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 |
|---|---|---|---|---|---|---|
369,200 | 41,388,235 | What do I put in the "compute capability list" field when configuring Tensorflow? | <p>I'm building Tensorflow from source, and it asks me to </p>
<pre><code>Please specify a list of comma-separated Cuda compute capabilities you want to build with.
You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
Please note that each additional compute capability signifi... | <p>Summarizing the comments as an answer:</p>
<p>You can put 5.0 there, which is the compute capability that your card supports and should be your best choice. 3.5 is too low (doesn't use the full features of your card) and 5.2 is too high (not supported by your card); in either case, I believe the binary code would b... | build|configuration|tensorflow | 3 |
369,201 | 27,615,838 | What does the savethread / restore thread do? | <p>This is a follow up from this question:</p>
<p><a href="https://stackoverflow.com/questions/27510838/when-does-a-numba-function-compile">When does a numba function compile?</a></p>
<p>I am confused as to what this part of the code does:</p>
<pre><code>from ctypes import pythonapi, c_void_p
savethread = pythonapi.... | <p>That code is used for calling a C function, in this case the functions are <code>PyEval_SaveThread</code> and <code>PyEval_RestoreThread</code>.</p>
<p><code>savethread = pythonapi.PyEval_SaveThread</code> keeps a reference to the function <code>pythonapi.PyEval_SaveThread</code> in the variable <code>savethread</c... | python|multithreading|numpy|numba | 2 |
369,202 | 27,825,461 | Formatting JSON from a Pandas data frame for Highcharts within a Django template | <p>I have a Pandas data frame that I have converted to JSON like so:</p>
<pre><code>json_data = data_df.to_json()
</code></pre>
<p>The original data frame looks like something similar to this:</p>
<pre><code> col1 col2 col3 col4
0 1 2 2 -1
1 2 4 3 -2
2 3 6 4 -3
3 4... | <p>Try</p>
<pre><code>var myData = {{ json_data | safe }};
</code></pre>
<p><strong>UPDATE</strong>:</p>
<p>Your data should be in format:</p>
<pre><code>json_data = [
{
'name': "col1",
'data': [1, 2, 3, 4, 5]},
{
'name': "col2",
'data': [2, 4, 6, 8, 10]},
]
</code></pre>
<p... | python|json|django|pandas|highcharts | 3 |
369,203 | 27,806,577 | Tricky Python array sorting | <p>Currently, I'm loading in some data into memory of the form:</p>
<pre><code>5.579158e-19 0 0
5.678307e-19 1 0
...
6.041513e-19 27 0
5.938317e-19 28 0
...
5.978803e-19 38 1
5.590008e-19 39 1
5.588807e-19 0 2
5.670948e-19 1 2
...
</code></pre>
<p>and so on with the command:</p>
... | <p>This can be done with no explicit loops. I'll use a smaller data set, and create a 10x10 array <code>mat</code>. If an index (i,j) is not in the CSV file, <code>mat[i,j]</code> will be 0.</p>
<p>Here's the input file:</p>
<pre><code>In [27]: !cat data.csv
0.1 0 0
0.2 1 0
0.3 7 0
0.4 8 0
0.5 ... | python|arrays|sorting|numpy | 4 |
369,204 | 27,757,732 | Find uncertainty from polyfit | <p>I use simple <code>polyfit</code> of order 2 to fit a line in sample data:</p>
<pre><code>np.polyfit(x, y, 2)
</code></pre>
<p>which returns the coefficients.</p>
<p>Now I want to find uncertainty of the fitted line, and tried to use <code>cov</code> argument, which returns 3x3 covariance matrix:</p>
<pre><code>... | <p>This problem is addressed by <a href="http://ipnpr.jpl.nasa.gov/progress_report/42-122/122E.pdf" rel="noreferrer">"Estimating Errors in Least-Squares Fitting"</a> by P.H. Richter, 1995, TDA Progress Report 42-122.</p>
<p>From the report, this paragraph may already be sufficient to you</p>
<blockquote>
<p>The fir... | python|numpy | 12 |
369,205 | 27,787,930 | How to get number of groups in a groupby object in pandas? | <p>This would be useful so I know how many unique groups I have to perform calculations on. Thank you.</p>
<p>Suppose groupby object is called <code>dfgroup</code>. </p> | <h1>[pandas >= 0.23] Simple, Fast, and Pandaic: <code>ngroups</code></h1>
<p>Newer versions of the groupby API provide this (undocumented) attribute which stores the number of groups in a GroupBy object.</p>
<pre><code># setup
df = pd.DataFrame({'A': list('aabbcccd')})
dfg = df.groupby('A')
</code></pre>
<p></p>
<p... | python|pandas|dataframe|group-by|pandas-groupby | 96 |
369,206 | 27,769,014 | Python function to expand image (NumPy array) | <p>Say I have a greyscale image that is <code>3x3</code> and is represented by the <code>numpy</code> array below.</p>
<p>I want to increase the size and resolution of the image, similar to a resizing function in a normal picture editing software, but I don't want it to change any of the values of the pixels, just to ... | <p>You could use <a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.repeat.html" rel="nofollow"><code>np.repeat</code></a> along both axes of the 3x3 <code>img</code> array:</p>
<pre><code>>>> img.repeat(2, axis=0).repeat(2, axis=1)
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
... | python|arrays|numpy|image-processing | 6 |
369,207 | 27,517,805 | Matrix with results of function f(x,y) in Python/numpy/pandas | <p>I have two series X and Y a function f(x,y) in Python. I would like to generate a matrix which contains the output of the function for every combination of X and Y. For instance, if the function was just a simple multiplication, it would look like this:</p>
<pre><code> 1 2 3 4 5
-------------------
1... | <p>The idiom is this: write function f(x,y) so that it can operate elementwise on Numpy arrays.
For example, if you want to calculate <code>f(x,y) = x**2 + y**2</code>, this is</p>
<pre><code>def f(x, y):
return x**2 + y**2
</code></pre>
<p>since power and sum operate elementwise so the expression is OK as it is.... | python|numpy|matrix|pandas | 1 |
369,208 | 27,842,229 | Problems with numpy divide | <p>I am trying to use numpy divide to perform division on arrays and I have two arrays and I call it as follows:</p>
<pre><code>log_norm_images = np.divide(diff_images, b_0)
</code></pre>
<p>I get the error:</p>
<pre><code>operands could not be broadcast together with shapes (96,96,55,64) (96,96,55).
</code></pre>
... | <p>You are attempting to broadcast a 4-D array together with a 3-D array. Based on NumPy's broadcasting behavior, this will only succeed if for each corresponding dimension, the dimensions are either equal or one of them is 1. Here's why it mismatches:</p>
<pre><code>Your 4-D array: 96 x 96 x 55 x 64
Your 3-D array... | python|arrays|numpy | 4 |
369,209 | 61,463,002 | Product of two string arrays | <p>I have an array :</p>
<pre><code>a1=['a','b','c']
</code></pre>
<p>and another :</p>
<pre><code>a2=['d','e','f']
</code></pre>
<p>How do I create a DataFrame containing all elements in a2 for each element in a1 in Python?</p>
<p>Expected output:</p>
<pre><code>a d
a e
a f
b d
b e
b f
c d
c e
c f
</code></pre> | <p>You can use <code>product</code> from <code>itertools</code></p>
<pre><code>In [1]: from itertools import product
In [2]: a1=['a','b','c']
In [3]: a2=['d','e','f']
In [4]: list(product(a1, a2))
Out[4]:
[('a', 'd'),
('a', 'e'),
('a', 'f'),
('b', 'd'),
('b', 'e'),
('b', 'f'),
('c', 'd'),
('c', 'e'),
('c', ... | python|pandas|numpy | 3 |
369,210 | 61,310,371 | Visual Studio getting confused about Python versions | <p>I am calling a simple Python script from a C# file as</p>
<pre><code> //ADD results and errors e.g., code Run GP tool.sln
#region Running Python scripts Commented
ProcessStartInfo psi = new ProcessStartInfo();
//Script variables, paths etc.
