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 |
|---|---|---|---|---|---|---|
12,800 | 53,597,454 | Updated approach for max value using pandas | <p>How to obtain the High of the day using pandas and make it update throughout the day as you can find it <a href="https://i.stack.imgur.com/0lCLP.png" rel="nofollow noreferrer">here</a> :</p>
<p>Note the highest price of the day is kept unchanged if the next high was lower than it. And it will be updated if a new hi... | <p>It seems you are looking for the cumulative maximum, <a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.cummax.html" rel="nofollow noreferrer"><code>.cummax()</code></a></p>
<pre><code>df["AlltimeHigh"] = df["High"].cummax()
</code></pre> | python|pandas|numpy|matplotlib | 4 |
12,801 | 20,356,725 | Error installing Numpy via setup tools in virtualenv on Ubuntu | <p>I'm having trouble installing numpy in a virtualenv with setuptools. I'm using setup() with a list of dependencies passed in via install_requires. One of those dependencies is numpy. I get an error complaining that Python.h can't be found. Yet the python-dev package is installed. Running:</p>
<pre><code>locate Pyth... | <p>Looks like you don't have a Fortran compiler installed. Try <code>apt-get install gfortran</code>.</p> | python|ubuntu|numpy|virtualenv|setuptools | 0 |
12,802 | 12,307,854 | How to get matplotlib to redraw axis | <p>Here is the program I've been working on for a while, and I've gotten some good troubleshooting tips on here already.</p>
<p>It runs just fine, and speed is not of primary concerns because it's just supposed to give an intuitive look at how different graphs change with a varying input. But, how can I get the graphs... | <p>Remove <code>frameon=False</code> when create the Figure object:</p>
<pre><code>Figure(figsize=(4,3), dpi=100)
</code></pre>
<p>If you want the background color of Figure is the same as Tk window, you can set it by <code>facecolor</code> aurgument:</p>
<pre><code>Figure(figsize=(4,3), dpi=100, facecolor=TK_BACKGR... | python|user-interface|numpy|matplotlib|tkinter | 1 |
12,803 | 71,834,529 | I have this code using the alpaca websocket but there is a function which is not defined | <pre><code>from time import sleep
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import requests
plt.style.use("fivethirtyeight")
import alpaca_trade_api as tradeapi
import threading
from bs4 import BeautifulSoup
import datetime
import logging
api_key = 'YOUR API KEY'
api_secret = 'YO... | <p>That tutorial is slightly out of date. Newer versions of alpaca_trade_api are using the <code>Stream</code> class:</p>
<pre><code>conn = tradeapi.stream.Stream(
key_id=api_key,
secret_key=api_secret,
base_url='https://paper-api.alpaca.markets',
data_feed='iex'
)
</code></pre>
<p>Note that the free da... | python|pandas|api|websocket|algorithmic-trading | 1 |
12,804 | 71,879,164 | Pytorch's autograd issue with joblib | <p>There seems to be a problem mixing pytorch's autograd with joblib. I need to get gradient in parallel for a lot of samples. Joblib works fine with other aspects of pytorch, however, when mixing with autograd it gives errors. I made a very small example which shows serial version works fine but the parallel version c... | <p>The problem is that parallel uses "loky" as a default backend, you should use "threading" as a backend, by this way your code will run as intended, refer to the following documentation about Joblib Parallel class <a href="https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html" rel... | pytorch|joblib|pytorch-lightning|autograd | 1 |
12,805 | 71,800,257 | Calculate difference and percent difference between rows within a group | <p>I have a dataframe <code>df</code> that looks like this:</p>
<pre><code>Supplier SKU PartFull 2022-03-12 2022-03-05 2022-02-26
A 123A 565 0.0564 0.0543 0.0554
B 123A 565 0.0392 0.0407 0.0432
A 424S 773 0.0121 ... | <p>You can achieve it by setting <code>["Supplier", "SKU", "PartFull"]</code> as index (so they will not be removed when grouping) and then grouping by <code>["Supplier", "SKU"]</code> to perform <code>diff</code> and <code>pct_change</code></p>
<pre><code>grouped = df... | python|pandas|dataframe | 1 |
12,806 | 17,814,320 | Scipy - Stats - Meaning of parameters for probability distributions | <p>Scipy <a href="http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.expon.html#scipy.stats.expon" rel="nofollow">docs</a> give the distribution form used by exponential as: </p>
<pre><code>expon.pdf(x) = lambda * exp(- lambda*x)
</code></pre>
<p>However the fit function takes :</p>
<pre><code>fit(da... | <ol>
<li><p>Every univariate probability distribution, no matter what its usual formulation, can be extended to include a location and scale parameter. Sometimes, this entails extending the support of the distribution from just the positive/non-negative reals to the whole real number line with just a PDF value of 0 whe... | python|numpy|scipy | 4 |
12,807 | 8,741,445 | better way to use index array to get elements? | <p>I want to get the elements of a <code>numpy</code> array using an index array like so</p>
<pre><code>import numpy
a = numpy.arange(6)
ind = [2,3]
</code></pre>
<p>now, <code>a[ind]</code> gives me the 3rd and 4th element, but I actually want all the <em>other</em> elements of <code>a</code>.
Is there a one line/... | <p>There isn't a straightforward way I know of to get the complement of a set of integer indices. Boolean index negation is easy, which lets you do something like this:</p>
<pre><code>In [100]: a=np.arange(6)
In [101]: ind=[2,3]
In [102]: cind=(a==a)
In [103]: cind[ind]=False
In [104]: a[cind]
Out[104]: array([0, ... | python|numpy | 4 |
12,808 | 55,361,711 | How to Insert excel data with multiple row headers into pandas dataframe | <p>I'm trying to convert and unstructured excel file to structured. My headers pan across two rows (1 AND 2) For headers SKU, Qty and sales </p>
<pre><code> A B C D
1 Qty Sales
2 SKU
3 2345 Nail Varnish ... | <p>If you know the column names beforehand then you can do </p>
<pre><code>df = pd.read_excel('D:\Sales.xlsx', header=None, names=['SKU','Description','Qty','Sales'], skiprows=2)
</code></pre>
<p><strong>explaination</strong></p>
<ul>
<li><code>header=None</code> won't take default headers from excel file</li>
<li><... | python|python-3.x|pandas | 1 |
12,809 | 55,421,029 | How to deal with 'out-of-bounds' error for timestamp in the method 'pandas.read_sql()' | <h2>Environment :</h2>
<ul>
<li>pandas 0.20.3</li>
<li>python 2.7.13</li>
</ul>
<h2>Objective and Problem :</h2>
<p>I'm using method <strong>pandas.read_sql()</strong> to retrieve the table from SQL that also contains values of invalidated timestamp (9999-12-30 23:00:00).
But, the line of code throws the error <str... | <p>In 0.20.3 <code>pandas.read_sql()</code> has <code>parse_dates : list or dict, default: None</code></p>
<p>So it would be unexpected that pandas is parsing the date found in your database.</p>
<p>Remove <code>select * from orders</code> to <code>select col1, ..., datecol from orders</code> and eliminate <code>date... | python|pandas|python-2.7 | 0 |
12,810 | 55,233,666 | How can I optimize my code? (python-opencv-numpy) | <pre><code>import cv2
import numpy as np
img = cv2.imread('/home/user/Vision Artificial/fig_2.png')
tam = np.size(img, 0), np.size(img, 1)
cadena = '[255 0 0]'
for i in range(tam[0]):
for j in range(tam[1]):
if(str(img[i, j]) != cadena):
img[i, j] = [255, 255, 255]
cv2.imwrite('/home/user... | <p>Conversion of the integer array to a string is slow.
