override"><code>In [161]: %%timeit
...: s = Series([1... | <p>First we establish the time needed to create a dataframe:</p>
<pre><code>%%timeit
songs = pd.DataFrame(index=np.arange(4464 ), columns=np.arange(15))
100 loops, best of 5: 5.21 ms per loop
</code></pre>
<p>It takes around 5.2 ms to create this dataframe and so we can use it as a reference for the next cases (to pre... | python|python-3.x|pandas|dataframe | 0 |
354,335 | 68,771,454 | Split dataframe based on a continuous column value, not present in the dataframe | <p>I am trying to split a dataframe into several ones based on a list with the splitting points or threshold, which don't necessarily need to be in the reference column. I haven't quite found an answer for this</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th>Values</th>
<th>Another header</... | <p>Another looping option</p>
<pre><code>thresholds = [4,10]
thresholds = [float('-inf')] + thresholds + [float('inf')]
empty = []
for i in range(len(thresholds)-1):
t_start = thresholds[i]
t_end = thresholds[i+1]
temp = df.query('Values > @t_start & Values <= @t_end')
empty.append(temp) ... | python|pandas | 1 |
354,336 | 68,868,881 | Match two separate dataframes to a larger dataframe based on matching values (In Python) | <p>I have 1 large dataframe, and 2 smaller dataframes in which I would like to append/match based on certain criteria.</p>
<p><strong>Data</strong></p>
<p><em>df1</em> (large dataframe)</p>
<pre><code>id Date pp pos
aa q122 200 10
aa q222 200 10
bb q322 500 5
bb q422 500 5
cc q122 100 2
cc q22... | <p>We can chain <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html" rel="nofollow noreferrer"><code>merge</code></a> operations:</p>
<pre><code>out = (
df1.merge(
df2, left_on=['id', 'Date'], right_on=['name', 'date1'], how='outer'
).merge(
df3, left_on=['id', 'Dat... | python|pandas|numpy | 2 |
354,337 | 68,484,274 | Pandas: Get matched value between two columns | <p>I have <strong>2 dataframes</strong></p>
<pre><code>data1 = {'Product': ['AAA','BBB','CCC','DDD','EEE','FFF'],
'Id': ['247610','287950','229XYZ','987340','111500','2345OZ'],
'Price':[40,50,0,985,34,0]}
data2 = {'Product': ['AAA','BBB','CCC','DDD','EEE'],
'Id': [508760,287950,678897,987340,11... | <p>It's easier to convert your 'Id' of df2 from int to str:</p>
<pre><code>df2 = pd.DataFrame(data2).astype({'Id': str})
df1['bestId'] = df1["Id"].isin(df2["Id"])
</code></pre>
<pre><code>>>> df1
Product Id Price bestId
0 AAA 247610 40 False
1 BBB 287950 50 ... | python|pandas|dataframe | 1 |
354,338 | 68,751,253 | How to keep leading zeros in a column when reading JSONwith Pandas? | <pre><code>total_list = ['123456','00123456']
df = pd.read_json(json.dumps(total_list))
print(df)
</code></pre>
<p>The result is:</p>
<pre><code> 0
0 123456
1 123456
</code></pre>
<p>But I want to keep the '0',how can I do this?</p> | <p>Use <code>dtype == str</code>:</p>
<pre><code>total_list = ['123456','00123456']
df = pd.read_json(json.dumps(total_list), dtype=str)
print(df)
</code></pre>
<p>Output:</p>
<pre><code> 0
0 123456
1 00123456
</code></pre> | python|json|pandas|dataframe | 0 |
354,339 | 68,764,677 | Extract HTML information from df variable | <p>Dear stackoverflow community,</p>
<p>This is my first time asking a question here. Hope you could cut me some slack.
Here is the description of a problem:</p>
<ol>
<li>I convert KML file to CSV using ogr2org <br />
<code>ogr2ogr -f CSV output.csv 'some KML file'.kml</code></li>
<li>I then read in the csv file in pan... | <p>You could use <code>str.extractall</code> with...</p>
<pre><code>df[['ID1', 'class', 'fold']] = df['description'].str.extractall(r'</b>\s?(\d+)<').unstack()
</code></pre>
<p>Or <code>str.findall</code> with something like this...</p>
<pre><code>df[['ID1', 'class', 'fold']] = df['description'].str.findall(r'... | html|pandas|kml | 1 |
354,340 | 68,809,809 | returning scikit-learn object while using Joblib | <p>I have a numpy array, and I am using sklearn to transform the array along the first axis. I also want to save the transformer object in a dict to use later in the code.
Here is my code:</p>
<pre><code>scalers_dict = {}
for i in range(train_data_numpy.shape[1]):
for j in range(train_data_numpy.shape[2]):
... | <p>You can use <a href="https://docs.dask.org/en/latest/" rel="nofollow noreferrer">Dask-ML</a> which is implemented on the top of <a href="https://dask.org/" rel="nofollow noreferrer">Dask Library</a>, yet it is compatible with <code>scikit-learn</code>.</p>
<p><a href="https://ml.dask.org/install.html?#installation" ... | python|scikit-learn|parallel-processing|numpy-ndarray|joblib | 1 |
354,341 | 68,486,056 | Different behavior while reading DataFrame from parquet using CLI Versus executable on same environment | <p>Please consider following program as <a href="https://stackoverflow.com/help/minimal-reproducible-example">Minimal Reproducible Example -MRE</a>:</p>
<pre class="lang-py prettyprint-override"><code>import pandas as pd
import pyarrow
from pyarrow import parquet
def foo():
print(pyarrow.__file__)
print('versi... | <p>Credit to @U12-Forward for assisting me in debugging the issue.</p>
<p>After a bit of research and debugging, and exploring the library program files, I found that pyarrow uses <code>_ParquetDatasetV2</code> and <code>ParquetDataset</code> functions which are essentially two different functions that reads the data f... | python|pandas|pyinstaller|parquet|pyarrow | 3 |
354,342 | 68,646,855 | How to ensure that my model is using all available GPU in jupyter notebook | <p>I am using <code>tensorflow 2.3</code> dedicated with <code>2-GPU's</code>. I am using <code>styleformer</code> model to get informal to formal sentences. I want to use all <code>2-GPU's</code> for this task.</p>
<p><strong>Here is the information about GPU:</strong></p>
<pre><code>!nvidia-smi
| NVIDIA-SMI XXX.XX.X... | <p>your model is not using the GPU, there could be multiple reasons,</p>
<ol>
<li>you did not install the Cuda toolkit & drivers for the GPU to access in the developer mode</li>
<li>Nvidia Driver issue ( uninstall & install)</li>
<li>Tensorflow version issue</li>
</ol>
<p>check all these and try again. The note... | python-3.x|tensorflow|jupyter-notebook|nvidia | 0 |
354,343 | 68,731,152 | How to create a heatmap to display matching and not matching data | <p>I have some parcel data that shows which city the volume of parcels belongs to.</p>
<pre><code>+---------+-------------+----------------+
| Volume | City | Foreign_City |
+---------+-------------+----------------+
| 200 | Chicago | New York |
| 300 | Los Angeles | NaN |
| 1... | <ul>
<li>Given the existing data, when there's a match between <code>'City'</code> and <code>'Foreign_City'</code>, there are three options depending on your desired plot.