psi.FileName = @"C:\Users\oguz\AppD... | <p>I removed the other Python versions and kept the one I need then it worked fine. I think those Python versions came with Visual Studio. That was the only way I was able to work it out. Hopefully, someone will give better answer. </p> | python|visual-studio|numpy|environment-variables | 0 |
369,211 | 61,603,747 | Whitespaces after addition to numpy array | <p>Why when I'm executing code below I get those weird whitespaces in output?</p>
<pre><code>import numpy as np
str = 'a a b c a a d a g a'
string_array = np.array(str.split(" "))
char_indices = np.where(string_array == 'a')
array = char_indices[0]
print(array)
array += 2
print(array)
</code></pre>
<p>output:</p>
<... | <p>That's just numpy's way of displaying data to make it appear aligned and more readable.</p>
<p>The alignment between your two lists changes</p>
<pre><code>[0 1 4 5 7 9]
[ 2 3 6 7 9 11]
</code></pre>
<p>because there is a two-digit element in the second list.</p>
<p>In vectors it is more difficult to apprecia... | python|numpy | 2 |
369,212 | 61,242,211 | tensor flow lite multiple camera compatibility | <p>I would like to know if tensor flow lite on the raspberry pi 4
i could'nt find any information on the tensor flow website
is compatible with multiples cameras for finding my cat in my house with multiple cameras</p> | <p>The challenge here is not in TensorFlow but in the interfacing the cameras. You can run inference with a batch size of 2, or 4, or however many cameras you have in your house to always keep an eye on your kitty </p> | tensorflow-lite | 0 |
369,213 | 61,593,321 | If both values in two different columns are NaN then 0 else 1 | <p>My dataframe looks lie this:</p>
<p>ts self_top_ask_price self_top_bid_price
0 2020-05-03 11:59:48.627436 NaN 0.08331
1 2020-05-03 11:59:36.286763 0.08367 0.08331
2 2020-05-03 11:59:24.279036 0.08367 NaN
3 2020-05-03 11:59:12.298755 NaN NaN</p>
<p>What I am trying to achieve is that if both columns a... | <p>Check both columns if not missing values with <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.any.html" rel="nofollow noreferrer"><code>DataFrame.any</code></a> and then convert to numbers <code>0,1</code> mapping boolean by <a href="http://pandas.pydata.org/pandas-docs/stable/ref... | python|pandas | 2 |
369,214 | 61,413,442 | Docker Error When Compiling Tensorflow from source on Raspberry Pi | <p>I am attempting to build tensorflow from source on Raspberry PI using docker following this tutorial: <a href="https://www.tensorflow.org/install/source_rpi" rel="nofollow noreferrer">https://www.tensorflow.org/install/source_rpi</a> </p>
<p>I have python 3.4 and 3.7 installed, and Docker version 18.06.3-ce. I get ... | <h2>Issue</h2>
<p>The guide you are following references <em>cross-compiling</em>, which means they are building on a host machine, while you are trying to build on the Pi.</p>
<p>To build Tensorflow go on raspberry pi you have to have <strong>libtensorflow.so</strong>, which is clibrary for Tensorflow used by GoLang... | python|python-3.x|docker|tensorflow|raspberry-pi | 1 |
369,215 | 61,285,064 | How to add an entry for all next dates once it first appeared | <p>I have a database that updates on a daily basis. Currently, it only has "active cases" - let's say when the value is 1 or higher. </p>
<pre class="lang-py prettyprint-override"><code>df = pd.DataFrame({
"Date": [
"2020-04-09", "2020-04-09",
"2020-04-10", "2020-04-10", "2020-04-10",
"2020... | <p>Idea is reshape by <code>pivot</code> and then replace missing values to <code>0</code> but only for rows if exist at least one non missing value before:</p>
<pre><code>df1 = df.pivot('Date','ID','Value')
df2 = (df1.mask(df1.ffill().notna() & df1.isna(), 0)
.stack()
.astype(int)
.r... | python|pandas | 2 |
369,216 | 61,335,078 | Colab throws errors when attempting to apply a SeparableConv2D layer in Tensorflow 2.0 | <p>While learning Tensorflow 2.0 and experimenting with various structures I came across SeparableConv2D. I attempted to re-create a simple stack of VGG blocks that used the separable layers instead of standard convolutional layers, but Colab throws an error every time I attempt to add additional separable convolutio... | <p>You are missing a bracket :</p>
<pre><code>model.add(layers.SeparableConv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3))
</code></pre>
<p>should be</p>
<pre><code>model.add(layers.SeparableConv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)) )
</code></pre>
<p>Note the <code>)</code> at the ... | deep-learning|tensorflow2.0|convolution|tf.keras | 0 |
369,217 | 61,253,542 | Add a new row to a CSV Dataframe inside a for loop with Pandas | <p>Hello I am really stuck and cannot get my head around this problem really appreciate any help or guidance. I have tried to ask this question a couple of times different ways but have had no full success on completing my task.</p>
<p>I am attempting to take a cell from each row in spreadsheet"a.csv" and then use tha... | <p>A possible way is to add the new row at the end of the dataframe and store the IDNumber in it. At the end of the loops, you can sort the dataframe on IDNumber and set it to blank on lines having no Title. Here is a possible code:</p>
<pre><code>for index, row in df1.iterrows():
for i, r in df2.iterrows():
... | python|pandas|csv|dataframe | 1 |
369,218 | 61,516,480 | Grab last file from folder Python | <p>This is an entire edit since I found what my issue is, but I am still not able to fix this issue.</p>
<p>I am trying to build a function that grabs the last file from a folder based on user input and turn it into a data frame. I have multiple folders assigned as constants. The questions I find are looking for the l... | <p><code>sort_values</code> works with DataFrame, you are using it with Series (this is the reason of the error: <code>sort_values() got an unexpected keyword argument 'by'</code>)</p>
<p>I suggest you modify inside <code>top_cases</code> function:</p>
<pre><code> if series == 'Country':
df = daily_framer(... | python|pandas|matplotlib | 0 |
369,219 | 61,264,212 | "undefined input shape at index" warning in training | <p>Tensorflow2 is used in training and I have quite a number of warnings printed out in object classification training.
What could be the reason for those warnings?</p>
<pre><code>2020-04-17 12:15:16.091784: W tensorflow/core/common_runtime/shape_refiner.cc:88] Function instantiation has undefined input shape at index... | <p>I had similar errors popping up. I used a dataset from a generator without specifing the output shape of the generator. After adding the output shape, no warning was generated:</p>
<pre><code>tf.data.Dataset.from_generator(generator, output_types=(tf.float32, tf.float32), output_shapes=(tf.TensorShape([2997, 16]), t... | tensorflow2.0 | 0 |
369,220 | 61,181,237 | Tensorflow: FailedPreconditionError: Error while reading resource variable from Container: localhost. When running sess.run() on custom loss function | <p>I have a code running Keras with TensorFlow 1. The code modifies the loss function in order to do deep reinforcement learning:</p>
<pre><code>import os
import gym
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
env = gym.make("CartPole-v0").env
env.reset()
n_actions = env.action_space.n
state... | <p>The initialization operation should be fetched and run (only one time) <strong>after</strong> the variables (i.e. model) have been created or the computation graph has been defined. Therefore, they should be put right before running the training step:</p>
<pre><code># Define and create the computation graph/model
#... | tensorflow|machine-learning|keras|deep-learning|tf.keras | 6 |
369,221 | 61,334,586 | How to export a DataFrame to multiple sheets of Excel File | <p>Assume that I have a dataframe of 8000 rows x 7 columns.</p>
<p>Overview of the data</p>
<pre><code>data = pd.read_excel('tmp.xlsx')
data.head(10)
</code></pre>
<pre><code>ID Type CatID Val1 val2 Comment Disposition
20192658 N 52 256 358 Processing In Progress
201... | <h2>Given your dataframe <code>data</code>:</h2>
<ul>
<li>Iterates through each unique ID and slices the dataframe for those values, and then saves to the file.</li>
</ul>
<pre class="lang-py prettyprint-override"><code>import pandas as pd
with pd.ExcelWriter('data.xlsx') as writer:
for i, value in enumerate(dat... | python|excel|pandas|dataframe | 2 |
369,222 | 61,488,097 | How to groupby a column every time its sum reaches a specified amount? | <p>I have a data frame <code>df</code> like this</p>
<pre><code> x
0 8.86
1 1.12
2 0.56
3 5.99
4 3.08
5 4.15
</code></pre>
<p>I need to perform some sort of <code>groupby</code> operation on <code>x</code> to aggregate <code>x</code> every time its sum reaches 10. If the index of <code>df</code>... | <p>Here's one approach:</p>
<pre><code># cumulative sum and modulo 10
s = df.x.cumsum().mod(10)
# if value lower than 10, we've reached the value
m = s.diff().lt(0)
# groupby de cumsum
df.x.groupby(m.cumsum().shift(fill_value=0)).sum()
x
0 10.54
1 13.22
Name: x, dtype: float64
</code></pre> | python|pandas|pandas-groupby | 2 |
369,223 | 61,331,898 | Tensorflow tflite c++ api inference for matrix data array | <p>I am creating a class that will be used to run inference on an embedded device (not raspberry pi) in c++ using tensorflow's tflite c++ api. Tensorflow doesn't seem to have decent documentation on how to run inference for n number of samples of image data. My data shape in python is (n, 5, 40, 1) [n samples, 5 heig... | <p>From the Tensorflow documentation we can find below details,</p>
<blockquote>
<p>It should be noted that:</p>
<ul>
<li>Tensors are represented by integers, in order to avoid string comparisons (and any fixed dependency on string libraries).</li>
<li>An interpreter must not be accessed from concurrent thr... | c++|tensorflow|tensorflow-lite | -1 |
369,224 | 61,561,537 | Encoding, Decoding Pinyin Characters in Python 3.7 | <p>I'm having trouble en-/decoding Pinyin characters from an excel file.</p>
<p>The structure of that file looks like this:</p>
<p><a href="https://i.stack.imgur.com/NAVA0.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/NAVA0.png" alt="enter image description here"></a></p>
<p>[...]</p>
<p>and I ... | <p>Finally figured out the problem.