Instead, compare integer arrays directly, using numpy.array_equal. </p>
<pre><code> import cv2
import numpy as np
import time
start_time = time.time()
img = cv2.imread('/home/user/Vision Artificial/fig_2.png')
tam = np.size(img, 0), np.siz... | python|numpy|opencv | 0 |
12,811 | 55,326,879 | Appending dataframes in python | <p>Append is not working using for loop in python</p>
<blockquote>
<p>It is working without for loop but It's not working using for loop</p>
</blockquote>
<pre><code>import os as o
import pandas as pd
j=0
ls=[]
files = o.listdir("demo")
for i in files:
ls.append(i)
df=pd.read_csv("demo/"+ls[0])
t=len(ls)
for i in... | <p>Using df.append is highly inefficient, you should instead</p>
<pre><code>dfs = [pd.read_csv("demo/"+ls[i]) for i in range(1, len(ls))]
df = pd.concat(dfs)
</code></pre> | python|pandas | 1 |
12,812 | 56,829,759 | Is it possible to pass a for loop in a function? | <p>I know it sounds ridiculous but I have to pass a for loop into a function. I have a dataframe with 75+ columns and most of them are categorical variables. One of the variable is called <code>SalePrice</code> and i wish to find the correlation between the categorical variables and <code>SalePrice</code>.</p>
<p>This... | <p>You can use a list comprehension - essentially, create a list using a <code>for</code> loop, and pass that in:</p>
<pre><code>stats.f_oneway([qualityTest['salePrice'][qualityTest['OverallQual'] == i] for i in qualities])
</code></pre>
<p>Or if you want it passed as <em><code>i</code> separate arguments</em> instea... | python|pandas|scipy|anova | 5 |
12,813 | 56,567,468 | NaN error from .map on a column in a dataframe | <p>I have a dataframe that I'm working with that contains a column that has state names spelled out and Im' trying to convert that into the two letter abbreviation form. I found a separate cvs file with all the state names and converted it into a dictionary. I then tried to use that dictionary to map the column but g... | <p>You have explained that you have split the <code>city_state</code> column into <code>city</code> and <code>state</code>. For <code>map</code> to work, the value must be an exact match. What I speculate is that you have <code>spaces</code> on either side of the state series.</p>
<p>Try doing</p>
<pre><code>newtop50... | python|pandas | 1 |
12,814 | 25,651,480 | Does scipy logsumexp() deal with the underflow challenge? | <p>Does the scipy's <code>logsumexp()</code> implementation include the hack that prevents underflow by subtracting the maximum found value in the array from each element?</p>
<p>The one explained here below, where <code>m = maxval</code>:</p>
<p><img src="https://i.stack.imgur.com/yDuEa.png" alt="enter image descri... | <p>You can inspect the source code defining <code>logsumexp</code> <a href="https://github.com/scipy/scipy/blob/v0.14.0/scipy/misc/common.py#L18">here</a>. (Note that there is a link to the source on <a href="http://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.misc.logsumexp.html">the doc page</a>).</p>
<... | python|arrays|numpy|scipy|natural-logarithm | 10 |
12,815 | 67,016,678 | How to use groups parameter in PyTorch conv2d function with batch? | <p>follow by the question in <a href="https://stackoverflow.com/questions/46536971/how-to-use-groups-parameter-in-pytorch-conv2d-function">How to use groups parameter in PyTorch conv2d function</a></p>
<p>May I know if the input batch size = 4, for each batch it has independent filter to conv with it, and I modify the... | <p>when you have filter of <code>shape (3,4,3,3)</code> then it is expected to have num of channels as 12</p>
<p>This should work</p>
<pre><code>import torch
import torch.nn.functional as F
inputs = torch.autograd.Variable(torch.randn(3,12,10,10))
filters = torch.autograd.Variable(torch.randn(3,4,3,3))
out = F.conv2d(i... | pytorch | 0 |
12,816 | 66,929,774 | Create a dataframe from numpy arrays | <p>I have the following code and I am stuck in creating a pandas dataframe by some numpy arrays.</p>
<pre><code>def gradient_descent_classification_prediction(x_test):
y_pred = pd.DataFrame()
for i in range(0 , 10):
print(i)
b = pd.read_csv("theta"+str(i)+".csv" , delimiter=&... | <p>There are many ways to do this but essentially append a list to dataframe does not ensure its shape. It's easier to keep it as a dictionary or list, then convert to data frame before writing. Below I use a dictionary:</p>
<pre><code>def gradient_descent_classification_prediction(x_test):
y_pred = {}
for i in... | python|pandas|numpy|numpy-ndarray | 0 |
12,817 | 67,155,961 | How to split whole text to sentence pieces (parts) by python | <p><strong>I need to split whole text to 4 parts.</strong></p>
<p>for example:</p>
<blockquote>
<p>AAAA: AAAAAAAAAAAAAAA AAAAAAAA AAAAAAAAAAAA AAAAAAAAAAAAAA. BBBB:
BBBBBBBBBB BBBBBBBBBBBBB BBBBBBBBB BBBBBBB. CCCC: CCCCCCC CCCCC CCCCCC
CCCCCCCCCCC CCCC CCCC CCCCC. DDDD: DDDDDDDDDD DDDDDDDDD DDDDDDDDDD
DDDDDDDDDD DDDDDD... | <p>The python string function split with the argument of dot would lead to what you wish for.</p>
<p>You can also specify myStr.split("./r/n") to see where you have a dot that followed by a line down or two /r/n for doble line separation.</p> | python|pandas | 0 |
12,818 | 67,180,875 | How to sort a list element in a DataFrame column | <p>I have a dataframe as follows:</p>
<pre><code> venue innings batting_team bowling_team score batsmen
M Chinnaswamy Stadium 1 Kolkata Knight Riders Royal Challengers Bangalore 61 [SC Ganguly, BB McCullum, RT Ponting]
M Chinnaswamy Stadium 2 Roy... | <p>Try</p>
<pre class="lang-py prettyprint-override"><code>df['batsmen'] = df['batsmen'].apply(sorted)
</code></pre>
<p>If you want to reverse the list, you can do</p>
<pre class="lang-py prettyprint-override"><code>df['batsmen'] = df['batsmen'].apply(lambda x: sorted(x, reversed=True))
</code></pre> | python|pandas|dataframe|sorting | 2 |
12,819 | 66,793,574 | AttributeError: module 'tensorflow.keras.metrics' has no attribute 'F1Score' | <p><< I already imported import tensorflow_addons as tfa
when I am running the below code</p>
<pre><code> densenetmodelupdated.compile(loss ='categorical_crossentropy', optimizer=sgd_optimizer, metrics=
['accuracy', tf.keras.metrics.Recall(),
tf.keras.metrics.Precision(),
... | <p>tensorflow_addons 0.16.0 with Tensorflow 2.7.0, <code>tfa.metrics.F1Score</code> works just fine.</p>
<p><strong>Working sample code</strong></p>
<pre><code>import tensorflow_addons as tfa
import numpy as np
metric = tfa.metrics.F1Score(num_classes=3, threshold=0.5)
y_true = np.array([[1, 1, 1],
[... | tensorflow|keras|deep-learning|metrics | 1 |
12,820 | 66,994,208 | numpy pairwise vectorized logic | <p>lets say I have two numpy arrays described by the matrices:</p>
<pre><code>a = [["a","b","c"],["d","e","f"],["g","h","i"]]
b = [[False,False,False],[True,False,False],[False,True,True]]
</code></pre>
<p>And I want to compute a ... | <p>Method <code>np.where()</code> does precisely what you need:</p>
<pre><code>np.where(b, a, None)
#array([[None, None, None],
# ['d', None, None],
# [None, 'h', 'i']], dtype=object)
</code></pre> | python|numpy | 2 |
12,821 | 66,931,667 | Only keep rows between specific time ranges in pandas dataframe | <p>I've got the following dataframe:</p>
<pre><code>activity_level2
Date_and_time ... walking_frame
Date_and_time ...