<ol>
<li>Use <code>.fillna</code> in <code>'Foreign_City'</code> with the corresponding row from <code>'City'</code>.
<ul>
<li>The heatmap will have... | python|pandas|matplotlib|plot|seaborn | 1 |
354,344 | 68,782,958 | Changing width of particular column in Dataframe | <p>I have pandas Dataframe with 10-11 Columns. I want to convert this dataframe into html using pd.to_html(index=False, border=0). However, I want to change CSS of returned table.
Can we change column width of dataframe itself, or format tags of returned table from pd.to_html()</p>
<p>Html Code for expected table</p>
... | <p>You can use the styler of df:</p>
<pre><code>df_html = df.style.set_table_styles({
'A': [{'selector': 'thead tr',
'props': [('width', '5%')]}],
'D': [{'selector': 'thead tr',
'props': [('width', '3%')]}],
})
print(df_html.render(sparse_index=True))
</code></pre>
<p>The output is pretty... | html|css|pandas|dataframe | 0 |
354,345 | 68,611,026 | Loss of Image Information | <p>When reading a JPEG image from a <a href="https://www.tensorflow.org/tutorials/load_data/tfrecord" rel="nofollow noreferrer">TFRecord</a> there seems to be loss of information. Here is an example:</p>
<ul>
<li>Original image:
<a href="https://i.stack.imgur.com/QyKMI.jpg" rel="nofollow noreferrer">https://i.stack.img... | <p>What do you mean by "loss of image informatIon"? I actually found out tf.io.encode_jpeg and tf.io.decode_jpeg op (using all their defaults), are not necessary symmetrical, meaning if you apply decode follow by encode, the image jpeg can have a different byte count. If you encode_jpeg with high quality=100,... | python|tensorflow|machine-learning|keras|tfrecord | 0 |
354,346 | 68,596,129 | Iterate each Pandas df row and identify if row value is in list, if so pull that value into df | <p>I have a pandas df with hand entered values for states around the world. I have a list of states values that are properly formatted and contain the correct syntax. I want to iterate through each row in the pandas df and compare the value per row against all values in the list of states to determine whether the value... | <p>Try the following:</p>
<pre><code>s = set([i.lower() for i in states_list])
df['match'] = df['state_name'].apply(lambda x: list(set([i.strip().lower() for i in x.split(',')]).intersection(
s)))
df['match']=df['match'].apply(lambda x: [i[0].upper() + i[1:] for i in x])
</code></pre> | python|pandas|dataframe|contains|difflib | 1 |
354,347 | 68,648,581 | Getting wrong results with np.argpartition, while selecting maximum n values from an array | <p>so I was using <a href="https://stackoverflow.com/a/23734295/12705907">this</a> answer on 'How do I get indices of N maximum values in a NumPy array?' question. I used it in my ML model in which it outputs Logsoftmax layer values and I was thinking to get top 4 classes in each. In most of the cases, it sorted and ga... | <p>If you're not planning on actually utilizing the sorted indices, why not just use <a href="https://numpy.org/doc/stable/reference/generated/numpy.sort.html" rel="nofollow noreferrer"><code>np.sort</code></a>?</p>
<pre><code>>>> arr = np.array([-3.0302, -2.7103, -7.4844, -3.4761, -5.3009, -5.2121, -3.7549,
... | python|arrays|numpy|sorting|numpy-slicing | 1 |
354,348 | 68,487,967 | Inserting rows in df based on groupby using value of previous row | <p>I need to insert rows based on the column week based on the groupby type, in some cases i have missing weeks in the middle of the dataframe at different positions and i want to insert rows to fill in the missing rows as copies of the last existing row, in this case copies of week 7 to fill in the weeks 8 and 9 and c... | <p>For the first part of your question. Suppose we have a dataframe like the following:</p>
<pre><code>df = DataFrame({"project":[1,1,1,2,2,2], "week":[1,3,4,1,2,4], "value":[12,22,18,17,18,23]})
</code></pre>
<p>We can create a new multi index to get the additional rows that we need</p>
<... | python|pandas|dataframe|missing-data | 1 |
354,349 | 68,604,186 | Convert 3D RGB np array to 2D binary | <p>I am currently trying to find an efficient way of taking an RGB image and converting it to a binary/ black and white image. To the likes of:</p>
<pre><code>RGBnp = [
[[255, 255, 255], [0 , 0 , 0 ], [0 , 0 , 0 ]],
[[255, 255, 255], [255, 255, 255], [255, 255, 255]],
[[255, 255, 255... | <p>Here you go:</p>
<pre><code>RGBnp = np.array(RGBnp)
RGBnp[RGBnp == 255] = 1
BinaryNP = RGBnp[:,:,0]
</code></pre> | python|numpy|matrix | 2 |
354,350 | 68,581,545 | Scatter Plot With Multi Column Data in Plotly Express | <p>I have a <code>pandas</code> dataframe like below</p>
<pre><code> x s y
Date
2021-06-25 1 red 2
2021-06-28 2 red 3
2021-06-29 3 red 4
2021-06-25 1 blue 2
2021-06-28 2 blue 3
2021-06-29 3 blue 4
</code></pre>
<p>How can I cre... | <ul>
<li>your sample data looks problematic, both red and blue have same values. Have added .5 to blue to demonstrate</li>
<li>simple <strong>pandas</strong> to structure data first, so colors are columns</li>