I was running it through VS 'Code Runner' extension which caused the error. I guess it is possible to configure it somehow to make it work, but for the moment I just use standard terminal within VS Code or run it directly through CLI.</p> | python-3.x|pandas|encoding|utf-8|decoding | 0 |
369,225 | 61,425,578 | How to reshape or transpose a dataset by considering selected columns and rows in a large dataset (World Bank example) | <p>I am trying to wrangle a dataset from the World Bank website and I need to reshape in a way for the series name to be the first row and the years to be all structured along a column. There are 50 years and over 100 indicators in the dataset so this reshape needs some form of automatisation to work for me. An extract... | <p>Use <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.set_index.html" rel="nofollow noreferrer"><code>DataFrame.set_index</code></a> with reshape by <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.stack.html" rel="nofollow noreferrer"><code>DataFr... | python|pandas|reshape|transpose|data-wrangling | 1 |
369,226 | 61,248,942 | what does the numpy vander function do and why use it in a regression? | <p>I am trying to under the code written in this <a href="https://searchcode.com/codesearch/view/88477497/" rel="nofollow noreferrer">module</a> to understand cointegration.</p>
<p>However when I step through the code I am confused by the last line in the section of code below. I have read the documentation of the van... | <p>The function np.vander returns an array where the first column is x^(N-1), the second x^(N-2) and so forth. Look this example:</p>
<pre><code>x = np.array([1, 2, 3, 5])
N = 3
np.vander(x, N)
array([[ 1, 1, 1],
[ 4, 2, 1],
[ 9, 3, 1],
[25, 5, 1]])
</code></pre>
<p>In your code N= order+1 and X=np... | python-3.x|numpy|regression | 1 |
369,227 | 61,521,464 | pytorch F.cross_entropy does not apply gradient to weights | <p>I'm trying to train an MLP from scratch using <code>torch</code> tensors and some of the built-in loss functions. I have IRIS-data downloaded and stored in tensor <code>(100, 4)</code> and labels <code>(100)</code> (integers 0-2) in <code>data_tr</code> and <code>targets_tr</code>. I have enabled gradients on the i... | <p>If you want to update the weights without using an optimizer, you have to either use <code>torch.no_grad()</code> or update their <code>data</code> directly to ensure autograd is not tracking the update operations. </p>
<pre class="lang-py prettyprint-override"><code>with torch.no_grad():
W1 -= lr * W1.grad
... | pytorch|gradient | 0 |
369,228 | 61,549,822 | Extracting all Nouns from a CSV File Using NLTK | <p>I am new to both Python and NLTK. I would like to ask how can I extract all nouns from a list of sentences in CSV file using nltk? </p>
<p>the list of sentences is in CSV file and is in this form of: </p>
<pre><code> **Sentences**
</code></pre>
<p>1 I like to eat bread<br>
2 I am excited to watch this ... | <p>Well, you can use pandas to change the CSV file to the DataFrame.(<code>pd.read_csv('filename')</code>)
Next thing is you have to play with NLTK. Here is the link to the <a href="https://www.nltk.org/book/ch07.html" rel="nofollow noreferrer">NLTK</a>.</p> | python-3.x|pandas|nltk | 0 |
369,229 | 61,603,619 | LSTM Keras sorting out the X and y input dimensions | <p>I am trying to build an LSTM and am confused about the best way to shape my data.</p>
<p>I have a dataframe that looks like this:</p>
<p><div class="snippet" data-lang="js" data-hide="false" data-console="true" data-babel="false">
<div class="snippet-code">
<pre class="snippet-code-html lang-html prettyprint-overr... | <p>if MULTIPLE timesteps are required you have to create a sliding window function which helps you to reshape your data, for this purpose <code>TimeSeriesGenerator</code> from Keras is a good instrument (<a href="https://machinelearningmastery.com/how-to-use-the-timeseriesgenerator-for-time-series-forecasting-in-keras/... | python|tensorflow|keras|deep-learning|lstm | 1 |
369,230 | 61,470,832 | pickle.load cannot open up a (Stylegan2 network) pickle model on my local machine, but can on the cloud | <p>Stylegan2 uses network pickle files to store ML models. I transfer trained one model, which I am able to open up on cloud servers. I have been generating images from this model fine with the following setup: </p>
<ul>
<li>Google Colab: Python 3.6.9, CUDA 10.1, tensorflow-gpu 1.15, CuDNN 7.6.5</li>
</ul>
<p>However... | <p>Actually, I figured it out by printing out what version was throwing the error. The version printed was '4'. I realized that this matched the pickle (HIGHEST_PROTOCOL) and that what I needed was the newest pull of the Stylegan2 repository, which included pickle format_version 4 in their allowed versions.</p> | tensorflow|machine-learning|pickle|google-colaboratory | 0 |
369,231 | 61,353,010 | Problem importing (and installing) NumPy in Jupyter Notebook | <p>I am having major trouble right now trying to use numpy in my jupyter notebook.</p>
<p>When I first tried to simply <strong>"import numpy"</strong>, it came back with the error: <strong>"ModuleNotFoundError: No module named 'numpy'"</strong></p>
<p>I then read somewhere that I probably needed to install numpy. </p... | <p>In my case, inside Jupyter notebook, you need to change Kernel (Anaconda environment). I thought you changed environment using <code>conda activate myEnv</code>, but when launching Jupyter, it defaults to the root environment.
I hope this is in fact true- I am a noob in Anaconda.</p> | python|numpy|jupyter-notebook | 0 |
369,232 | 61,219,125 | pandas dealing with a column with multiple values separated with delimiter for data analysis | <p>here's a python self learner trying to find a way working with columns with multiple values. the dataset is TMDb Movie Dataset and there are multiple values columns are like genres, cast etc.</p>
<p>I managed splitting values and counting them, it's okay. but what if I want to see the relationship between genres an... | <p>I would use something like the <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.stack.html" rel="nofollow noreferrer">stack</a> function to create a row for every item when splitting. For example, when you want to group by genre, create a row for genre when splitting (keeping othe... | pandas | 0 |
369,233 | 61,401,064 | operations on arrays in python from memory perspective | <p>I am trying to understand the memory allocation in the following operation:</p>
<pre><code>x_batch,images_path,ImageValidStatus = tf_resize_images(path_list, img_type=col_mode, im_size=IMAGE_SIZE)
x_batch=x_batch/255;
x_batch = 1.0-x_batch
x_batch = x_batch.reshape(x_batch.shape[0],IMAGE_SIZE[0]*IMAGE_SIZE[1]*IMA... | <p>I see "tf" in your code, so I am unsure if you are asking about tensors or arrays. Lets assume you are asking about arrays. In general, arrays are written to memory once and then manipulated. For example, </p>
<pre><code>import numpy as np
data = np.empty((1000,30,30,5)) #This took up 1000*30*30*5*dtype_size bytes... | python|numpy|numpy-ndarray | 0 |
369,234 | 61,308,861 | Add values in column based on condition in Python | <pre><code>Source_No Parent Type Amt
123 123 Tail 100
456 123 Coll 100
789 123 Coll 100
</code></pre>
<p>I want the code to create a new column a new column 'Tot_Amt' which will add the Amt of all Source_No, where Parent = Source_No and for other </p>
<p>Expected out... | <p>Here you go:</p>
<pre><code>df["Tot_Amt"] = (df.groupby(["Parent"])["Amt"].sum() - df.groupby(["Source_No"])["Amt"].sum()).fillna(0).reset_index(drop=True)
</code></pre> | python|pandas | 1 |
369,235 | 61,395,585 | How to go through a pandas data frame and only keep rows that have the same value throughout the entire row? | <p>If I have a pandas data frame like this:</p>
<pre><code> NaN NaN NaN 2 2 2
NaN NaN NaN 2 7 9
1 NaN NaN 2 6 4
8 NaN NaN 7 6 9
1 NaN NaN 1 1 1
NaN NaN NaN 2 7 9
2 NaN NaN 2 2 2
8 NaN NaN 7 6 9
1 NaN NaN 1 NaN 1
</code></pre>
<p>How do I only keep rows wh... | <p><code>DataFrame.nunique</code> will not count <code>NaN</code> so it suffices to check if there is a single unique value row-wise</p>
<pre><code>df.loc[df.nunique(axis=1).eq(1)]
0 1 2 3 4 5
0 NaN NaN NaN 2 2.0 2
4 1.0 NaN NaN 1 1.0 1
6 2.0 NaN NaN 2 2.0 2
8 1.0 NaN NaN 1 NaN 1
</code>... | python|python-3.x|pandas | 2 |
369,236 | 61,473,330 | CUDA error: CUBLAS_STATUS_ALLOC_FAILED when calling `cublasCreate(handle)` | <p>I got the following error when I ran my pytorch deep learning model in colab</p>
<pre><code>/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in linear(input, weight, bias)
1370 ret = torch.addmm(bias, input, weight.t())
1371 else:
-> 1372 output = input.matmul(weight.t())
... | <p>This error can actually be due to different reasons. It is recommended to debug CUDA errors by running the code on the CPU, if possible. If that’s not possible, try to execute the script via:</p>
<pre><code>CUDA_LAUNCH_BLOCKING=1 python [YOUR_PROGRAM]
</code></pre>
<p>This will help you get the right line of code wh... | nlp|pytorch|bert-language-model | 20 |
369,237 | 61,343,278 | Runtime warning when importing libraries in jupyter notebook | <p>So, I have never had this issue before on prior laptops- but recently on my new laptop- I get runtime warnings when trying to import libraries into Jupyter notebook- I'm not sure of the cause or how to fix it. Any solutions? </p>
<p><a href="https://i.stack.imgur.com/2qWJd.png" rel="nofollow noreferrer">enter image... | <p>Maybe there is some problem with the package which showing warning. Try to re-install the package. </p>
<p>First check whether the package working properly. If not install the pakage. Here in your screenshot it shows problem with numpy. To install numpy follow</p>
<pre><code>pip install numpy
</code></pre>
<p>Or<... | python|pandas|numpy|scipy|jupyter-notebook | 0 |
369,238 | 61,584,678 | how to reverse a dataframe to its original form in pandas | <p>i have data frame and used the command <code>pd.pivot_table(df,columns="category",index=["year","period"])</code>
did some data processing at this dataframe and i want to reverse the process to get the original formation of the df.