2020-07-24 23:00:00 2020-07-24 23:00:00 ... 0
2020-07-24 23:01:00 2020-07-24 23:01:00 ... 0
2020-07-24 2... | <p>I would suggest to try this:</p>
<pre class="lang-py prettyprint-override"><code># In case "Date_and_time" column is not already of type 'datetime' in both dfs:
activity_level2["Date_and_time"] = pd.to_datetime(
activity_level2["Date_and_time"], format="%Y-%m-%d %H:%M:%S"
... | python|pandas | 0 |
12,822 | 47,346,272 | Why does scipys stats.bernoulli.rvs yields an array with dtype int32? | <p>I am wondering why <code>scipy</code>s random variable class <code>stats.bernoulli</code> yields ndarrays with dtype <code>int32</code> as samples: </p>
<pre><code> > stats.bernoulli.rvs(0.3, size=10)
array([0, 1, 0, 1, 0, 1, 0, 0, 0, 1]
> stats.bernoulli.rvs(0.3, size=10).dtype
dtype('int32')
</code></pre... | <p>On my system it's <code>int64</code>, so yeah, it's just a default integer size. Why not <code>bool</code> you say. In the source code it uses <code>scipy.hypergeom</code> which returns integers.
The only way I can think of is to pre-initialize your output arrays with <code>dtype=np.bool</code>, if you can. Then alt... | python|numpy|scipy | 1 |
12,823 | 47,206,744 | Convert column float64/int64 to column with float/int as type in pandas dataframe | <p>I wanted to save my pandas dataframe as a Stata file and there seems to be a problem with having columns with <code>int64</code> or <code>float64</code> types and thus need to be converted to standard Python types <code>int</code> and <code>float</code>. I have searched a lot but not found a solution to my problem a... | <p>See the <a href="https://pandas.pydata.org/pandas-docs/stable/io.html#writing-to-stata-format" rel="nofollow noreferrer">IO section of the docs</a>:</p>
<blockquote>
<p>Stata data files have limited data type support; <strong>only strings with 244 or fewer characters, int8, int16, int32, float32 and float64 can b... | python|pandas|types|stata | 1 |
12,824 | 68,063,760 | Pandas multiple condition and get dataframe | <p>I have a datafram df, and i want to get the rows that
a column value equals 1 b 0 c 0 and d 0</p>
<pre><code>df_result = df[df.a == 1 and df.b == 0 and df.c == 0 and df.d == 0]
</code></pre>
<p>It says ;
The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().</p> | <p>Use <code>&</code> instead of <code>and</code> and put brackets around each value test:</p>
<pre><code>df_result = df[(df.a == 1) & (df.b == 0) & (df.c == 0) & (df.d == 0)]
</code></pre>
<p>Alternatively, to avoid using extra brackets, you can use <code>.eq()</code>:</p>
<pre><code>df_result = df[df.... | python|pandas | 3 |
12,825 | 68,294,243 | Formatting print function with list comprehensions | <p>I am trying to write a code where it formats the list comprehension function <code>[print('{:<15} ${:<4f}'.format(Timeframes[counter],x)) for counter,x in enumerate(Compounding)]</code> so that it will print the expected output. I am trying to print the values of <code>Compounding</code> and <code>Non_Compound... | <p>Use <code>zip()</code> to loop over the lists in parallel.</p>
<pre><code>for timeframe, compound, noncompound in zip(TimeFrames, Compounding, NonCompounding):
print(f'{timeframe:<15} ${compound{:<4f} ${noncompund:<4f}')
</code></pre>
<p>There's no need for a list comprehension. <code>print()</code> doe... | python|arrays|numpy|for-loop|printing | 2 |
12,826 | 68,287,778 | Count number of non empty columns and select the one which is non blank - python | <p>I am searching for values in possible columns. I am able to find where there are matches. My challenge then becomes how to select the non empty columns.</p>
<pre><code>import pandas as pd
import numpy as np
data = {"Search1":["one_two","two_ten", "five_ten"],
"S... | <p>If I understand what you are trying to then you can use nested <code>np.where()</code>:</p>
<pre><code>np.where((df['Found1'].str.len() > 0) ^ (df['Found2'].str.len() > 0), \
np.where((df['Found1'].str.len() > 0), df['Found1'], df['Found2'] ), '' )
</code></pre>
<p>Result:</p>
<pre><code>array(['th... | pandas|string|sum | 1 |
12,827 | 68,033,349 | First identical value distance | <p>I want to find the first of the following occurrences of each value. Two "for in" solves. Is there a faster method?</p>
<pre><code>df = pd.DataFrame(columns=list("AB"))
df["A"] = [4,2,4,4,2,5,2,6,1,6,4,5,9,3,7,3,3]
for i, a1 in enumerate(df["A"][:-1]):
for j, a2 in enumer... | <p>IIUC, you can try:</p>
<pre><code>df = (
df.reset_index()
.groupby('A')
.apply(lambda x: x['index'].diff().shift(-1))
.reset_index(0)
.sort_index()
.convert_dtypes()
.rename(columns = {'index': 'B'})
)
</code></pre>
<h4>OUTPUT:</h4>
<pre><code> A B
0 4 2
1 2 3
2 4 ... | python|pandas|list|numpy|dictionary | 0 |
12,828 | 68,103,556 | Python: Converting between nested dictionaries and .csv files (generalised)? | <p>For a project I'm having to parse files, extract needed data to file and then use those files for further analysis. I have a majority of it working dictionary to .csv, but am unsure going .csv -> nested dictionary.</p>
<p><strong>Nested dictionary format</strong></p>
<pre><code>gene_dict[4943] = {'ID': 'SPCC569.0... | <p>Input data:</p>
<pre><code>>>> gene_dict
{4943: {'ID': 'SPCC569.02c',
'startpos': '2432505',
'endpos': '2433520',
'len': 1015,
'direction': 0,
'chromosome': 3,
'intron': 0},
4944: {'ID': 'SPCC569.01c',
'startpos': '2434691',
'endpos': '2436530',
'len': 1839,
'direction': 0,
'chromosom... | python|pandas|csv|dictionary | 0 |
12,829 | 59,466,258 | numpy array to the just number in the array | <p>I got array list looks like <br/></p>
<blockquote>
<p>[array(99.75142857), array(99.79928571), array(99.82238095),
array(99.83857143), array(99.85), array(99.85738095),
array(99.86285714), array(99.86767857)]</p>
</blockquote>
<p>I'm not sure what is this array but I just want to ge a numbers <br/>
[99.7514... | <p>What you here have is a <em>list</em> of numpy arrays. Each array wraps a single element.</p>
<p>You can construct a list of elements, by first wrapping the list in an array:</p>
<pre><code>import numpy as np
<b>np.array(</b>my_data<b>)</b></code></pre>
<p>this will produce an array with eight elements:</p>
<pr... | python|numpy | 0 |
12,830 | 59,058,840 | how can i read 3500 rows from a csv file using pandas? | <p><code>pd.read_csv(...)</code></p>
<ol>
<li>I try by this but it's just read 1520rows. But the main CSV file has 35000 rows.</li>
</ol> | <p>You could simply use the “nrows” field at the read_csv() function:</p>
<pre><code>Import pandas as pd
df = pd.read_csv(“some.csv”,nrows=3500)
</code></pre>
<p>Also you can find more information at this link <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html" rel="nofollow nore... | pandas|data-analysis|large-data | 0 |
12,831 | 59,246,856 | Dataframe is not recognizing the column? | <p>So I have a dataframe where I have different columns of prices of Reliance Stock. I can see that index of the dataframe is Date after I used <code>df.index</code>. However when I use <code>df["Date"]</code>, it gives me an error saying </p>
<blockquote>
<p>AttributeError: 'DataFrame' object has no attribute 'Date... | <p><a href="https://i.stack.imgur.com/RaEnf.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/RaEnf.png" alt="enter image description here"></a>There is no column named Date in the Dataframe. The date you see is the index in the dataframe.