<li>then use <strong>plotly express</strong> <code>scatter()</code></li>
</ul>
<pre><code>import pandas as pd... | pandas|dataframe|plotly | 1 |
354,351 | 68,747,545 | How to calculate cumulative percent change by each group? | <p>I'd like to create a new column to calculate the cumulative percent change by each group</p>
<p>Sample dataset:</p>
<pre><code>import pandas as pd
df = pd.DataFrame({'Group':['A', 'A', 'A', 'B', 'B'],
'Col_1':[100, 200, 300, 400, 500],
'Col_2':[55, 66, 77, 88, 99]})
</cod... | <p>You need to <code>groupby</code> twice, once to compute the percent change (with <code>pct_change</code>) and once for the cumulative sum+1 (<code>cumsum</code> and <code>add(1)</code>):</p>
<pre><code>df['CPC'] = (df.groupby('Group')['Col_2']
.pct_change()
.fillna(0)
.gr... | pandas|dataframe|group-by | 1 |
354,352 | 68,778,058 | Pandas Dataframe Subtract Value From Previous Rows Based On Condition | <p>I have the following Pandas dataframe:</p>
<p><strong>UPDATE:</strong></p>
<p><strong>I slightly changed the example (Last row) to make the output clearer for @mozway</strong></p>
<pre><code> value initial_quantity updated_quantity
date
2021-09-01 50 100 100
2021-10-01 50 ... | <p>You can use <a href="https://pandas.pydata.org/pandas-docs/version/0.23/generated/pandas.Series.cumsum.html" rel="nofollow noreferrer"><code>cumsum</code></a> + <a href="https://pandas.pydata.org/pandas-docs/version/1.0.3/reference/api/pandas.Series.clip.html" rel="nofollow noreferrer"><code>clip</code></a>:</p>
<pr... | python|pandas|dataframe | 1 |
354,353 | 68,790,771 | AttributeError: module 'tensorflow.compat.v2' has no attribute 'depth_to_space' | <p>I am trying to run a code, which was written with tensorflow version 1.4.0
I am running my code on google colab which gives in tensorflow version 2.x with it.</p>
<p>To run my code, I am using backward compatibility like:
replacing <code>import tensorflow as tf</code> with</p>
<pre><code>import tensorflow.compat.v1 ... | <p>I tried with <strong>Tensorflow 1.15</strong>.Its working fine.
Looks like your Tensorflow version bit old,</p>
<p>Below sample code tested with Tf 1.15 without any error.</p>
<pre><code>import tensorflow as tf
print(tf.__version__)
x = [[[[1, 2, 3, 4]]]]
tf.nn.depth_to_space(x, 2, data_format='NHWC', name=None)
</... | python|tensorflow|tensorflow1.15 | 0 |
354,354 | 68,485,692 | Copy rows from one dataframe to another dataframe for specific matched rows | <p>I have a dataframe <code>df1</code> like the following:</p>
<pre><code>ID Name Test1
100 Ben 30
111 Mark 40
122 Dave 25
</code></pre>
<p>and another dataframe <code>df2</code> like the following:</p>
<pre><code>ID Test2 Test3
100 22 ... | <p>You want <code>pandas.merge</code>:</p>
<pre><code>>>> pd.merge(df1, df2, on="ID", how="left")
ID Name Test1 Test2 Test3
0 100 Ben 30 22.0 29.0
1 111 Mark 40 NaN NaN
2 122 Dave 25 37.0 34.0
</code></pre> | python|pandas|dataframe | 1 |
354,355 | 68,553,853 | if df.A=0 & df.b=1 then df.c=1/ pandas python | <p>df:</p>
<pre><code>A B
0 1
1 1
0 0
</code></pre>
<p><strong>Aim</strong>: if df.A=0 & df.B=1 then create a column C=1 else return nothing. The result should be:
df:</p>
<pre><code>A B C
0 1 1
1 1
0 0
0 1 1
</code></pre>
<p>My current code gives this error: "ValueError: The truth value of a DataF... | <p>Try:</p>
<pre><code>m=(df['A'].eq(0)) & (df['B'].eq(1))
#Finally:
df['C'] =m.astype(int).replace(0,'')
</code></pre>
<p>OR</p>
<pre><code>#import numpy as np
m=(df['A'].eq(0)) & (df['B'].eq(1))
df['C']=np.where(m,1,'')
</code></pre>
<p>OR</p>
<p>For your current method use bitwise <code>&</code> and <cod... | python|pandas|dataframe | 1 |
354,356 | 68,818,383 | pip install pandas conflict with Pylance | <p>"pip is not defined"
"install is not defined"
"pandas is not defined"</p>
<p>I'm trying to install pandas into VSCode since I received a "ModuleNotFoundError" from trying to import pandas in the first place.</p>
<p><img src="https://i.stack.imgur.com/xhLGo.png" alt="" /></p> | <p>So i do not have the knowledge to go in the details to explain why pip is particular but pip is an executable so you have to execute it in a terminal. to install pandas (or any other module from pyPI like sklearn, seaborn, time and many others) follow the instruction (it works for me):</p>
<ul>
<li>go on VScode and ... | python|pandas|visual-studio-code|pip | 0 |
354,357 | 68,864,520 | Pandas: Cell frequency count by index | <p>My dataframe is a long list of 4 letters, <code>'A', 'T', 'G','C'</code>, I need to count the frequency of each letter by index</p>
<pre><code>df = pd.DataFrame({'cases': ['ACCTTGTAGTGTATTTTATGACCAAATGACTTTTTCCCCCCAGTGGCTAATTTGTCTCAGGCCTGCGTCTTAAAGAGACACGGTAATGAGTAGGAAGTCCAGCGTGGTCTGGA','ACCTTGTACTGTATCTTATGACCAGATG... | <p>Let us do <code>explode</code> with <code>crosstab</code></p>
<pre><code>s = df.cases.map(list).explode()
out = pd.crosstab(s.groupby(level=0).cumcount(),s)
Out[583]:
cases A C G T
row_0
0 3 0 1 0
1 0 4 0 0
2 0 4 0 0
3 0 0 0 4
4 0 0 0 4
.. .. .. ..