i tried with <code>pd.melt</code> and <code>pd.wide_to_long</code> without any luck. ... | <p>In general, for a given way to construct a pivot table from a data frame, two different data frames may yield the same pivot table. So this process is irreversible. For example, compare the following to your <code>df</code> and <code>table</code>:</p>
<pre class="lang-py prettyprint-override"><code>data2 = {"col1":... | python|pandas|dataframe|pivot|melt | 0 |
369,239 | 61,566,993 | Pip installs files to the old version of Python (Pip, Pandas, Python, Mac) | <p>I just started learning about Python (on macOS Mojave). I downloaded the latest version of Python and wanted to download software libraries like pandas.</p>
<p>So firstly I downloaded pip like that: sudo easy_install pip</p>
<p>Then I installed pandas using pip, however pandas location is: Requirement already sati... | <p>You might have both <code>pip</code> and <code>pip3</code> installed, where the first is used for python <code>2.7</code> and the second for python <code>3.8.2</code></p>
<pre><code>pip3 install pandas
</code></pre> | python|pandas|macos|pip | 2 |
369,240 | 61,488,796 | datetime is chaning date and month wrongly for some cases | <p>I am trying to change a <code>object</code> to a <code>datetime</code> value.</p>
<pre><code> id date
1 07/03/2020
2 20/02/2020
</code></pre>
<p>In the above <code>df</code> the column <code>date</code> is in the format <code>%d%m%Y</code> in strings value and when I apply <code>df['date'] = pd.dateti... | <p>You should try specifying the format when you convert:</p>
<pre class="lang-py prettyprint-override"><code>import pandas as pd
df['date'] = pd.to_datetime(df['date'], format='%d/%m/%Y')
</code></pre> | python|pandas|datetime | 2 |
369,241 | 61,220,657 | How to strip all text located to the left of the first number Python | <p>I am trying to augment addresses.
An example string:</p>
<pre><code>"Unit 3/45 main st, London"
</code></pre>
<p>Alternatively, I am trying to create addresses from dirty inputs by a customer, eg</p>
<pre><code>"U 68 25 MARKET ST"
"52/225 Jamboree Ave Old Saints Retirement Village"
"Unit 9 13-15 Endeavour Str... | <p>In response to how to strip all text to the left of the first number using python:</p>
<pre><code>samp_string = "U 68 25 Market St"
numbers = ['0','1','2','3','4','5','6','7','8','9']
def strip_left(string):
length = len(string)
count = 0
for i in range(0,length):
if (count == 0) and s... | python|string|pandas | 0 |
369,242 | 61,425,961 | How to generate predictions based on the distribution of the data using Python | <p>My dataframe currently looks like this (lets call this df_1).</p>
<pre><code>date var1
1-1-01 0.1
2-1-01 0.02
3-1-01 3.00
4-1-01 4.5
5-1-01 0.9
6-1-01 0.22
</code></pre>
<p>The <code>var_1</code> is normally distributed. (see photo below)
<a href="https://i.stack.imgur.com/NUfGI.png" rel="nofol... | <p>You can fit a normal distribution to <code>var_1</code>, and then draw samples from it,</p>
<pre class="lang-py prettyprint-override"><code>import scipy
import numpy as np
# fit to var_1
mu, std = scipy.stats.norm.fit(df['var_1'])
# generate data for var_2
var_2 = np.random.normal(mu, std, size=len(df['var_1']))
... | python|pandas|numpy|dataframe | 1 |
369,243 | 61,571,771 | pd.to_datetime format argument is rejected | <p>I can not understand this exception:</p>
<pre><code>date = '01/01 24:00:00'
pd.to_datetime(date, format = '%m/%d %H:%M:%S')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~\Anaconda3\envs\tf2\lib\site-packages\... | <p><strong>Short answer</strong>: 24 is not a valid hour.</p>
<p>The hours should be in the 0-23 range, as is specified in the <code>datetime</code> package for the <code>%H</code> format directive:</p>
<pre><code>%H Hour (24-hour clock) as a zero-padded decimal number. <b>00, 01, …, 23</b></code></pre>
<p>so <code>... | python-3.x|pandas|format|strftime|string-to-datetime | 1 |
369,244 | 61,363,712 | How to print a pandas.io.formats.style.Styler object | <p>I have the following code which produces a pandas.io.formats.style.Styler object:</p>
<pre><code>import pandas as pd
import numpy as np
df = pd.DataFrame({'text': ['foo foo', 'bar bar'],
'number': [1, 2]})
df1 = df.style.set_table_styles([dict(selector='th', props=[('text-align', 'center')])])
df... | <p>I found the answer for this:</p>
<pre><code>import pandas as pd
from IPython.display import display
import numpy as np
df = pd.DataFrame({'text': ['foo foo', 'bar bar'],
'number': [1, 2]})
df1 = df.style.set_table_styles([dict(selector='th', props=[('text-align', 'center')])])
df2 = df1.set_prope... | python|pandas|dataframe|pandas-styles | 12 |
369,245 | 61,440,184 | Who is Scott? - ValueError in Seaborn pairplot: Could not convert string to float: 'scott' | <h2>Who is Scott?</h2>
<h3>Problem</h3>
<p>I get the following error when trying to add the Education attribute from the Loan Prediction dataset to a pairplot using seaborn:</p>
<blockquote>
<p>ValueError Traceback (most recent call last)
~/anaconda3/lib/python3.7/site-packages/sta... | <p><code>scott</code> is the name of a method to choose the bandwidth when plotting a Kernel Density estimation (KDE). It is named after DW Scott (1).</p>
<p>I cannot look at your data, but my guess is that something is weird with one of the pairs of variable for a certain hue-level that prevents seaborn to calculate ... | python|pandas|matplotlib|runtime-error|valueerror | 4 |
369,246 | 61,207,129 | Less memory consumption for df.loc and drop rows which specific characters in a specific column | <p>i am loading a big dataframe in python, with several columns and million of rows, so for sure this is quite memory consuming. To exclude some types in a specific column I use:</p>
<pre><code>import pandas as pd
files = glob.glob("Path/*.csv")
dfs = [pd.read_csv(f, sep='\t', encoding='unicode_escape') for f in files... | <p>You can deal with the memory issues using <code>dask</code></p>
<pre><code>import dask.dataframe as dd
df = dd.read_csv('file.csv')
df = df.loc[~df.Type.isin(['A', 'B',...,'F'])]
df = df.compute() # this will give back the pandas dataframe
</code></pre>
<p>This will silently carryout operations chunk-wise in the ... | python|pandas | 1 |
369,247 | 61,525,611 | I am trying to build a neural network with one neuron using the pytorch library. It keeps giving me an error | <p>I am trying to build a neural network with one neuron using the pytorch library. This is my code (the error is at the bottom)</p>
<pre><code>import numpy as np
import random
import matplotlib.pyplot as plt
x_train = np.array([random.randint(1,1000) for x in range(1000)], dtype = np.float32)
y_train = np.array([int... | <p><code>torch</code> expects 2D input, so you need to add a new dimension to your inputs tensors.</p>
<pre><code>X_train = torch.from_numpy(x_train[..., np.newaxis])
X_test = torch.from_numpy(x_test[..., np.newaxis])
</code></pre>
<p>As someone commented above, you can also use <code>torch.unsqueeze</code>:</p>
<pr... | python|numpy|deep-learning|neural-network|pytorch | 0 |
369,248 | 61,232,835 | pandas options: apply float format to floats in tuples | <p>I have a dataframe which looks as follows:</p>
<pre><code>import pandas as pd
df = pd.DataFrame({"A":[1.25,2.25], "B":[(3.25,4.23),(1.22,6.33)]})
A B
0 1.25 (3.25, 4.23)
1 2.25 (1.22, 6.33)
</code></pre>
<p>Now, I want all floats to have only one decimal when I print the dataframe. So I apply... | <p>It can't be done globally, but can be specified using pandas <a href="https://pandas.pydata.org/pandas-docs/stable/user_guide/style.html#Finer-Control:-Display-Values" rel="nofollow noreferrer">styler</a> on a column level:</p>
<pre><code>import pandas as pd
df = pd.DataFrame({
'A': [1.25, 2.25],
'B': [(3.... | python|pandas | 0 |
369,249 | 61,404,150 | Searching data frame by indexes and exporting information | <p>So, I have indexes in <em>range</em> data frame. I want to use them to find values in test dataframe and extract values from into new data frame.