You can add Date column using following line:</p>
<p>data.rese... | python|pandas | 0 |
12,832 | 59,400,632 | How to add multiple columns to dataframe by function | <p>If I have a df such as this:</p>
<pre><code> a b
0 1 3
1 2 4
</code></pre>
<p>I can use <code>df['c'] = ''</code> and <code>df['d'] = -1</code> to add 2 columns and become this:</p>
<pre><code> a b c d
0 1 3 -1
1 2 4 -1
</code></pre>
<p>How can I make the code within a function, so I can apply... | <p>Create a dictionary:</p>
<pre><code>dictionary= { 'c':'', 'd':-1 }
def new_columns(df, dictionary):
return df.assign(**dictionary)
</code></pre>
<p>then call it with your df:</p>
<pre><code>df = new_columns(df, dictionary)
</code></pre>
<p>or just ( if you don't need a function call, not sure what your use ... | python|pandas|function|dataframe|multiple-columns | 2 |
12,833 | 59,061,384 | Given a vector field (dx, dy), move matrix value at position (Row, Col) to new position (Row + dx, Column + dy) | <p>Given a matrix </p>
<pre><code>[ a b - ]
[ - e f ]
[ g h - ]
</code></pre>
<p>where, for the sake of demonstration, - denotes a zero entry. </p>
<p>We also work with a vector field </p>
<pre><code>[ (0,1) (0,1) (0,0) ]
[ (0,0) (0,-1) (0,-1) ]
[ (0,1) (0,1) (0,0) ]
</code><... | <p>This can be done using <code>np.add.at</code>:</p>
<pre><code>A = np.array([["a","b",""],["","c","d"],["e","f",""]])
l,n,r = [[0,-1],[0,0],[0,1]]
B = np.array([[r,r,n],[n,l,l],[r,r,n]])
out = np.zeros_like(A)
i,j = np.ogrid[:3,:3]
np.add.at(out.view('u4'),(i+B[...,0],j+B[...,1]),A.view('u4'))
out
# array([['', 'a... | python|pandas|numpy|image-processing | 1 |
12,834 | 44,890,067 | ValueError: Cannot feed value of shape (128,) for Tensor 'Placeholder_4:0', which has shape '(?, 1161)' | <p>I am facing a Value Error in Tensorflow's placeholder tensor. I have declared it as [None, n_classes] so that it can accept batch of any size. Yet I am facing a ValueError that there is a mismatch with the batch size and the tensor label feed.</p>
<p>Following is the code:</p>
<pre><code>n_inputs = 5000
n_classes ... | <p>As the error says, you are feeding the wrong size to the tensor: <code>labels</code>. <code>labels</code> expects the input to be <code>[batch_size, num_classes]</code> but you are feeding it <code>[batch_size]</code>. Change to <code>labels = tf.placeholder(tf.int32, [None])</code> and use <code>tf.one_hot(labels, ... | python-3.x|tensorflow | 1 |
12,835 | 44,902,983 | How to select the same columns of a numpy array based on indices of a second array | <p>I need to filter dataframes or numpy arrays based on selected columns in a previous one, i.e. filtering the same columns as those in the first array.</p>
<p>Here is my approach:</p>
<p><strong>Excludes zero variables (filtering, selection of columns in the first df)</strong></p>
<pre><code>df_NN_70 = df_NN_70.loc... | <p>You need to filter in the second dimension of the array, e.g.:</p>
<pre><code>df_filtered = np.array(df_NN_x).ix[:, filter_columns]
</code></pre>
<p>or:</p>
<pre><code>df_filtered = np.array(df_NN_x)
df_filtered = df_filtered[df_filtered.columns[filter_columns]]
</code></pre>
<p>Note that the first version inclu... | python|arrays|pandas|numpy|filtering | 0 |
12,836 | 45,149,064 | Numpy row wise masking | <p>I have a numpy array which has NaN values in some locations.</p>
<pre><code>>>> d1
array([[ 0., 1., 2., nan, 4., 5., nan, 7.],
[ 8., 9., 10., nan, 12., 13., nan, 15.],
[ 16., 17., 18., nan, 20., 21., nan, 23.],
[ 24., 25., 26., nan, 28., 29., 30., 31... | <p>Here are few approaches -</p>
<pre><code>np.repeat(~np.isnan(d1).any(1,keepdims=1),d1.shape[1],axis=1)
~np.isnan(d1).any(1,keepdims=1)*([True]*d1.shape[1])
np.tile(~np.isnan(d1).any(1,keepdims=1),d1.shape[1])
np.broadcast_to(~np.isnan(d1).any(1,keepdims=1), d1.shape)
np.broadcast_to(~np.isnan(d1).any(1), d1.shape).... | python|numpy | 5 |
12,837 | 57,214,424 | How to display training progress bar in tensorflow? | <p>I'm trying to output to the terminal the same type of training progress bar that is done with Keras training. I'm new to tensorflow and have not yet tried Keras, but I'm interested in knowing if it can be done without Keras.</p> | <pre><code>import tensorflow as tf
train_data = (...)
progbar = tf.keras.utils.Progbar(len(train_data))
for i, d in enumerate(train_data):
(train model here...)
progbar.update(i) # This will update the progress bar graph.
3714/3715 [============================>.] - ETA: 20s
</code></pre>
<ul>
<li>In Ten... | python|tensorflow | 12 |
12,838 | 57,210,021 | Add special characters in csv pandas python | <p>While writing strings containing certain special characters, such as</p>
<pre><code> Töölönlahdenkatu
</code></pre>
<p>using <strong>to_csv</strong> from <strong>pandas</strong>, the result in the csv looks like</p>
<pre><code> T%C3%B6%C3%B6l%C3%B6nlahdenkatu
</code></pre>
<p>How do we get to write the tex... | <p>What you're trying to do is remove German umlauts and Spanish tildes. There is an easy solution for that.</p>
<pre class="lang-py prettyprint-override"><code>import unicodedata
data = u'Töölönlahdenkatu Adiós Pequeño'
english = unicodedata.normalize('NFKD', data).encode('ASCII', 'ignore')
print(english)
</code></p... | python|pandas|csv|utf-8 | 1 |
12,839 | 57,216,251 | Using pandas.Series.str.get: what is the correct way? | <p>I am following Wes Mckinney's wonderful book to get up to speed with <code>pandas</code>. I however can't seem to get why <code>pandas.Series.str.get</code> won't work. I've looked at a few Github issues and questions on here but none seems to help. </p>
<p><strong>Data</strong></p>
<pre><code>data = pd.Series({'D... | <p>The reason you get a series containing <code>NaN</code>s is because <code>matches</code> is a boolean <code>Series</code>:</p>
<pre><code>In[58]:
matches
Out[58]:
Dave True
Steve True
Rob True
Wes NaN
dtype: object
</code></pre>
<p>So it doesn't make sense to return an element at the ordinal po... | python|python-3.x|string|pandas | 2 |
12,840 | 46,039,400 | .loc for CategoricalIndex in Pandas | <p>I'm trying to access the rows of a CategoricalIndex-based Pandas dataframe using .loc but I get a <code>TypeError</code>. A minimum <strong>non</strong> working example would be</p>
<pre><code>import pandas as pd
df = pd.DataFrame({'foo': rand(3), 'future_index': [22, 13, 87]})
df['future_index'] = df['future_inde... | <p>For me working:</p>
<pre><code>print (df.loc[pd.CategoricalIndex([13])])
foo
future_index
13 2
</code></pre>
<p>But if convert to <code>str</code> as mentioned <a href="https://stackoverflow.com/questions/46039400/loc-for-categoricalindex-in-pandas#comment79039304_46039400">EdChum</... | python|pandas | 3 |
12,841 | 28,686,977 | Read and write fixed format (MODFLOW) text files with Python | <p>I am trying to read, manipulate and write text files using python. These files contain numeric matrices and were generated from a FORTRAN groundwater flow code called MODFLOW, and have an unusual shape, because the matrix rows are split across several file lines so that there are no more than 7 values per line. So a... | <p>I think any Python based file reader that handles the files line by line is going to have similar speed. Pandas supposedly has a faster CSV reader, but I'm not familiar with it. Do you have any sense of where your code is slow? reading the files? parsing? collecting values in a list/array?</p>