108 0... | python|pandas|dataframe | 6 |
354,358 | 68,795,120 | What's an alternative way to wright a nested for loop instead of single line for loop below? I have been getting index errors on my current solution | <p><img src="https://i.stack.imgur.com/eos7j.jpg" alt="example question" /></p>
<pre><code>import sys
import numpy as np
myarray=[]
for j in range(3):
myarray.append(j)
for i in range (3):
myarray[i]=i+j
print(myarray)
</code></pre> | <p>The key thing to understand is you are creating nested lists here. You need to create an intermediate list on each outer iteration to append to the final list.</p>
<pre><code>>>> result = []
>>> for j in range(3):
... intermediate = []
... for i in range(3):
... intermediate.append(... | python|numpy | -1 |
354,359 | 68,492,035 | Python Parallelise Simple For Loop | <p>I am trying to make the code below run faster. In its current state, it is taking around 5-6 minutes, which is a lot for the occasion. I am working on two pandas dataframes, taking a datetime and an 'instrument' from the <code>rfqs</code> dataframe, matching the instrument on the second dataframe and finding the clo... | <p>Letting Pandas do its magic by formulating the problem at a higher level of abstraction may already yield the speed-up you're looking for.
I'd create a new column <code>nearest_date</code> in <code>rfqs</code>, e.g.</p>
<pre><code>mids = mids.set_index("instrument") # faster lookup
rfqs['nearest_date'] = r... | python|pandas|numpy|parallel-processing | 0 |
354,360 | 68,774,902 | Pandas Date Conversion from "23-Oct-2020; 27-Aug-2020" to "10/23/2020; 8/27/2020" | <p>I received data from an external source that has time stamps in DDMMMYYYY format and want to convert it to MM/DD/YYYY format. Can you think of a way to do this?</p>
<p>Input
23-Oct-2020
27-Aug-2020
04-Dec-2019</p>
<p>Output that i am looking to get
10/23/2020
8/27/2020
12/04/2019</p> | <p>Like this:</p>
<pre><code>df['date'] = pd.to_datetime(df['date'], format='%d-%b-%Y')
</code></pre> | python-3.x|pandas|dataframe | 1 |
354,361 | 68,523,348 | How to apply statististical tests (functions) on pandas dataframe on combination of subsets of data | <p>I have dataframe which is similar to this one.</p>
<pre><code>import pandas as pd
import string
import random
def generate_example_dataframe()-> pd.DataFrame:
"""
This simple function will generate simple dataframe in long format
"""
num = 20 # number of regions udsed... | <p>im also learn other methods from others' answers. i made a solution like this below...</p>
<pre><code># grouping
df['grouping']=df['region']+"_"+df['group']+"_"+df['condition']
for i in df.grouping.unique():
print(i)
t='result_'+i
locals()[t]=stats.ttest_1samp(df.loc[df['grouping']=... | python|pandas|pandas-groupby|statistical-test | 0 |
354,362 | 68,560,005 | numpy broadcast multiply on condition? | <p>I have two arrays, one of shape <code>arr1.shape = (1000,2)</code> and the other of shape <code>arr2.shape = (100,)</code>.</p>
<p>I'd like to somehow multiply <code>arr1[:,1]*arr2</code> where <code>arr1[:,0] == arr2.index</code> so that I get a final shape of <code>arr_out.shape = (1000,)</code>. The first column ... | <p>I believe this does exactly what you want:</p>
<pre><code>indices, values = arr1[:,0].astype(int), arr1[:,1]
arr_out = values * arr2[indices]
</code></pre> | python|arrays|numpy | 2 |
354,363 | 68,791,512 | Pandas to_numeric unable to turn decimal data from db2 into float64 | <p>The process is as below:</p>
<ol>
<li><p>Script retrieve a column from a data in IBM DB2. The data is of data type = "Decimal"</p>
</li>
<li><p>after retrieval, the queries is store in a variable called"result" and being casted into a dataframe column "loading". dtype indicates the data... | <p>This is because you still have parantheses. Remove them then convert:</p>
<pre><code>Tuen_Mun_Weather_2013["Loading"] = Tuen_Mun_Weather_2013.Loading.str.replace('\(|\)','').astype('float64')
</code></pre> | python|pandas|db2 | 1 |
354,364 | 68,481,158 | Which Java class is compatible with python Pandas DataFrame when using DJL(Deep Java Library)? | <p>I'm trying to import Python Tensorflow custom model to <code>spring-boot</code> using <code>DJL Tensorflow</code>, and the model gets <code>Pandas DataFrame</code> as both input and output.</p>
<p>I'm wondering if there is any particular table or dataFrame class that is applicable for Criteria<I, O> and ZooMod... | <p>You need create your own <code>Translator</code> to convert <code>DataFrame</code> into <code>NDList</code>:</p>
<pre><code> class MyTranslator implements NoBatchifyTranslator<DataFrame, Classifcations> {
@Override
public NDList processInput(TranslatorContext ctx, DataFrame input) {
... | java|pandas|spring-boot|dataframe|djl | 0 |
354,365 | 68,704,002 | ImportError: cannot import name 'ABCIndexClass' from 'pandas.core.dtypes.generic' | <p>I have this output :</p>
<blockquote>
<p>[Pandas-profiling] ImportError: cannot import name 'ABCIndexClass' from 'pandas.core.dtypes.generic'</p>
</blockquote>
<p>when trying to import pandas-profiling in this fashion :</p>
<pre class="lang-py prettyprint-override"><code>from pandas_profiling import ProfileReport
</... | <p>Pandas v1.3 renamed the <code>ABCIndexClass</code> to <code>ABCIndex</code>. The <code>visions</code> dependency of the <code>pandas-profiling</code> package hasn't caught up yet, and so throws an error when it can't find <code>ABCIndexClass</code>. Downgrading pandas to the 1.2.x series will resolve the issue.</p>... | python|pandas|pandas-profiling | 18 |
354,366 | 68,608,741 | Numpy array with different data types | <p>We both know that: "Numpy array is multidimensional array of objects of all the same type"</p>
<p>However, I could create a Numpy array that contains different data types as example below. Can anyone give an explain, how it could be.</p>
<pre><code>import numpy as np
a = np.array([('a',1),('b',2)],dtype=[... | <p>Those are numpy records:</p>
<ul>
<li><a href="https://numpy.org/doc/stable/user/basics.rec.html" rel="nofollow noreferrer">https://numpy.org/doc/stable/user/basics.rec.html</a></li>
</ul>
<p>Numpy provides two data structures, the homogeneous arrays and the structured (aka <em>record</em>) arrays. The latter one, w... | python|numpy|multidimensional-array | 3 |
354,367 | 68,605,299 | ValueError: Cannot convert non-finite values (NA or inf) to integer | <pre><code>df.dtypes
name object
rating object
genre object
year int64
released object
score float64
votes float64
director object
writer object
star object
country object
budget float64
gross float64
company object
runtime float64... | <p>Assuming that the budget does not contain infinite values, the problem may be because you have nan values. These values are usually allowed in floats but not in ints.</p>
<p>You can:</p>
<ol>
<li>Drop na values before converting</li>
<li>Or, if you still want the na values and have a recent version of pandas, you ca... | python|pandas|numpy | 8 |
354,368 | 68,471,066 | Set individual wedge hatching for pandas pie chart | <p>I am trying to make pie charts where some of the wedges have hatching and some of them don't, based on their content. The data consists of questions and yes/no/in progress answers, as shown below in the MWE.</p>
<pre><code>import pandas as pd
import matplotlib.pyplot as plt
raw_data = {'Q1': ['IP', 'IP', 'Y/IP', 'Y... | <p>This snippet shows how to add hatching in custom colors to a pie chart. You can extract the Pandas valuecount - this will be a <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html" rel="nofollow noreferrer">Series</a> - then use it with the snippet I have provided.</p>
<p>I have add... | python|python-3.x|pandas|matplotlib | 1 |
354,369 | 68,593,293 | Performance tuning: string wordcount in df | <p>I have a df with column "free text". I wish to count how many characters and words each cell has.