My current code is:</p>
<pre><code>d = []
for index in _range_.index:
d.append((test.loc[[index],:]))
</code></pre>
<pre><code>_range_ data set:
a
23... | <p>You could <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.join.html" rel="nofollow noreferrer">join</a> the two dataframes together on their common index using 'inner' then keep only the <strong>test</strong> columns. </p>
<pre><code>cols = __test__.columns
df = __range__.join(... | python|pandas|dataframe | 0 |
369,250 | 61,588,450 | Arrage dataframe based on the data presence in columns in multilevel dataframe | <p>I have a multilevel columns in the pandas <code>df</code> with the index as <code>appid</code> as follows:</p>
<pre><code>year |2016 2017 2018 2019 2016 2017 2018 2019
|ttl ttl ttl ttl tta tta tta tta
-----------------------------------------------------------------... | <p>You can first <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.stack.html" rel="nofollow noreferrer"><code>DataFrame.stack</code></a> for years to columns, then use <code>justify</code>, filter last 3 columns, create DataFrame and reshape back by <a href="http://pandas.pydata.org/p... | python|pandas|sorting|multilevel-analysis | 1 |
369,251 | 61,231,963 | Convert DD-MMM-YY to YYYY/MM/DD in Pandas | <p>DoB column in my dataset has date in the format below:</p>
<pre><code>0 12-Jan-79
1 13-Jan-70
2 11-Mar-84
3 11-Mar-84
4 01-May-86
...
1080 15-Mar-81
1081 07-Jul-96
1082 11-Apr-90
1083 22-Oct-64
1084 12-Jul-95
</code></pre>
<p>I need to convert the same int... | <p>You should specify the format:</p>
<pre><code>df['DateOfBirth'] = pd.to_datetime(df['DateOfBirth'], '%d/%m/%y')
</code></pre> | python|pandas|date | 0 |
369,252 | 61,607,740 | How to resample 1 minute data to 10 minute data? | <pre><code>import pandas as pd
file = pd.read_csv('D:\\Ayush\\Data\\Bank nifty Data\\Testing.csv')
file['Date_time'] = file['Date/Time'] + ' ' + file['Time']
file['Date_time'] = pd.to_datetime(file['Date_time'])
file.drop(columns=['Date/Time','Time'],inplace=True)
file['Date'] = file['Date_time'].dt.date
file['Date_t... | <pre><code>file.high.resample('10min').max()
</code></pre>
<p>I will suggest you to do the resample 1 by 1. </p>
<p><a href="https://benalexkeen.com/resampling-time-series-data-with-pandas/" rel="nofollow noreferrer">https://benalexkeen.com/resampling-time-series-data-with-pandas/</a></p> | python|pandas|resampling | 1 |
369,253 | 61,342,916 | Groupby cumulative sum in pandas based on specific condition | <p>I have a data frame as shown below.</p>
<pre><code>B_ID No_Show Session slot_num Patient_count
1 0.4 S1 1 1
2 0.3 S1 2 1
3 0.8 S1 3 1
4 0.3 S1 3 2
5 0.6 S1 4 1... | <p>So similar to your <a href="https://stackoverflow.com/q/61364013/9274732">question</a> later on, i think you need to create a function to return your two columns then <code>groupby.apply</code>. And if I understand correctly how you want to increment U_slot_num, then you can do:</p>
<pre><code>def create_u_columns ... | pandas|pandas-groupby | 1 |
369,254 | 68,545,690 | Concatenate ragged inputs in Keras | <p>The following code creates a dummy model that concatenate 2 inputs. One input is used with an Embedding layer with output size of 5, while the second input is just merged with the output of the Embedding layer:</p>
<pre><code>import tensorflow as tf
import numpy as np
from tensorflow.keras.layers import Input, Embed... | <p>Using <a href="https://www.tensorflow.org/api_docs/python/tf/concat" rel="nofollow noreferrer"><strong>tf.concat</strong></a> instead of <a href="https://www.tensorflow.org/api_docs/python/tf/keras/layers/Concatenate" rel="nofollow noreferrer"><strong>tf.keras.layers.Concatenate</strong></a> resolves the issue, beca... | python|tensorflow|machine-learning|keras|ragged | 1 |
369,255 | 68,603,456 | Is there a way how i can find the index of an multidimensional numpy array by matching it to another numpy array? | <p>Lets say i have the following case:</p>
<pre><code>array1=np.array([[1,0,0],[0,1,0],[0,0,1]])
array2=np.array([0,0,1])
</code></pre>
<p>now</p>
<pre><code>array1[2]
</code></pre>
<p>gives me the output</p>
<pre><code>[0,0,1]
</code></pre>
<p>so now i want to have code that gives the index of <code>array1</code> (in... | <p>I am not sure I understand your question correctly. But you could either do this:</p>
<pre><code>import numpy as np
np.where((array2 == array1).all(axis=1))
</code></pre>
<p>You can do this:</p>
<pre><code>index = np.argmax([0,0,1])
</code></pre>
<p>Or use this:</p>
<pre><code>indices = np.where(np.array([0,0,1]) ==... | python|arrays|numpy|indexing | 1 |
369,256 | 68,514,308 | Filling up a new column with values based on 2 window dates in another dataframe (in Pandas and PySpark) | <p>I have 2 dataframes. df1 looks like this:</p>
<pre><code>DATE QUANTITY
2015-10-28 14
2015-10-29 881
2015-10-30 533
2015-10-31 634
2015-11-01 637
</code></pre>
<p>...</p>
<p>I have a second df, df2 which is like this:</p>
<pre><code>STARTDATE ENDDATE VALUE
2015-10-25 2015-10-29 2
2015-11-01 ... | <p><strong>Pandas</strong>:</p>
<p>You can create a date range using <code>pd.to_datetime</code> and then explode followed by an outer merge:</p>
<p>Starting with converting the date values to datetime dtype (ignore this step if already a datetime dtype)</p>
<pre><code>df2[['STARTDATE','ENDDATE']] = df2[['STARTDATE','E... | python|pandas|dataframe|pyspark | 1 |
369,257 | 68,621,184 | Incrementally add pandas column value | <p>I have a dataframe like this:</p>
<pre><code>id trade_id tradedate settledate amt
3136 6828 20200616 20200630 15000000.0
3136 6934 20200616 20200630 15000000.0
3136 7007 20200618 20200630 30000000.0
3136 7050 20200620 20200630 25000000.0
3137 7091 ... | <p>Use <code>groupby</code> to get sum by <code>id</code> and <code>tradedate</code> but first convert <code>tradedate</code> to a real date for upsampling:</p>
<pre><code>df['tradedate'] = pd.to_datetime(df['tradedate'], format='%Y%m%d')
</code></pre>
<pre><code>>>> df.groupby(['id', 'tradedate'])['amt'].sum(... | pandas|dataframe|python-3.6 | 2 |
369,258 | 68,621,021 | when converting to csv from python i a missing first column in excel | <p>i had header info like</p>
<pre><code>header = Sr. No.^Name of Deductor^TAN of Deductor^^^^^Total Amount Paid / Credited(Rs.)^Total Tax Deducted(Rs.)^Total TDS Deposited(Rs.)^Sr. No.^Section^Transaction Date^Status of Booking^Date of Booking^Remarks^Amount Paid / Credited(Rs.)^Tax Deducted(Rs.)^TDS Deposited(Rs.)