<p>For a start I'd... | python|csv|text|matrix|pandas | 0 |
12,842 | 28,511,216 | Pandas gbq load - to_gbq timeout exception | <p>anyone has a clue about this error:</p>
<blockquote>
<p>{u'kind': u'bigquery#tableDataInsertAllResponse', u'insertErrors':
[{u'index': 90, u'errors': [{u'reason': u'timeout'}]},</p>
</blockquote>
<p>I am trying to execute:</p>
<pre><code>from pandas.io import gbq
df.to_gbq(tablename, project_id=projectid)
</... | <p>BigQuery supports partial success on batched insertions. From the reply, it looks like row 90 failed to insert with reason "timeout". See "Success HTTP response codes" at <a href="https://cloud.google.com/bigquery/streaming-data-into-bigquery#troubleshooting" rel="nofollow">https://cloud.google.com/bigquery/streamin... | pandas|google-bigquery | 1 |
12,843 | 50,875,234 | Python - speed up Pandas iteration | <p>Sorry for my bad english.</p>
<p>This is a simplified version of my dataframe:</p>
<pre><code>d = {'League': {5697: 'Premier League', 5695: 'Premier League', 5694: 'Premier League', 5693: 'Premier League', 5692: 'Premier League', 5691: 'Premier League', 5696: 'Premier League', 5689: 'Premier League', 5688: 'Premie... | <p>Use simple python call and remove some of pandas call.</p>
<pre><code>def testTime():
df = pd.DataFrame.from_dict(d)
#print(df)
#print(d.keys())
lstOverdue = []
lstTeams = [team for team in df.HomeTeam.unique()]
for team in lstTeams:
mask = (df.HomeTea... | python|pandas|numpy|vectorization | 1 |
12,844 | 33,427,688 | converting time to Hz in python | <p>I have a dataset consists of timestamp(ms), x , y and z. I want to transform it to the frequency domain (Fourier). I used <code>numpy.fft.fft(a, n=None, axis=-1, norm=None)[source]</code></p>
<p>my code is </p>
<pre><code>import panda as pd
from scipy.fftpack import fft
import matplotlib.pyplot as plt
data=pd.re... | <p>From <a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.fft.fftfreq.html#numpy.fft.fftfreq" rel="nofollow"><code>numpy.fft.fftfreq</code>'s reference</a>:</p>
<blockquote>
<p>The returned float array f contains the frequency bin centers in cycles per unit of the sample spacing (with zero at the st... | python|numpy|import|fft | 0 |
12,845 | 33,462,401 | Pandas: expand list in column to distinct rows | <p>I have a dataset with a big number of columns that contain several values (imported from google forms, those are columns allowing multiple selection). I've imported those as lists initially.</p>
<p>Now I want to analyse the data based on some values from those columns, i.e. given</p>
<pre><code>df = pd.DataFrame(d... | <p>You can do this by summing the columns (basically concatenating the contents) and then calling <code>pd.value_counts</code> on them. For example (modifying your dataframe definition slightly so it will not raise an error):</p>
<pre><code>df = pd.DataFrame(dict(a=[(1,2),(2,3),(1,)],
b=[(1,3),(... | python|pandas | 1 |
12,846 | 9,026,331 | numpy subclass will not accept arguments to __new__ from pythonically inheriting class | <p>I've created a subclass of ndarray called "Parray" which takes two arguments: p, and dimensionality. It works fine on its own. Now, I want to create a class called SirPlotsAlot, which inherits Parray without all the fancy <strong>new</strong> and <strong>array_finalize</strong> etc.</p>
<pre><code>import numpy as n... | <p>It's been ten years and I long left the project, but I resolved this issue by creating helper functions to create new classes and set them up. In the code example below, see the definitions at the bottom of the file. I imported and used those.</p>
<p>Props to Matthew Schinckel to pointing out that <code>__new__</cod... | python|inheritance|numpy|attributeerror|default-arguments | 1 |
12,847 | 6,086,289 | Can anyone please explain how this python code works line by line? | <p>I am working in image processing right now in python using numpy and scipy all the time. I have one piece of code that can enlarge an image, but not sure how this works.</p>
<p>So please some expert in scipy/numpy in python can explain to me line by line. I am always eager to learn.</p>
<pre><code>import numpy as ... | <p>First, a grid of empty points is created with point per pixel.</p>
<pre><code>x, y = N.meshgrid(N.arange(img.shape[1]), N.arange(img.shape[0]))
</code></pre>
<p>The actual image pixels are placed into the variable <code>pts</code> which will be needed later.</p>
<pre><code>pts = N.column_stack((x.ravel(), y.ravel... | python|image-processing|numpy|scipy | 5 |
12,848 | 66,442,309 | AttributeError: 'Node' object has no attribute 'input_masks' | <p>I created a network, but got the error:
AttributeError: in user code:</p>
<pre><code>C:\Users\LocalAdmin\.conda\envs\newenvt\lib\site-packages\keras_contrib\metrics\crf_accuracies.py:23 crf_viterbi_accuracy *
mask = crf._inbound_nodes[idx].input_masks[0]
AttributeError: 'Node' object has no attribute 'input_ma... | <p>I have encountered the same problem as you. I still have problems after replacing the new version of CRF.
got a error:
TypeError: Value passed to parameter 'x' has DataType bool not in list of allowed values: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, float16, uint32, uint64</p> | python|tensorflow|keras | 0 |
12,849 | 66,661,375 | Significantly higher outcomes in stock price predicting LSTM model than expected | <p>I have made my LSTM model to estimate next day's stock prices. I have used tensorflow and keras.</p>
<p>However, I do not understand why my model's predicted price is almost always 2 or 3 factors higher than the current stock price. Is there anybody who knows what I am doing wrong?</p>
<p>The code is shown below:</p... | <p>You are using a min max scaler between <code>0</code> and <code>1</code>, where the defined highs are historical. Your LSTM model will predict a new high and when you <code>inverse_transform</code> the prediction, it will likely be higher than the min and max that has been fitted to the scaler.</p>
<p>Therefore the ... | python|tensorflow|keras|lstm | 0 |
12,850 | 16,274,954 | Filtering with itemgetter and numpy arrays | <p>I'm getting the following error:</p>
<pre><code>Traceback (most recent call last):
File "calibrating.py", line 160, in <module>
intrinsic = calibrate2(corners, cb_points, (640,480))
File "calibrating.py", line 100, in calibrate2
valid_corners = filter(itemgetter(0), image_corners)
ValueError: The ... | <p>The <em>dtype</em> attribute isn't accessible by <em>itemgetter</em>.</p>
<p>Try this filter instead:</p>
<pre><code>filter(lambda arr: arr.dtype != float32, image_corners)
</code></pre>
<p>That will give you all the matricies without <code>dtype==float32</code>.</p> | python|arrays|matrix|numpy | 1 |
12,851 | 16,092,557 | How to check that a matrix contains a zero column? | <p>I have a large matrix, I'd like to check that it has a column of all zeros somewhere in it. How to do that in numpy?</p> | <p>Here's one way:</p>
<pre><code>In [19]: a
Out[19]:
array([[9, 4, 0, 0, 7, 2, 0, 4, 0, 1, 2],
[0, 2, 0, 0, 0, 7, 6, 0, 6, 2, 0],
[6, 8, 0, 4, 0, 6, 2, 0, 8, 0, 3],
[5, 4, 0, 0, 0, 0, 0, 0, 0, 3, 8]])
In [20]: (~a.any(axis=0)).any()
Out[20]: True
</code></pre>
<p>If you later decide that you n... | python|numpy | 18 |