Currently, I do it like this:</p>
<pre><code>d = {'free text': ["merry had a little lamb", "Little Jonathan found a chicken"]}
df = pd.DataFrame(data=d)
df['Chars'] = df['free text'].apply(... | <p>IIUC:</p>
<p>you can try via <code>str.len()</code> and <code>str.count()</code>:</p>
<pre><code>df['Chars'] = df['free text'].str.len()
df['Words'] = df['free text'].str.count(' ')+1
</code></pre>
<p>Sample dataframe used:</p>
<pre><code>d = {'free text': ["merry had a little lamb", "Little Jonathan ... | python|pandas|performance | 1 |
354,370 | 68,661,587 | .loc function for a specific label returning an empty data frame? | <p>For context, I'm trying to filter out rows in my dataframe that only belong to the year 2021.</p>
<p>This is my script code:</p>
<p><code>test = all_SS_batting_columns.loc[all_SS_batting_columns['Year'] == '2021']</code></p>
<p>but it only returns:</p>
<pre><code>Empty DataFrame
Columns: [index, Year, Age, Tm, Lg, G... | <p>Duplicate df name to see all columns:</p>
<pre><code> test = all_SS_batting_columns[all_SS_batting_columns.loc[all_SS_batting_columns['Year'] == '2021']]
</code></pre> | python|pandas|filter|.loc | 0 |
354,371 | 68,782,961 | How to know a movie has how many 0.5/1/1.5/2/2.5/3/3.5/4/4.5/5 rating that rated by every user? | <p>I would like to know how many 0.5/1/1.5/2/2.5/3/3.5/4/4.5/5 ratings that rated by every user in a data frame of a certain movie which is Ocean's Eleven (2001) in order to calculate Pearson Correlation using the formula.</p>
<p><strong>Below is the code</strong></p>
<pre><code>import numpy as np
import pandas as pd
... | <p>You can use <code>groupby</code>:</p>
<pre><code>oceanRatings = matrix_user_ratings["Ocean's Eleven (2001)"].groupby('rating').count()
</code></pre>
<p>Or <code>value_counts()</code>:</p>
<pre><code>oceanRatings = matrix_user_ratings["Ocean's Eleven (2001)"].value_counts()
</code></pre> | python|pandas|dataframe | 0 |
354,372 | 68,857,527 | Object Detection with TensorFlow 2 : ImportError: cannot import name 'anchor_generator_pb2' from 'object_detection.protos' | <p>I am trying to train a model using Tensorflow 2 as written here:
<a href="https://colab.research.google.com/drive/1sLqFKVV94wm-lglFq_0kGo2ciM0kecWD#scrollTo=fF8ysCfYKgTP&uniqifier=1" rel="nofollow noreferrer">https://colab.research.google.com/drive/1sLqFKVV94wm-lglFq_0kGo2ciM0kecWD#scrollTo=fF8ysCfYKgTP&uniq... | <p>I solved the problem. It turned out that python was trying to import files from a different directory. I moved the project to this folder and it worked.</p>
<p>I spent 2 days solving this problem.