</... | <p>The first column contains the index labels, which you can suppress like this:</p>
<pre><code>dfs["PART A"].to_csv("f.csv", index=False)
</code></pre> | python|excel|pandas | 0 |
369,259 | 68,501,033 | Keras LSTM model overfitting | <p>I am using an LSTM model in Keras. During the fitting stage, I added the validation_data paramater. When I plot my training vs validation loss, it seems there are major overfitting issues. My validation loss just won't decrease.</p>
<p>My full data is a sequence with shape <code>[50,]</code>. The first 20 records ar... | <p>20 records as training data is too small. There won't be enough variation in the training data for the model to approximate a function accurately, and so your validation data, which is likely much smaller than 20, will likely contain an example wildly different from just those 20 in the training data (i.e. it hasn't... | python|keras|time-series|lstm|tensorflow2.0 | 0 |
369,260 | 68,622,915 | How to analyze data efficiently | <p>TLDR: I would like some suggestions on how I can improve my code.</p>
<p>I'm learning data science from datacamp, I have an beginner-intermediate knowledge about coding. This is a data-analysis project I did today and am not happy with my code since it feels jumbled and inefficient.</p>
<p>In the below code I'm supp... | <p>I suggest that you take a look at the docs, and read about <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html" rel="nofollow noreferrer">groupby</a> and <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.agg.html" rel="nofollow noreferr... | python|pandas|dataframe | 0 |
369,261 | 68,684,274 | Pandas - conditional row average | <p>I have a dataframe:</p>
<pre><code>x = pd.DataFrame({'1':[1,2,3,2,5,6,7,8,9], '2':[2,5,6,8,10,np.nan,6,np.nan,np.nan],
'3':[10,10,10,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan]})
</code></pre>
<p>I am trying to generate an average of a row but only on values greater than 5. For instance - if a row had values of ... | <p>You can mask the values greater than 5 then take mean:</p>
<pre><code>x.where(x>5).mean(1)
</code></pre>
<p>Or:</p>
<pre><code>x.mask(x<=5).mean(1)
</code></pre> | python|pandas|dataframe | 3 |
369,262 | 68,485,434 | Pandas systematically identify missing multi-index categorical values | <p>I have the following dataframe, in the ID column we can have 2 feedbacks, good or bad. I cannot figure out how to systematically identify if a user is missing a feedback and if it is missing, add a new line with the missing feedback in the level 1 and add 0 to all values.</p>
<pre><code>import pandas as pd
df = {'I... | <p>Try:</p>
<pre class="lang-py prettyprint-override"><code>idx = pd.MultiIndex.from_product(
[
df.index.get_level_values(0).unique(),
df.index.get_level_values(1).unique(),
],
names=["USERS", "ID"],
)
df = df.reindex(idx, fill_value=0)
print(df)
</code></pre>
<p>Prints:... | python|pandas|dataframe|categories | 2 |
369,263 | 68,662,988 | Convert JSON to CSV and print directly (Python) | <p>I need to convert a json object to csv and print it directly and NOT write it to a file like the pandas method does. Is there a way to do so?</p> | <p>If I understand correctly:</p>
<p>you can try via <code>read_json()</code>+<code>to_csv()</code>:</p>
<pre><code>print(pd.read_json('yourjson.json',lines=True).to_csv())
</code></pre>
<p>OR</p>
<p>If your json is stored in a variable then:</p>
<pre><code>print(pd.Series(your json variable).to_csv())
</code></pre>
<p... | python|json|pandas|csv | 0 |
369,264 | 68,672,902 | Parse pandas dataframe with column contain array | <p>I have data from csv file in pandas dataframe. One column contain array (I mean its JSON originally) who I need parse.</p>
<p>Example of one row:</p>
<pre><code>ID_access,ID_part,ID_user,DATE,DESCRIBE,NOTE
865434334,66784,5468,2020-12-18 09:56:00,"Array
(
[ar] => 0034
[ident_a] => Array
(
... | <p>You can use regexp, for example:</p>
<pre><code>import re
import pandas as pd
rows = re.findall(r"(\d+),(\d+),(\d+).([-:\s\d]+).*?\[id_sec] => ([^\n]+)\s+\[note] => ([^\n]+)\s+\[date] => ([^\n]+)", t, re.S)
df = pd.DataFrame(rows, columns = ['ID_access','ID_part','ID_user','DATE','id_sec','note',... | json|python-3.x|pandas|dataframe | 1 |
369,265 | 68,515,585 | get column names using criteria and column value using pandas? | <p>I have a dataframe like as shown below</p>
<pre><code>df = pd.DataFrame({'sub_id': [101,101,101,102,102,103,104],
'test_status':['Y','N','Yes','No','Not sure','NOT SURE','YES'],
'remarks':[np.nan,"testvalue",np.nan,"ilike","wild",np.nan,"test&q... | <p>Use <code>isin</code> and keep only columns where all rows are True:</p>
<pre><code>>>> df.columns[df.isin(response).all()].tolist()
['test_status', 'reg_value']
</code></pre>
<p>Modify <code>response</code> to be more elegant:</p>
<pre><code>resp = fr"({'|'.join(set([r.lower() for r in response]))})
<... | python|python-3.x|pandas|dataframe|series | 4 |
369,266 | 68,644,644 | Pandas: plot a dataframe with on its right side rectangle colored according to an array's values | <p>I have a dataframe with 100 rows and 4 columns. I have an array (size 100,1) filled with values spanning between 0 and 1. I would like to plot my dataframe, with on its right side a rectangle which will take a color depending on the value of the array at a specific row (see the poor drawing I made, the array is writ... | <p>My solution would be to use <code>plot.subplots</code> to create two plots with the <code>width_ratios</code> argument as something like 19:1. On the left hand side you plot the data frame as usual, on the right hand side you plot the vector. Notice that I am using <code>vmin</code> and <code>vmax</code> to set the ... | pandas|dataframe|matplotlib|plot|seaborn | 1 |
369,267 | 68,651,332 | ValueError: expected sequence of length 0 at dim 2 (got 1) | <p>I've recently started a tutorial about Neural Networks with Python.
I am working on a cat/dog classification task with a CNN. However even though I thought I've done exactly what the tutorial told me to do, I somehow ended up with a dim error.</p>
<p><a href="https://www.youtube.com/watch?v=1gQR24B3ISE&list=PLQV... | <p>You didn't define the labels properly, it shouldn't be</p>
<pre><code>np.eye(2, self.LABELS[label])
</code></pre>
<p>but instead:</p>
<pre><code>np.eye(2)[self.LABELS[label]]
</code></pre> | python|neural-network|pytorch|conv-neural-network|valueerror | 0 |
369,268 | 68,857,847 | How to fix incorrect format in pandas column? | <p>I have a pandas dataframe called df1</p>
<pre><code>ID| ACTIVITY | Date
1 | activity 1 | 2/04/2016
2 | activity 2 | 3/04/2015
3 | activity 3 | 7/05/2016
3 | activity 4 | 2/04/2016
4 | activity 3 | 2/04/2017
5 | activity 6 | 2/04/2015
5 | activity 2 | 2/04/2016
6 | activity 1 | 2/04/2018
</code></pre>
<p>i have too... | <p>Use <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html" rel="nofollow noreferrer"><code>DataFrame.loc</code></a> for filter by conditions and columns names:</p>
<pre><code>mask = (df['Date'].dt.year == 2015)
df1 = df.loc[mask & (df['ACTIVITY'] == 'activity 1'), ['ID','Da... | python|pandas | 1 |
369,269 | 68,606,598 | Value. Error when doing a string strip and attempting to convert object column to an integer in python | <p>I am attempting to strip out any characters other than numbers in a column and then convert that column from an object to integer, but I am receiving an error message.</p>
<pre><code>data.dtypes
Column1 object
</code></pre>
<p>the column of interest has numbers but also <code>','</code> in it which I belie... | <p>try this :</p>
<pre><code>data['Column1'] = data['Column1'].str.replace(",", "").astype(int)
</code></pre> | python|pandas|numpy | 1 |
369,270 | 68,790,081 | Selecting explicit cells from pd.DataFrame via .at with MultiIndex | <p>I am having a MultiIndex based pd.DataFrame:</p>
<pre class="lang-py prettyprint-override"><code>import pandas as pd
data = pd.DataFrame([[2, 3], [4, 5], [6, 7], [8, 9], [10, 11], [12, 13]], index=pd.MultiIndex.from_tuples([
(pd.Timestamp('2019-07-01 23:00:00'), pd.Timestamp('2019-07-01 23:00:00'), 0),
... | <p>We can still use <code>loc</code> to assign the single cell value by creating the intermediate series having the same index corresponding to the cell that needs to be updated. As a side note, storing complex objects in pandas columns is generally not a good practice as you will loose the benefits of vectorization.</... | python|pandas|dataframe|multi-index | 5 |
369,271 | 68,568,527 | create excel file from DataFrame and allow download in flask, error: file format/extension not valid | <p>I've created a simple Flask app that turns a Pandas Dataframe into an excel file. The file gets downloaded when I go to the link, however, I'm just getting the following error once I try and open the file:</p>
<p><code>Excel cannot open the file 'df.xlsx' because the file format or file extension is not valid. Verif... | <p>The problem is that you're passing the dataframe to <code>ExcelWriter</code>'s <code>path</code> parameter instead of the <code>BytesIO</code> object (<code>output</code>).</p>
<pre><code>writer = pd.ExcelWriter(df, engine='xlsxwriter')
</code></pre>
<p><a href="https://pandas.pydata.org/pandas-docs/stable/reference... | python|pandas|flask|xlsxwriter | 2 |
369,272 | 68,471,877 | Tone sweep from array of frequencies with python | <p>I have array with frequency values and want to generate wav file with tone that sweeps between given values. Say</p>
<pre><code>freqs = [100, 100, 200, 400, 1000, 100, 50]
duration = 7
</code></pre>
<p>I want WAV with duration of 7 seconds. So from T=0 to T=1s tone should be 100Hz, from T=1 to T=2 sweeps from 100Hz ... | <p>Here's how you can do this for two arbitrary frequencies:</p>
<pre><code>import numpy as np
from scipy.signal import chirp
from scipy.io.wavfile import write
interval_length = 1 # in seconds
fs = 16000 # sampling of your signal
f0 = 100 # frequency 1
f1 = 200 # frequency 2
t = np.linspace(0, interval_length, in... | python|numpy|audio|scipy|signal-processing | 2 |
369,273 | 68,610,006 | Pandas DataFrame Dividing a column by itself taking first element and divide all the rows and so on | <p>I have a DataFrame from Pandas:</p>
<pre><code>import pandas as pd
data1 = {"a":[1.,3.,5.,2.]}
df1 = pd.DataFrame(data1)
</code></pre>
<p>df1:</p>
<pre><code> a
0 1.0
1 3.0
2 5.0
3 2.0
</code></pre>
<p>Now I want to iterate over the rows. For every row, divided by the first elements of the sam... | <p>You can use <code>numpy</code> to increase the speed of the process:</p>
<pre><code>>>> df1
a
a_1
a_2
0 1.0
1 3.0
2 5.0
3 2.0
</code></pre>
<pre><code>import numpy as np
a = np.hstack(df1.values)
m = np.repeat(a, len(a)).reshape((a.shape[0], -1))
df = pd.DataFrame(a / m, columns=df1.columns... | python|pandas|dataframe | 3 |
369,274 | 68,725,646 | Styled pandas dataframe in Dash-Plotly | <p>I have pandas data frame and am using the "df_styled = df.style.apply(...)" method to style the values in data frame conveniently (in a Jupyter Notebook).