12,852 | 57,674,274 | Poor performance transfer learning ResNet50 | <p>I have a dataset of 11k images labeled for semantic segmentation. About 8.8k belong to 'group 1' and the rest to 'group 2'</p>
<p>I am trying to simulate what would happen if we lost access to 'group 1' imagery but not a network trained from them.</p>
<p>So I trained ResNet50 on group 1 only. Then used that networ... | <p>I have read a few articles about the same topic - i have 12k jpeg images from 3 classes and after 3 epochs the accuracy dropped to 0. I am awaiting delivery of a new graphics card to improve performance (it's currently taking 90 - 120 minutes per epoch) and hope to give more feedback. I am just wondering if the fa... | python|tensorflow|keras|deep-learning | 0 |
12,853 | 57,366,308 | Appending Data from Loop into Dictionary or Series for new dataframe | <p>I'm trying to convert parsed date times with tz_localize and tz_convert in a loop. What I'd like to do is take each converted timestamp and write it into a dictionary, series, or new dataframe and then join it to the existing dataframe. </p>
<p>Looking at other threads it seems like appending data directly into the... | <pre><code>import pandas as pd
data = pd.read_csv('lab.csv')
data['Site DOWN'] = pd.to_datetime(data['Site DOWN'])
data['Site UP'] = pd.to_datetime(data['Site UP'])
</code></pre>
<h3><code>DataFrame</code>:</h3>
<p><a href="https://i.stack.imgur.com/VcK1d.png" rel="nofollow noreferrer"><img src="https://i.stack.img... | python|pandas|date|datetime | 0 |
12,854 | 24,232,701 | Pandas diff() functionality on two columns in a dataframe | <p>I have a data frame in which column A is the start time of an activity and column B is the finish time of that activity, and each row represents an activity (rows are arranged chronologically). I want to compute the difference in time between the end of one activity and the start of the next activity, i.e. df[i+1][... | <p>You can shift <code>A</code> column first:</p>
<p><code>df['A'].shift(-1) - df['B']</code></p> | python|python-2.7|pandas|offset | 6 |
12,855 | 43,552,785 | Merge table on either of the 2 columns in pandas | <p>I am working on python to merge a table using pandas, but I am having little trouble. Here's the problem. </p>
<p>I have 2 tables_A and table_B. I have two columns on table_A say "one", "two". I have two column on table_B say "one","three". column "one" in table_B has some values which maps to column "one" in table... | <p>Consider a concatenation with merge which would translate your SQL query as <code>OR</code> is often analogous to a <code>UNION</code>:</p>
<pre><code>pd.concat([pd.merge(table_A, table_B, on='one'),
pd.merge(table_A, table_B, left_on='two', right_on='one')])
</code></pre> | python|mysql|pandas|merge | 7 |
12,856 | 43,571,202 | Neural network performance optimization | <p>I am trying to classify medical report based on the symptoms mentioned in the report. Steps i am doing are</p>
<p>1) Extract symptoms from each medical report.</p>
<p>2) Create a Set of all the symptoms extracted from all medical reports, total terms so far are 3700.</p>
<p>3) Create a set of all diseases diagnos... | <p>There aren't enough details understand you problem and implementation
but a good starting point that will help you understand if you have a more technical issue (bug, network architecture, etc...) or a data issue is to create synthetic data that you know should fit your model (fake some diseases, each with a set of ... | machine-learning|tensorflow|neural-network|gradient-descent|multilabel-classification | 1 |
12,857 | 43,485,469 | Apply textblob in for each row of a dataframe | <p>i have a data frame with a col which has text. I want to apply textblob and calculate sentiment value for each row.</p>
<pre><code>text sentiment
</code></pre>
<p>this is great<br>
great movie
great story </p>
<p>When i execute the below code:</p>
<p><code>df['sentiment'] = list(map(lambda tweet:... | <p>You can use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.apply.html" rel="noreferrer">.apply</a>:</p>
<pre><code>df['sentiment'] = df['text'].apply(lambda tweet: TextBlob(tweet).sentiment)
</code></pre>
<p>Sentiment returns a namedtuple of the form Sentiment(polarity, subjectivit... | python|pandas|textblob | 25 |
12,858 | 43,702,324 | solving non linear problems in python | <p><a href="https://i.stack.imgur.com/iReR3.png" rel="nofollow noreferrer">in last equation i need to solve for q.</a> <a href="https://i.stack.imgur.com/zTPIz.png" rel="nofollow noreferrer">Here is the problem from miranda feckler , I need to develop equivalent python code</a> If my function is based on many variable... | <p><code>broyden1(resid(co, p_node, q), co)</code>
breaks because the term <code>resid(co, p_node, q)</code> gets evaluated (returning an array) before passing into the function.</p>
<p><code>broyden1(resid, co)</code>
breaks because when broyden1 evaluates it calls resid(co) which is clearly not well defined. You wan... | python|python-3.x|numpy|scipy | 0 |
12,859 | 73,093,901 | Transform DataFrame in Pandas | <p>I am struggling with the following issue.</p>
<p>My DF is:</p>
<pre><code>df = pd.DataFrame(
[
['7890-1', '12345N', 'John', 'Intermediate'],
['7890-4', '30909N', 'Greg', 'Intermediate'],
['3300-1', '88117N', 'Mark', 'Advanced'],
['2502-2', '90288N', 'Olivia', 'Elementary'],
['7890-2', '22345N', ... | <p>I'd start with the same approach as @Andrej Kesely but then sort by index after unstacking and <code>map</code> over the column names with <code>' '.join</code>.</p>
<pre class="lang-py prettyprint-override"><code>df[["Id", "No"]] = df["Id"].str.split("-", expand=True)
df_wide... | python|pandas|group-by | 1 |
12,860 | 73,150,265 | How can I create a list of the number of times the same number appears in column B? | <p>Let say I have a dataframe :</p>
<pre><code>A B
1 1401
2 1401
3 1401
4 1601
5 2201
6 2201
7 6401
8 6401
9 6401
10 6401
</code></pre>
<p>I would like to obtain this ouput:</p>
<pre><code>L1 = [1401, 1601, 2201, 6401]
L2 = [3, 1, 2, 4] (the number of times the same number app... | <p>Answer :</p>
<pre><code>df.B.value_counts().values
</code></pre> | python|pandas|dataframe | 0 |
12,861 | 70,594,634 | Pandas boxplot compare all data from column with filtered version of same column | <p>This seem like it should be easy, but can not seem to get it working.</p>
<pre><code>data = {'Name':['Tom', 'nick', 'krish', 'jack', 'Tom', 'nick', 'krish', 'jack'],
'Age':[31, 46, 21, 37, 31, 46, 21, 37],
'Times':[20, 21, 19, 18, 19, 20, 20, 19]}
df = pd.DataFrame(data)
df
# basic boxplot for 'Tim... | <p>You simply pass a list to <code>plt.boxplot()</code>:</p>
<pre class="lang-py prettyprint-override"><code>box = plt.boxplot([df['Times'], df[df['Name'] == 'Tom']['Times']],
labels=['all','Toms'])
</code></pre>
<p><a href="https://i.stack.imgur.com/Trk4Y.png" rel="nofollow noreferrer"><img src="http... | python|pandas|boxplot | 3 |
12,862 | 70,682,298 | pandas filter rows based on atmost matching criteria | <p>I have a dataframe like as shown below</p>
<pre><code>df=pd.DataFrame({'subjects':['A','A','D','B','B','C'],
'B':['12','12','13','14','14','16'],
'C':[21,23,24,25,26,27]
})
df['r_no'] = df.groupby(['subjects','B']).cumcount()+1
</code></pre>
<p>Now, I would like to onl... | <pre><code>df[df.groupby('subjects')['r_no'].transform(lambda x: ~(x.ne(1).any()))]
subjects B C r_no