If anyone has the same problem, take a close look at where the import fails.</p> | python|tensorflow | 0 |
354,373 | 68,643,845 | Pandas conditional formatting: Highlighting cells in one frame that are unequal to those in another | <p>Given two pandas dataframes df1 and df2 that have exact same schema (i.e. same index and columns, and hence equal size), I want to color just those cells in df1 that are unequal to their counterpart in df2. Any hints?</p>
<p>More generally, if I have a predefined matrix of colors, colormat, that has the same dimensi... | <p>The styles need to be valid CSS, so change 'red' and 'green' to <code>'background-color: red'</code> and <code>'background-color: green'</code>, then simply <a href="https://pandas.pydata.org/docs/reference/api/pandas.io.formats.style.Styler.apply.html" rel="nofollow noreferrer"><code>apply</code></a> on <code>axis=... | python|pandas|pandas-styles|python-applymap | 0 |
354,374 | 68,532,200 | Creating a Pandas DataFrame from a NumPy masked array? | <p>I am trying to create a Pandas <code>DataFrame</code> from a NumPy masked array, which I understand is a supported operation. This is an example of the source array:</p>
<pre class="lang-py prettyprint-override"><code>a = ma.array([(1, 2.2), (42, 5.5)],
dtype=[('a',int),('b',float)],
mask=[... | <p>If the array has a simple dtype, the dataframe creation works (as documented):</p>
<pre><code>In [320]: a = np.ma.array([(1, 2.2), (42, 5.5)],
...: mask=[(True,False),(False,True)])
In [321]: a
Out[321]:
masked_array(
data=[[--, 2.2],
[42.0, --]],
mask=[[ True, False],
[False, True]],
... | pandas|numpy|missing-data | 3 |
354,375 | 68,848,067 | Fuzzy matching only for values within same group | <p>I am stuck with this problem that should have a simple solution but I cannot find it.</p>
<p>I have two data frames:</p>
<p>dfA</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th>Company</th>
<th>Country Code</th>
</tr>
</thead>
<tbody>
<tr>
<td>CompanyA</td>
<td>IT</td>
</tr>
<tr>
<td>Comp... | <p>Build a a restrictive list where to apply the process. Use <code>extractOne(...)</code> instead of <code>extract(...)[0]</code> to get only one value.</p>
<pre><code>dfA['Company List'] = pd.merge(dfA, dfB, on='Country Code', how='left') \
.groupby('Company_x')['Company_y'] \
... | python|pandas|fuzzywuzzy | 0 |
354,376 | 68,503,155 | Shortest path from A to B in a weighted and directed 2D pandas csv graph | <p>I have a weighted graph represented by 2D n x n matrix that I created using pandas and saved as a csv file</p>
<p>The indices and column headers are numbers that represent the nodes. the edges are weights connecting two nodes</p>
<p>for example: {1232: {1232: inf, 2342: 12, 45654: inf, 45678: 21}} and so on</p>
<p>I... | <p>Making a small example using your data + an extra edge.</p>
<pre><code>import pandas as pd
import networkx as nx
so = pd.DataFrame({
"source": [1232, 1232 , 1232, 2345 ],
"target": [2342, 45678, 2345, 45678],
"weight": [12 , 21 , 1, 1]
})
G = nx.from_pandas_edge... | python|pandas|graph|dijkstra | 1 |
354,377 | 68,782,144 | PyTorch: Can I group batches by length? | <p>I am working on an ASR project, where I use a model from HuggingFace (<code>wav2vec2</code>). My goal for now is to move the training process to PyTorch, so I am trying to recreate everything that HuggingFace’s <code>Trainer()</code> class offers.</p>
<p>One of these utilities is the ability to group batches by leng... | <p>One possible way of going about this is by using a <em>batch sampler</em> and implementing a <code>collate_fn</code> for your dataloader that will perform the dynamic padding on your batch elements.</p>
<p>Take this basic dataset:</p>
<pre><code>class DS(Dataset):
def __init__(self, files):
super().__ini... | pytorch|pytorch-dataloader|huggingface-datasets | 2 |
354,378 | 68,509,678 | How to speed up iteration? | <p>I got this code, and I want to iterate over a csv file with ~100000 columns.</p>
<p>This script do run very slowly to iterate over that number of columns.</p>
<p>Do any of you have a possible solution to speed up my code?</p>
<pre><code>import pandas as pd
import matplotlib.pyplot as plt
import math
a=pd.read_... | <p><strong>Edit</strong></p>
<p>The OP is having trouble reading the CSV file, presumably because of the 2-row header and the slightly unusual separator (and the decimal comma).</p>
<p>Here is a way to read such a file:</p>
<pre class="lang-py prettyprint-override"><code>a = pd.read_csv(io.StringIO(txt), sep=';', decim... | python|pandas | 2 |
354,379 | 68,806,265 | HuggingFace Trainer logging train data | <p>I'm following this tutorial to train some models:</p>
<p><a href="https://huggingface.co/transformers/training.html" rel="nofollow noreferrer">https://huggingface.co/transformers/training.html</a></p>
<p>I'd like to track not only the evaluation loss and accuracy but also the train loss and accuracy, to monitor over... | <p>You can use the methods <code>log_metrics</code> to format your logs and <code>save_metrics</code> to save them. Here is the code:</p>
<pre><code># rest of the training args
# ...
training_args.logging_dir = 'logs' # or any dir you want to save logs
# training
train_result = trainer.train()
# compute train result... | pytorch|huggingface-transformers | 3 |
354,380 | 68,810,795 | Filter rows in DataFrame where certain conditions are met? | <p>I have a DataFrame with relevant stock information that looks like this.</p>
<p><a href="https://i.stack.imgur.com/A8aKr.png" rel="nofollow noreferrer">Screenshot of my dataframe</a></p>
<p>I need it so that if the 'close' from one row is different from the 'open' in the next row a new dataframe will be created stor... | <p>This can be accomplished using <code>Series.shift</code></p>
<pre class="lang-py prettyprint-override"><code>>>> df['close'] != df['open'].shift(-1)
0 2020-01-01 False
1 2020-01-01 False
2 2020-01-01 True
3 2020-01-02 True
4 2020-01-02 True
5 2020-01-02 False
6 2020-01-03 Tru... | python|pandas | 0 |
354,381 | 68,583,741 | How to find out error percentage in pandas dataframe? | <p>I have sample work history data data where history of pieces of work moving through the system are recorded. To do so, I selected rows based on error status which is end with '1'. Now, I tried to find error percentage from it but the output doesn't make sense to me.</p>
<p>Essentially, what I want to do is, I want ... | <p>Ok. if I get your explanation right all <code>status</code> ending with 1 are errors. So, here is a way to do this. Maybe not the most beautiful, but it does the trick.</p>
<p>Step 1 is to create a column containing the last digit of the <code>status</code> number:</p>