However, I am unable to display this styled output when using Dash-Plotly dashboard.</p>
<p>Can someone suggest anything?</p>
<p>I have tried df_styled.... | <p>Instead of directly using the styled components of Pandas, I would recommend the below</p>
<ol>
<li>Transform your dataframe to a plotly-dash datatable</li>
<li>Format the dash datatable based on the plotly-dash documentation.</li>
</ol>
<p>an example code would be as below</p>
<pre><code>
mytable = dash_table.Data... | python|pandas|jupyter-notebook|plotly-dash | 0 |
369,275 | 68,583,045 | get partial string contained in "()" from a pandas DataFrame | <p>I have a df:</p>
<pre><code> MinMaleTA
28 888(G2M)
29 888(AAM)
30 888(G2M)
31 888(G2M)
32 888(AAM)
33 888(G2M)
34 888(G2M)
35 888(AAM)
36 888(G2M)
37 888(G2M)
38 888(G2M)
39 888(G2M)
40 888(AAM)
41 888(G2M)
42 888(G2M)
43 888(G2M)
</code></pre>
<p>sometimes more ... | <p>Use <code>str.extract</code> method with a regex:</p>
<pre><code>>>> df['MinMaleTA'].str.extract(r'\((.*)\)')
0
28 G2M
29 AAM
30 G2M
31 G2M
32 AAM
33 G2M
34 G2M
35 AAM
36 G2M
37 G2M
38 G2M
39 G2M
40 AAM
41 G2M
42 G2M
43 G2M
</code></pre>
<p><code>\(</code> and <code>\)</code> match the... | python|pandas|dataframe|numpy | 1 |
369,276 | 68,461,656 | Correct application of numpy array dimensions | <p>I am in the process of collecting 3D-coordinates of multiple key points of one person's or multiple people's body over time. This data needs to be collected in one data set.
This basically leads to an input for each frame consisting of one or multiple 19x3 matrices. The data set structure is supposed to look as foll... | <p>If you know the max number of 19x3 matrices in each frame, then you could set up the outer size and put empty matrices (or matrices of 0) where you don't have data. I think you might want to use pandas dataframe and take advantage of the indexing to make it easier to access your data later on.</p> | python|numpy|numpy-ndarray | 1 |
369,277 | 68,554,267 | Excessive disk writes when using numpy.memmap | <p>I have implemented a file-backed HashTable using <a href="https://numpy.org/doc/stable/reference/generated/numpy.memmap.html" rel="nofollow noreferrer">numpy.memmap</a>. It appears to be functioning correctly, however, I notice that on Linux both KSysGuard and SMART are reporting ridiculous IO Write amounts. About 5... | <p><code>memmap</code> work by mapping <strong>pages</strong> in virtual memory (typically to physical memory pages or storage device ones like in your case). On most platforms, the size of pages is at least 4 KiB. As a result, any write in a page may cause the whole page to be updated.</p>
<p>SSDs and more generally <... | python|python-3.x|linux|numpy|numpy-memmap | 1 |
369,278 | 68,785,645 | How to add a title to a pandas dataframe plot | <p>Able to create 2 nice time series graphs, but <code>plot.title()</code> is not working. How can this be fixed?</p>
<pre><code>import pandas as pd
import datetime
import matplotlib.pyplot as plt
if __name__ == '__main__':
index_data = pd.read_csv('comparison_dataset.csv')
index_data['DATE'] = pd.to_dateti... | <p>Your <code>plt.title</code> call happens before the figure is created (with <code>plt.figure()</code>). This cannot work. You should move the <code>plt.title</code> command after the graph <code>df.plot()</code>.</p>
<p>Also, if you want a <strong>figure</strong> title, the command is <code>plt.suptitle</code>. <cod... | python|pandas|matplotlib | 1 |
369,279 | 68,807,139 | Convert Duration Column to integer value | <p>I have a pandas dataframe with a duration column, they're currently of the object type with entries like 01:15:12 which means 1 hour and 15 minutes and 12 seconds, not the time. I want to strip the seconds off and convert these values to 1.25 because 15 minutes/60 minutes in an hour is .25 hours. Any suggestions? Th... | <p>Use <a href="https://pandas.pydata.org/docs/reference/api/pandas.to_timedelta.html" rel="nofollow noreferrer"><code>pandas.to_timedelta</code></a>:</p>
<pre><code>pd.to_timedelta(df['COLUMN']).dt.floor('T').dt.total_seconds()/3600
</code></pre> | python-3.x|pandas|dataframe|datetime|time | 0 |
369,280 | 68,459,352 | Python Dataframe Convert string to list type | <p>I have a CSV file coming from the field. It has the data in a peculiar format. That is, a list of values in string format. I want to convert it to the list type</p>
<p>My code:</p>
<pre><code>df = pd.DataFrame({'x':['-1,0,1,2,10','1.5,2,4,5'],'y':['2.5,2.4,2.3,1.5,0.1','5,4.5,3,-0.1']})
df =
x ... | <p><code>applymap</code> with <code>ast.literal_eval</code> would be the fastest option</p>
<pre><code>import ast
df.applymap(ast.literal_eval)
</code></pre>
<p>Note this will produce tuples in output, although it doesn't matter but if you specifically need lists in your output then we can chain another <code>applymap... | python|pandas|dataframe | 1 |
369,281 | 68,639,180 | list of Bytes object to dataframe | <p>I have a list of Bytes (strings) which is separated by "\n".
I want to create a data frame from the list, and to separate each element to 15 columns.
I have succeeded to separate the rows but I'm getting 1 column instead of 15.</p>
<pre><code>from io import BytesIO
df = pd.read_csv(BytesIO(b'\n'.join(tmp))... | <p>From your output it looks like your <code>tmp</code> list contains <code>\t</code> characters already, which would imply that the <code>\t</code> character is the separator in the data and not <code>\n</code> as you have specified. It is unlikely that <code>\n</code> is a separator between columns, which is what the... | python|pandas|dataframe | 1 |
369,282 | 68,453,246 | Creating a sub-set of data having only null values | <p>I have a data as under in a pandas dataframe [Original shape of the data : 149347 rows and 2 columns]. Purpose includes a text/strings and employeeID includes floats.</p>
<p><a href="https://i.stack.imgur.com/Elmi9.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/Elmi9.png" alt="enter image descrip... | <p>Try:</p>
<pre class="lang-py prettyprint-override"><code>print(df[df.Purpose.eq("")])
</code></pre>
<p>Prints:</p>
<pre class="lang-none prettyprint-override"><code> Purpose Employee
2 3
4 5
</code></pre>
<hr />
<p><code>df</code> used:</p>
<pre class="lang-none prettyprin... | python|pandas|dataframe | 1 |
369,283 | 68,561,499 | How to unit test the columns names are valid for a function that reads data with `pandas.read_sql()`? | <p>I have a function to read data from a MySQL database:</p>
<pre><code>def example(mysql_engine) -> DataFrame:
query = """SELECT col_1 FROM xxx.xxx"""
df = pandas.read_sql(query, mysql_engine)
return df
</code></pre>
<p><code>mysql_engine</code> is returned by another func... | <p>You can use <code>assert col_1 in df.columns</code> for the unit tests.</p>
<p>For the Database engine, you can use a mock object. You can check <a href="https://docs.python.org/3/library/unittest.mock.html" rel="nofollow noreferrer">this link</a> from the standard library.</p> | python|mysql|pandas|unit-testing|read-sql | 0 |
369,284 | 68,544,825 | Python: how to groupby a dataframe with column name numbered for the unique values of a column? | <p>I have a dataframe that looks like the following where I have different number of <code>case</code> and 3 unique values for <code>val</code>.</p>
<pre><code>df
case val
0 0 x
1 0 y
2 0 z
3 1 x
4 1 z
5 2 y
</code></pre>
<p>Now I would like to have ... | <p>Use <code>pivot</code>:</p>
<pre><code>out = df.pivot(index='case', columns='val', values='val')
out.columns = [f'val{i}' for i in range(len(out.columns))]
</code></pre>
<pre><code>>>> out
val0 val1 val2
case
0 x y z
1 x NaN z
2 NaN y NaN
</code></pre> | python|pandas | 2 |
369,285 | 68,656,156 | Is there a way to stop to_dict() from updating strings to datetime? | <p>I'm trying to develop a general solution to upload data from a dataframe to a MySQL table. The MySQL insert statement requires a ON DUPLCIATE KEY UPDATE so I can't just use to_sql(). I have it working fairly well, except in some cases to_dict() is converting date strings ('YYYY-MM-DD') to datetimes which can't be ... | <p>After you import your df into python</p>
<pre><code>df['column'].astype(str)
</code></pre>
<p>See if this works, I don't have enough info to test this myself. Regardless the pandas.DataFrame.astype function will probably be useful. <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.... | python|pandas | 0 |
369,286 | 68,659,794 | Python Transpose/Stack multiple columns | <p>Have a number of people that return responses to three questions.</p>
<p>These three questions are asked numerous times, the issue is new responses are recorded as new columns.</p>
<p><a href="https://i.stack.imgur.com/0uewk.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/0uewk.png" alt="Dataset" ... | <p>With a little renaming to add a suffix to the base <code>stubnames</code>, we can the use <a href="https://pandas.pydata.org/docs/reference/api/pandas.wide_to_long.html" rel="nofollow noreferrer"><code>pd.wide_to_long</code></a>:</p>
<pre><code># Add Suffix to base Q1 Q2 Q3
df = df.rename(columns=dict(zip(['Q1', 'Q2... | python|pandas | 1 |
369,287 | 68,635,970 | Cylindrically symmetric magnetic field | <p>I want to plot the motion of a positive charge in a cylindrically symmetric magnetic field.