2 D 13 24 1
5 C 16 27 1
</code></pre> | python|pandas|dataframe|numpy|filter | 1 |
12,863 | 70,457,034 | Flatten complex json using Databricks and ADF | <p>I have following json which I have flattened partially using explode</p>
<pre><code>{
"result":[
{
"employee":[
{
"employeeType":{
"name":"[empName]",
"displayName":"theNa... | <p>Since you see they won't be dynamic. You can traverse through the <code>json</code> while mapping like as below. Just identify record and array, specify <code>index [i]</code> as needed.</p>
<p>Example:</p>
<pre><code>id --> $['employee'][1]['groupValue'][0]['id']
name --> $['employee'][1]['groupValue'][0]['... | pandas|azure|azure-data-factory|azure-data-factory-2|azure-databricks | 1 |
12,864 | 70,404,605 | Evaluate SMOTE and RandomUnderSampling different strategies | <p>I am working in pandas in Python with a data frame <code>df</code>. I am carrying out a classification task and have two imbalanced classes <code>df['White']</code> and <code>df['Non-white']</code>. For this reason, I have built a pipeline that includes both SMOTE and RandomUnderSampling.</p>
<p>This is what my pipe... | <p>Below is an example of how you could compare the classifier's accuracy for different parameter combinations using 5-fold cross-validation and visualize the results.</p>
<pre class="lang-py prettyprint-override"><code>import pandas as pd
import seaborn as sns
from sklearn.datasets import make_classification
from skle... | python|pandas|machine-learning|scikit-learn | 1 |
12,865 | 42,790,542 | find row positions and column names of cells contanining inf in pandas dataframe | <p>How can I retrieve the column names and the rows of all the cells that contain inf in a multicolumns panda datarame <code>df</code>?</p>
<p>I have tried </p>
<pre><code>inds = np.where(np.isinf(df)==True)
</code></pre>
<p>but I dont have the expected result</p> | <p>row positions:</p>
<pre><code>df.index[np.isinf(df).any(1)]
</code></pre>
<p>column names:</p>
<pre><code>df.columns.to_series()[np.isinf(df).any()]
</code></pre>
<p>Demo:</p>
<pre><code>In [163]: df
Out[163]:
minor AAPL GS
Adj Close Volume Adj Close ... | python|pandas|dataframe | 28 |
12,866 | 42,812,105 | Titles of subplots are shown when kind='bar' but not when kind='line' | <p>When I create subplots like this:</p>
<pre><code>import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import itertools
df = pd.DataFrame(np.random.randn(20, 10), columns=list('ABCDEFGHIJ'))
ax = df.plot(kind='bar', subplots=True, layout=(4, 3), sharex=True, sharey=False)
</code></pre>
<p>I get ... | <p>A workaround: </p>
<ol>
<li>Get the current <a href="https://github.com/pandas-dev/pandas/blob/998c801f76256990b98d3f0d2ad885ae27c955a1/pandas/tools/plotting.py" rel="nofollow noreferrer">development version of <code>plotting.py</code></a>. This allows to provide lists to the <code>titles</code> argument of <code>p... | python|pandas|matplotlib|subplot | 2 |
12,867 | 27,260,534 | Scaling Amplitude After Windowing FFT to Recover Correct Amplitude | <p>I am trying to apply a Hann window to a sinusoidal signal with the idea of applying an FFT to recover the frequency and the amplitude. This is a canonical case I have created to increase my understanding before I move onto my data (real time signal where I want to accurately determine the frequency content and ampl... | <p>The mean value of the von Hann window is (approximately) 0.5, for <code>N=1000</code> you have</p>
<pre><code>>>> N=1000 ; print sum(np.hanning(N))/N
0.4995
>>>
</code></pre>
<p>Does this explain the necessity of multiplying by two to recover the discrete amplitudes?</p> | python|numpy|signal-processing|fft | 3 |
12,868 | 25,349,693 | Change FaceColor and EdgeColor of Matplotlib Streamplot Arrows | <p>I have some data in grid and I plot the streamlines with streamplot with color and width related to the speed. How can I change the color of the arrows, or just the edgecolors?
My goal is to emphasis the stream direction. If someone have another way to do it..</p>
<p>I tried to do it using <code>c.arrows</code>, ed... | <p>(Beware, the analysis below may not be completely correct, I only took a cursory look into the source.)</p>
<p>It seems that <code>streamplot</code> does two things when it creates the arrows:</p>
<ul>
<li>adds arrow patches (type <code>FancyArrowPatch</code>) to the axes</li>
<li>adds the same arrow patches to th... | python|numpy|matplotlib | 4 |
12,869 | 25,320,987 | Fermi Surface Plots | <p>Essentially I am trying to do a Fermi Surface plot, in 2D. i.e. a 2D cut of f(n,vec_k)=e_f for some plane in K-space, with interpolation. Specifically, I have a numpy array: Eigen, with shape,</p>
<p>Eigen.shape = (100,100,100,10), where the first three indices are over the vector vec_k, and the third is the band... | <p>This is generally solved using a marching cube algorithm. You should look into <code>contour3d()</code> function of <code>MLab</code> here: <a href="http://docs.enthought.com/mayavi/mayavi/auto/mlab_helper_functions.html#mayavi.mlab.contour3d" rel="nofollow">http://docs.enthought.com/mayavi/mayavi/auto/mlab_helper_f... | python|numpy|3d|interpolation|surface | 2 |
12,870 | 30,493,614 | Pandas merge dataframes based on closest match | <p>I have the following 2 dataframes (df_a,df_b):</p>
<pre><code>df_a
N0_YLDF
0 11.79
1 7.86
2 5.78
3 5.35
4 6.32
5 11.79
6 6.89
7 10.74
df_b
N0_YLDF N0_DWOC
0 6.29 4
1 2.32 4
2 9.10 4
3 4.89 4
4 10.22 4
5 3.80 3
6 5.55 3
7 6.36 3
</code></pre>
<p>I wo... | <p>Another way is to do an subtract all pairs in the cartesian product and get the index of minimum absolute value for each one:</p>
<pre><code>In [47]:ix = abs(np.atleast_2d(df_a['N0_YLDF']).T - df_b['N0_YLDF'].values).argmin(axis=1)
ix
Out[47]: array([4, 2, 6, 6, 0, 4, 7, 4])
</code></pre>
<p>Then do </p>
... | python|pandas | 3 |
12,871 | 26,667,524 | pandas transform dataframe pivot table | <p>I can transform the following dataframe:</p>
<pre><code> VALUE COUNT RECL_LCC RECL_PI
0 1 15,686,114 3 1
1 2 27,537,963 1 1
2 3 23,448,904 1 2
3 4 1,213,184 1 3
4 5 14,185,448 3 2
5 6 13,064,600... | <p>According to comments, I think you can do that like following. Note that I converted the COUNT column to integers to do this :</p>
<pre><code>#convert strings of the COUNT column to integers
import locale
locale.setlocale( locale.LC_ALL, 'en_US.UTF-8' )
LCC_PI_df.COUNT = LCC_PI_df.COUNT.apply(locale.atoi)
plot_ta... | python|pandas|pivot | 3 |
12,872 | 39,286,058 | How to check if 2-D array is in another 2-D array | <p>consider the two dataframes <code>df1</code> and <code>df2</code></p>
<pre><code>df1 = pd.DataFrame(np.zeros((6, 6)), list('abcdef'), list('abcdef'), dtype=int)
df1.iloc[2:4, 2:4] = np.array([[1, 2], [3, 4]])
df1
</code></pre>
<p><a href="https://i.stack.imgur.com/UEWbP.png" rel="nofollow noreferrer"><img src="ht... | <p>Assuming the dataframes contain <code>0's</code> and <code>1s</code> only, you can use <a href="http://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.convolve2d.html" rel="nofollow noreferrer"><code>2D convolution</code></a> and look if any element in the convoluted output is equal to the number of elemen... | python|pandas|numpy|scipy | 3 |
12,873 | 12,860,421 | How to aggregate unique count with pandas pivot_table | <p>This code:</p>
<pre><code>df2 = (
pd.DataFrame({