<pre><code>df['error'] = df['status'].astype(st... | python|pandas | 2 |
354,382 | 68,729,349 | getting mean() used in groupby to use the right grouped values for calculation | <p>Data import from csv:</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th>Date</th>
<th>Item_1</th>
<th>Item 2</th>
</tr>
</thead>
<tbody>
<tr>
<td>1990-01-01</td>
<td>34</td>
<td>78</td>
</tr>
<tr>
<td>1990-01-02</td>
<td>42</td>
<td>19</td>
</tr>
<tr>
<td>.</td>
<td>.</td>
<td>.</td>
</tr... | <pre class="lang-py prettyprint-override"><code>import pandas as pd
df = pd.read_csv( 'test.csv', index_col = 'date' )
df.index = pd.to_datetime( df.index )
df.groupby([(df.index.year),(df.index.month)]).mean()
</code></pre>
<p>Seems to do the trick from the provided data.</p> | python|pandas | 1 |
354,383 | 68,616,366 | Recognizing Date inside of a string type in a Pandas DataFrame | <p>I am attempting to recognize dates contained inside strings inside of a pandas dataframe. Inside of the csv file, the dates appear as: <code>'7\23\2019'</code>. However, when I printed the actual information to see what was contained inside of the cell, the following appeared: <code>7, 23, 2019</code>.</p>
<p>My ori... | <p>use <code>concat()</code>+<code>to_datetime()</code>+<code>dropna()</code>+list comprehension:</p>
<pre><code>output=pd.concat([pd.to_datetime(df[x],errors='coerce',format='%m/%d/%Y') for x in df.select_dtypes('O')],axis=1).dropna(axis=1,how='all')
</code></pre>
<p><strong>OR</strong></p>
<p>use <code>select_dtypes(... | python|python-3.x|pandas|dataframe | 0 |
354,384 | 68,566,562 | How to go through the array halfway and then continue from the end of the array to the half array? | <p>I'm trying to loop through the array, so that we take the first two values from the array at once, then the following iterations will take one value at a time until half of the array. When it comes to the middle of the field, it starts the same way, but from the end of the field towards the beginning. It takes first... | <p>You can see the problem like that</p>
<ul>
<li><p>each group of values is printed the same way</p>
</li>
<li><p>you apply to the first half and to the second half in reverse</p>
</li>
</ul>
<pre><code>def print_half(half_values):
print(*half_values[:2])
print(*half_values[2:], sep="\n")
def prin... | python|numpy|for-loop | 0 |
354,385 | 68,867,081 | Convert an array of objects into an array of arrays with Python | <p>I have a large .csv database with a column name VELOCITY containing 3D velocity vectors.</p>
<p>Each element of the VELOCITY column has the form: '(v1, v2, v3)'</p>
<p>To read the data I used:</p>
<pre><code>df = pd.read_csv('database.csv')
df = pd.DataFrame(df)
</code></pre>
<p>Now, I tried to define a velocity_arr... | <p>Seen from your sample data that each entry in the <code>velocity_array</code> has 2 single quotes enclosing the entry e.g. <code>'(a1, a2, a3)'</code>. Therefore, suppose your entries are actually string entries.</p>
<p>If this is true, you can transform each string in the column to a list by:</p>
<pre><code>df['VE... | python|arrays|pandas|numpy | 3 |
354,386 | 68,825,672 | Random Sparse Matrix in Python | <p>I want to create a random sparse matrix in python, where the non - zero elements are between 1 and 7 and the diagonal elements are zero. Also no row or column should have all elements zero. The % of zero elements also would be chosen randomly. Also, if i,j is non-zero, then j,i should be 0.</p>
<p>I have the followi... | <p>a possible algorithm would be to pick coordinates from a list at ramdom and if the xy and yx are both available and x != y then set a value in that coordinate. continue until you have enough percentage fill.</p>
<p>afterwards check that all rows have at-least one non-zero.</p> | python|matrix|graph|pytorch | 0 |
354,387 | 68,633,825 | how to convert a set of matrices into a data frame in pandas? | <p>I have four-time point matrices, A0, A1, A2, and A3, which are m*n matrices. I would like to make a data frame in pandas that involves these matrices and whenever I call them I can access them easily. Is that possible?</p>
<p>For example <code>A0=np.array([1,2,3],[3,4,5])</code>, <code>A1=np.array([0,2,0],[3,4,0])... | <p>Well numpy and pandas are compatible in many builtin functions.</p>
<pre class="lang-py prettyprint-override"><code>import numpy as np
numpy_data = np.array([[1, 2], [3, 4]])
df = pd.DataFrame(data=numpy_data)
# If you know your column names
column_names = ['first_col', 'second_col']
df = pd.DataFrame(data=numpy_da... | python|pandas|dataframe|matrix | 0 |
354,388 | 68,544,019 | Compare values between 2 dataframes and transform data | <p>The main aim of this script is to compare the regex format of the data present in the csv with the official ZIP Code regex format for that country, and if the format does not match, the script would carry out transformations on said data and output it all in one final dataframe.</p>
<p>I have 2 csv files, one (count... | <p>Based on your response to my comment, I would suggest to directly fix the zip code using your regexes:</p>
<pre><code>df3 = df2.set_index('Country')
df1['corrected_Zip'] = (df1.groupby('Country')
['Zip Code']
.apply(lambda x: x.str.extract('(%s)' % df3.loc[x.nam... | python|regex|pandas|csv|formatting | 1 |
354,389 | 36,669,219 | Fastest way to find maximum value of a list in specific intervals | <p>I have a list that contains 1024 elements. I want to check the maximum value of the list between an interval that i determined.</p>
<p>for example X is my list that is in numpy array form. Then;</p>
<pre><code>if np.amax(X[0:31]) > 200:
print("1")
elif np.amax(X[0:31]) < 200:
print("1a")
if np.amax(... | <p>Well, for such regular intervals, a standard for-loop should do. For starters, this will work:</p>
<pre><code>for x in xrange(0, 1024, 32): # 0, 32, 64, ... , 992
m = np.amax(X[x:x+32])
if m > 200:
print(str(x/32 + 1)) # 1, 2, 3, ... , 32 (not 16)
elif m < 200:
print(str(x/32 + 1... | python|algorithm|performance|python-2.7|numpy | 1 |
354,390 | 36,526,282 | Append multiple pandas data frames at once | <p>I am trying to find some way of appending multiple pandas data frames at once rather than appending them one by one using </p>
<pre><code>df.append(df)
</code></pre>
<p>Let us say there are 5 pandas data frames <code>t1</code>, <code>t2</code>, <code>t3</code>, <code>t4</code>, <code>t5</code>. How do I append the... | <p>I think you can use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.concat.html" rel="noreferrer"><code>concat</code></a>:</p>
<pre><code>print pd.concat([t1, t2, t3, t4, t5])
</code></pre>
<p>Maybe you can <code>ignore_index</code>:</p>