<br>
I am assuming a cylinder around the z-axis, with the magnetic field going in clockwise direction. The B-field has magnitude of 6T and the distance R from the z-axis is 3m. The charged particle is launched in positive dir... | <p>With <code>solve_ivp()</code>:</p>
<p>There are only two functions, the first one, <code>B()</code> takes care of the geometry of the magnetic field (rotationally invariant relative to the z axis and radially invariant, with always the same magnitude at each point), and the second one <code>f()</code> takes care of ... | python|numpy|physics|ode | 1 |
369,288 | 68,827,675 | Sorting a pandas dataframe by a column containing numbers and text | <p>I have a pandas dataframe that I want to sort by one of the columns. Problem is, the data that needs to be sorted looks like this: "'Number 1' - Specimen 'Number 2'".
I want to sort by 'Number 1' first, and then 'Number 2'.</p>
<p>An example:</p>
<pre><code>import pandas as pd
df = pd.DataFrame({'Name': [... | <p>I convert the 'Name' column to a list of 2 numbers and then sort the column and then return the index.</p>
<pre><code>index = df['Name'].apply(lambda x: list(map(int,x.split('- Specimen')))).argsort().to_list()
df.iloc[index]
</code></pre> | python|pandas | 3 |
369,289 | 68,794,425 | How to convert multilevel columns to multilevel index | <p>I have a dataframe with multilevel columns such as this:</p>
<pre><code> x1 x2
A B A B
date
2021-01-01 11.82 22.88 11.83 22.93
2021-01-02 11.95 22.78 12.02 23.08
2021-01-03 12.18 22.99 12.18 22.99
</code></pre>
... | <p>You can try stack with swaplevel:</p>
<pre><code>df.rename_axis(['','sym'],axis=1).stack().swaplevel().sort_index())
</code></pre>
<hr />
<pre><code> x1 x2
sym date
A 2021-01-01 11.82 11.83
2021-01-02 11.95 12.02
2021-01-03 12.18 12.18
B 2021-01-01 22.88 ... | python|pandas|dataframe | 2 |
369,290 | 68,869,465 | How to make types in the rows of pandas dataframe to become the column header with result as row type? | <p>I have a df structured in following setting and would like to change it so that the types found in the column <code>type</code> are the the row readers with the original <code>result</code> as the row the new type column, condensing all <code>ids</code> into one row. For example, I would like to change the following... | <p>Try <code>pivot_table</code> with <code>rename_axis</code>:</p>
<pre><code>>>> df.pivot_table('result', ['id', 'name'], 'type', aggfunc=''.join).reset_index().rename_axis(columns=None)
id name 1 2 3
0 A Apple X X X
1 B Banana Y Y NaN
2 C Cantaloupe NaN Z Z
>... | python|pandas|dataframe | 0 |
369,291 | 68,726,630 | Accessing yahoo finance 104 stocks closing price but it appends the data in a single row instead of column | <pre><code>
tickers = "GNA.BO PDMJEPAPER.BO MEGH.BO REFEX.BO GULPOLY.BO TRIVENI.BO TCI.BO NUCLEUS.BO
SHILPAMED.BO JUBILANT6.BO TITANBIO.BO INDOBORAX.BO POLYPLEX.BO MAZDALTD.BO KSE.BO RAJGLOWIR.BO
MANORG.BO TATAMETALI.BO HIL.BO BAJAJST.BO TINPLATE.BO SESHAPAPER.BO DECCANCE.BO GESHIP.BO
ESTER.BO DIAMINESQ.BO DENO... | <p>For long form data collection, I think it is easy to prepare an empty data frame, get the stock data sequentially, and add it to the empty data frame.</p>
<pre><code>import yfinance as yf
import pandas as pd
tickers = "GNA.BO PDMJEPAPER.BO MEGH.BO REFEX.BO GULPOLY.BO TRIVENI.BO TCI.BO NUCLEUS.BO"
tickerl... | python|pandas|stock|yfinance | 0 |
369,292 | 68,535,750 | square the numbers in lists in dictionaries in Python | <p>I have a dictionary created like:</p>
<pre><code>di = {
"R": [{7, 9}, {5, 8}],
"N": [{6, 9}, {8, 8}],
"L": [{7, 9}, {5, 0}],
"P": [{0, 9}, {7, 8}]
}
</code></pre>
<p>I want to square <code>dic["R"]</code> numbers so the end result would be <code>{4... | <p>Note that these are not lists of ints, or lists of lists of ints, they are lists of sets of ints:</p>
<pre><code>>>> di = {
... "R": [{7, 9}, {5, 8}],
... "N": [{6, 9}, {8, 8}],
... "L": [{7, 9}, {5, 0}],
... "P": [{0, 9}, {7, 8}]
... }
>>> di... | python|python-3.x|list|numpy|math | 2 |
369,293 | 68,635,750 | replace nested 'for loop' with lambda in python | <p>I am working on one task where I need to check the cosine similarity between two dataframe columns.
I am using two for loop to iterate over two columns of data1 and data2 respectively.</p>
<pre><code>for i in range(0,len(input_df)):
for j in range(0,len(data1)):
##check similarity ratio
similari... | <p>To compute the cosine similarity between two vectors (your two columns), you could make use of NumPy:</p>
<pre><code>import numpy as np
def cosine_similarity(a, b):
return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
cosine_similarity(input_df['Summary'], data1['Summary'])
</code></pre>
<p>However, b... | python|python-3.x|pandas | 0 |
369,294 | 68,795,929 | Pandas error: "None of [Index([' '], dtype='object')] are in the [columns]" | <p>For some reason, my code works when a list I am passing contains only integers. Using strings otherwise leads to the error in the title.</p>
<p>Here is my code:</p>
<pre><code>def get_support(self, data, itemset):
return data[itemset].all(axis = 'columns').sum()
# I also tried: return data.loc[:, itemset].al... | <p>You're going wrong here:</p>
<p><code>data[itemset].all(axis = 'columns').sum()</code></p>
<p>You can't <code>sum()</code> a string. You could run it through a data cleaning function first to make sure the list only has integers or floats.</p> | python|pandas | 0 |
369,295 | 68,498,883 | in python, spyder, i import sklearn, why it doesn't find 'classification' in 'sklearn.metric'? | <p>it sends :
print(metrics.classification.accuracy_score(y_test, y_pred))</p>
<p>AttributeError: module 'sklearn.metrics' has no attribute 'classification'</p>
<p>it would appear that my sklearn can't import 'classification', but i can't find out why, can you help me please ?</p>
<pre><code>from sklearn import metrics... | <p>Upgrade your sklearn should solve this.</p>
<p>And it's called scikit-learn instead of sklearn in pip, do</p>
<pre><code>pip install scikit-learn
</code></pre>
<p>If you already have it, try</p>
<pre><code>pip install --upgrade scikit-learn
</code></pre> | python|classification|sklearn-pandas | 1 |
369,296 | 68,760,794 | How to merge two dataframes without getting additional rows? | <p>Basically, I have two dataframes, the first one looks like this:</p>
<p><img src="https://i.stack.imgur.com/QfXkK.png" alt="df1" /></p>
<p>And the second one like this:</p>
<p><img src="https://i.stack.imgur.com/0zBuj.png" alt="df2" /></p>
<p>I want to get the columns "lat" and "lnt" of the secon... | <p>The <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html" rel="nofollow noreferrer">Pandas merge function</a> defaults to an inner join. Since you're looking to merge in the columns of <code>df2</code> to <code>df1</code>, you should use a left join. This will give you all the rows of <c... | python|pandas | 1 |
369,297 | 68,727,367 | Group the column values in a data frame | <p>I have a Json array with key value pairs like below</p>
<pre><code>[
[
{
"value":"Dimension1",
"formattedValue":"Dimension1"
},
{
"value":"In",
"formattedValue":"In"
},
... | <p>You can either use @AnuragDabas’ solution from the comments, combined with <code>.rename_axis()</code> to remove the index names:</p>
<pre><code>>>> df.set_index(['Dimension', 'Type']).rename_axis([None, None])
Amount1 Amount2
Dimension1 In 100 200
Out 30 4... | python|pandas|dataframe | 2 |
369,298 | 68,749,984 | How to change from an int to a string based on a condition | <p>Let's say that I have a data frame with a list of names and their gender. The default data frame has either a "0" or a "1" in the gender column for each name of the person in which the "0" implies that the person is a male while the "1" implies that the person is a female. How... | <p>Just do:</p>
<pre><code>df['gender'] = df['gender'].map({1:'female', 0:'male'})
</code></pre>
<p>OR</p>
<pre><code>df.loc[(df.gender == 0), 'gender'] = 'male'
df.loc[(df.gender == 1), 'gender'] = 'female'
</code></pre> | python|pandas | 1 |
369,299 | 68,856,313 | Python Pandas pivot_table - Count of values in one column | <p>I am having a DataFrame (28 rows from the Titanic passenger list) which has a column "Sex" consisting of two values, "Male", "Female". I want to get the count of Males/Females</p>
<p>The output should show "Sex" as Row labels(index) and the count (of Male/Female) in the second... | <p>It's just <code>df['Sex'].value_counts()</code></p> | python|pandas | 1 |
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