<pre><code>print pd.concat([t1, t2, t3, t4, t5], ignor... | python|pandas|append | 112 |
354,391 | 36,635,238 | Specifying default dtype for np.array(1.) | <p>Is there a way to specify default dtype that's used with constructs like <code>np.array(1.)</code>?</p>
<p>In particular I want <code>np.array(1.)</code> to be <code>np.float32</code> and <code>np.array(1)</code> to be <code>np.int32</code>. Instead I'm getting <code>np.float64</code> and <code>np.int64</code></p> | <p>The default depends on your system. On a 64-bit system, default types will be 64-bit. On a 32-bit system, default types will be 32-bit. There is no way to change the default short of re-compiling numpy with a different system C header.</p>
<p>You can of course specify dtypes explicitly, e.g.</p>
<pre><code>>>... | python|numpy | 9 |
354,392 | 36,588,171 | pandas - perform string operation on all elements of a column | <p>I have a column in a pandas dataframe that is all capitals. I would like to change this to words with only the first letter capitalized.</p>
<p>I have tried the following:</p>
<pre><code>import pandas as pd
data = pd.read_csv('my_file.csv')
data['field'] = data['field'].title()
</code></pre>
<p>This returns the ... | <p>Found the answer here:</p>
<p><a href="http://pandas.pydata.org/pandas-docs/stable/text.html" rel="noreferrer">http://pandas.pydata.org/pandas-docs/stable/text.html</a></p>
<pre><code>data['field'] = data['field'].str.title()
</code></pre> | python|string|pandas | 10 |
354,393 | 36,462,100 | Creating new column in pandas dataframe with a list of values from another column without using "groupby" | <p>I work with large datasets, making pandas group and groupby functions take a long time/use too much memory. I have heard some people say groupby can be slow, but am having trouble finding a better solution. </p>
<p>If my dataframe has 2 columns similar to:</p>
<pre><code>df = pd.DataFrame({'a':[1,2,2,4], 'b':[1,1,... | <p>On a 4K row df I get the following:</p>
<pre><code>In [29]:
df_group = df.groupby('a')
%timeit df.apply(lambda row: df_group.get_group(row['a'])['b'].tolist(), axis=1)
%timeit df['a'].map(df.groupby('a')['b'].apply(list))
1 loops, best of 3: 4.37 s per loop
100 loops, best of 3: 4.21 ms per loop
</code></pre> | python|pandas | 0 |
354,394 | 36,401,596 | Convert data type object DD-Mon-YYYY to data format in Python | <p>I've loaded a csv using pd.read_csv in the following format - </p>
<pre><code>obj = pd.read_csv('usd_brl_date.csv', sep=';', usecols=[1,2,3,4,5,6])
In [34]: obj
Out [34]:
Date Price Open High Low Change %
0 18/Mar/2016 3.6128 3.6241 3.6731 3.6051 -0.31%
1 17/Mar/2016 3.6241 ... | <p>You can use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html" rel="nofollow noreferrer"><code>to_datetime</code></a>:</p>
<pre><code>df['Date'] = pd.to_datetime(df['Date'], format="%d/%b/%Y")
print df
Date Price Open High Low Change %
0 2016-03-18 3.6128 ... | python|csv|pandas|strftime|strptime | 0 |
354,395 | 36,676,150 | Getting very high values in linear regression | <p>I am trying to make a simple MLP to predict values of a pixel of an image - <a href="http://evolvingstuff.blogspot.in/2012/12/generating-mona-lisa-pixel-by-pixel.html" rel="nofollow">original blog</a> .
Here's my earlier attempt using Keras in python - <a href="https://github.com/goelakash/MonaNet/blob/master/MonaNe... | <p>First of all, I think that instead of running the train_op and then the MSE
you can run both ops in a list and reduce your computational cost significantly. </p>
<pre><code>for i in range(nb_epochs):
cost += sess.run([MSE, train_op], feed_dict ={x: X_train, y: Y_train})
</code></pre>
<p>Secondly, I suggest always ... | python|tensorflow|linear-regression | 1 |
354,396 | 36,494,926 | Combine two dataframe boxplots in a twinx figure | <p>I want to display two Pandas dataframes within one figure as boxplots.
As each of the two dataframes has different value range, I would like to have them combined in a twinx figure.</p>
<p>Reduced to the minimum, I have tried the following:</p>
<pre><code>import pandas as pd
import numpy as np
import matplotlib.py... | <p>The best solution is to concatenate the data frames for plotting and to use a mask. In the creation of the mask, we use the <code>dfs == dfs | dfs.isnull()</code> to create a full matrix with <code>True</code> and then we query on all column names that are not <code>'E'</code> or <code>'F'</code>. This gives a 2D ma... | pandas|matplotlib|axis|boxplot | 3 |
354,397 | 36,672,618 | How to create a recurrent neural network in tensor flow for variable sequence length? | <p>I am trying the create a recurrent neural network in tensor flow. The input to the network is a sequence of vectors. The sequence length is different for all the inputs. I want to do this with batch of inputs.</p>
<p>Can anyone help me on how exactly to do this? I have gone through the tutorials on the tensorflow s... | <p>You can use the rnn function defined <a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/rnn.py#L35" rel="nofollow">here</a></p>
<p>One of the arguments it takes is sequence_length</p>
<blockquote>
<p>sequence_length: Specifies the length of each sequence in inputs.
An int... | tensorflow|recurrent-neural-network | 2 |
354,398 | 36,569,827 | Read txt data separated by empty lines as several numpy arrays | <p>I have some data in a txt file as follows:</p>
<pre><code># Contour 0, label: 37
41.6 7.5
41.5 7.4
41.5 7.3
41.4 7.2
# Contour 1, label:
48.3 2.9
48.4 3.0
48.6 3.1
# Contour 2, label:
61.4 2.9
61.3 3.0
....
</code></pre>
<p>So every block begins with a comment and ends with a bl... | <p>Like this?</p>
<pre><code>import numpy as np
text = \
'''
# Contour 0, label: 37
41.6 7.5
41.5 7.4
41.5 7.3
41.4 7.2
# Contour 1, label:
48.3 2.9
48.4 3.0
48.6 3.1
# Contour 2, label:
61.4 2.9
61.3 3.0
'''
for line in text.split('\n'):
if line != '' and not line.startswith('... | python|arrays|numpy | 3 |
354,399 | 36,255,918 | SummaryWriter not writing summaries to file | <p>I'm trying to use tensorflow's SummaryWriter, however it does not seem to write events, images, or histograms to file. However it does write the graph to file, (which I can then see in tensorboard), indicating at least, tensorboard and SummaryWriter know where my logdir is. </p>
<p>Here is my (simplified) code, bro... | <p>I know this is an old post, but I was experiencing the same thing in a virtual environment running TensorFlow 1.1.0. Running version 1.2.1 I don't seem to have this problem. You can execute the following at the command line to determine which version of TensorFlow you're running:</p>
<pre><code>python -c "import ... | tensorflow|tensorboard | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.