'X' : ['X1', 'X1', 'X1', 'X1'],
'Y' : ['Y2', 'Y1', 'Y1', 'Y1'],
'Z' : ['Z3', 'Z1', 'Z1', 'Z2']
})
)
g = df2.groupby('X')
pd.pivot_table(g, values='X', rows='Y', cols='Z', margins=False, aggfunc='count')
</code></pre>
<p>returns the fo... | <p>Do you mean something like this?</p>
<pre><code>>>> df2.pivot_table(values='X', index='Y', columns='Z', aggfunc=lambda x: len(x.unique()))
Z Z1 Z2 Z3
Y
Y1 1 1 NaN
Y2 NaN NaN 1
</code></pre>
<p>Note that using <code>len</code> assumes you don't have <code>NA</code>s in your DataFrame.... | python|pandas|pivot-table | 125 |
12,874 | 29,281,815 | Pandas Select DataFrame columns using boolean | <p>I want to use a boolean to select the columns with more than 4000 entries from a dataframe <code>comb</code> which has over 1,000 columns. This expression gives me a Boolean (True/False) result: </p>
<pre><code>criteria = comb.ix[:,'c_0327':].count()>4000
</code></pre>
<p>I want to use it to select only the <co... | <p>What is returned is a Series with the column names as the index and the boolean values as the row values.</p>
<p>I think actually you want:</p>
<p>this should now work:</p>
<pre><code>comb[criteria.index[criteria]]
</code></pre>
<p>Basically this uses the index values from criteria and the boolean values to mask... | python|pandas | 42 |
12,875 | 29,267,573 | what is wrong with this code ? pandas python | <p>class stock: </p>
<p>def <strong>init</strong>(self,ticker):
self.ticker = ticker
con = lite.connect(".//stocks.db")
self.data = pd.read_sql("SELECT * FROM daily where ticker = '" + ticker + "' ORDER BY datum DESC LIMIT 100",con,index_col="datum")
con.close()</p>
<p>def data(self):
pr... | <p>One of your first errors is you did not close your SQLite connection.</p>
<p>in <code>__init__</code>:</p>
<pre><code>con.close() # instead of only con.close
</code></pre>
<p>Your other issue is that <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_sql.html" rel="nofollow"><code>pandas.r... | python|pandas | 0 |
12,876 | 33,900,842 | tensorflow and bazel "undefined reference to" compilation error | <p>I am writing a C++ code in tensorflow framework and I want to use a dynamic library written using makefile.
In source code I put the path to header file:</p>
<pre><code>#include "tensorflow/cc/include/libtrading/proto/tf_fix_client.h"
</code></pre>
<p>to use a function called fix_client(int argc, char **argv)
and ... | <p>Bazel requires all dependencies to be declared, so the TensorFlow library should be in your deps attribute. It looks like it is not the case in your target (especially the flag for tensorflow includes is out of place).</p>
<p>After a quick look at TensorFlow build file I would say it needs the following deps attrib... | c++|tensorflow|bazel | 2 |
12,877 | 23,767,668 | Populate large files into sqlite from Python | <p>I am new to Python and Sqlite. I run large neural network simulations and I store the spikes as ASCII file(here named spikeTimes.csv ) with 2 columns, First column is the spike time and the second the Neuron Id. Each simulation run has a different parameter (call it theta). I want to populate the database such that ... | <p>Use a transaction.</p>
<pre><code>with spkDB.transaction():
for k in unique(st[:, 1]):
tmp = SimData.create(neuronId = k)
tmp.save()
for m in st[st[:, 1] == k, 0]:
tmpSt = SpikeTimes.create(spkNeuronId = tmp, theta = curTheta, spkTimes = m)
tmpSt.save()
</code></p... | python|sqlite|numpy | 1 |
12,878 | 23,704,622 | pandas, apply string operation to column should be string type, but has missing values (np.nan) | <p>I have a pandas dataframe df, one of df's column "names" is a Series of strings, where I use numpy.nan for missing values. </p>
<p>When I try to truncate each string in that column based on certain condition: </p>
<pre><code>trunc = lambda s: s[:-10] if cond1 else s
df.names = df.names.apply(trunc)
</code></pre>
... | <p><code>df.names</code> is a <code>pd.Series</code>, and <a href="http://pandas.pydata.org/pandas-docs/stable/api.html#string-handling" rel="nofollow"><code>pd.Series</code> have string methods</a> accessible through its <code>str</code> attribute:</p>
<pre><code>df.loc[cond1, 'names'] = df.loc[cond1, 'names'].str.sl... | python|string|pandas|missing-data | 2 |
12,879 | 23,672,414 | Pandas Pivot MultiIndex efficiently | <p>I'm working with ~300 MB financial data in Pandas, that corresponds to the limit orders in an auction. It is multi-dimensional data, and looks like this:</p>
<pre><code> bid ... | <p>Unstack essentially creates an enumeration of index x columns so it can create a huge memory space when you have a lot of columns and rows.</p>
<p>Here is a soln, that is slower, but should have a much lower peak memory usage (I think). It gives a slightly smaller total space, in that you may have some zero entries... | python|pandas | 1 |
12,880 | 22,604,564 | Create Pandas DataFrame from a string | <p>In order to test some functionality I would like to create a <code>DataFrame</code> from a string. Let's say my test data looks like:</p>
<pre><code>TESTDATA="""col1;col2;col3
1;4.4;99
2;4.5;200
3;4.7;65
4;3.2;140
"""
</code></pre>
<p>What is the simplest way to read that data into a Pandas <code>DataFrame</code>?... | <p>A simple way to do this is to use <a href="https://docs.python.org/2/library/io.html#io.StringIO" rel="noreferrer"><code>StringIO.StringIO</code> (python2)</a> or <a href="https://docs.python.org/3/library/io.html#io.StringIO" rel="noreferrer"><code>io.StringIO</code> (python3)</a> and pass that to the <a href="http... | python|string|pandas|csv|csv-import | 737 |
12,881 | 29,345,093 | Python: DataFrame constructor not properly called | <p>So im walking thourhg the pandas manual and I dont understand what im doing wrong. Could somebody please help? I mean this is standard code from the manual, except that I use a different random function I believe.</p>
<p><a href="http://pandas.pydata.org/pandas-docs/dev/advanced.html" rel="nofollow">http://pandas.p... | <p>pandas is using <code>np.random.randn</code> which returns an array. You are passing a single float value using <code>random.random()</code>:</p>
<pre><code>In [49]: import numpy as np
In [50]: randn = np.random.randn
In [51]: randn(8)
Out[51]:
array([ 1.5530158 , -0.08940148, 0.10467891, -0.05558743, -0.90833863... | python|pandas|dataframe | 3 |
12,882 | 62,219,337 | Creating pandas DataFrame columns from dictionary | <p>I've created a df which looks as follows:</p>
<pre><code> a b 1 2 3 4 5 6
0 x NaN . . . . . NaN
1 y . . .
2 . . . .
. . . . . NaN . . .
. . . . .
. . . ... | <p>You can use pandas <code>apply</code> method to loop over rows in the main dataframe and then extract the inside dataframes to new columns. Before that you need to initialize those new columns. Here is a minimal example of what you could do:</p>
<pre class="lang-py prettyprint-override"><code>import numpy as np
imp... | python|pandas|dataframe | 0 |
12,883 | 62,358,077 | How to modify a DataFrame's List to get Mean, Length and number of NaN's, while deleting the List? | <p>I need to convert a Dataframe's information in a certain way.
Here is an example I made up to illustrate my problem. The original DataFrame looked like this:</p>
<pre><code>Ethnicity Employed Weight Gender
1 1 NaN 1
3 0 ... | <p>You can use <code>df.apply</code>:</p>
<pre><code>In [3103]: res = df.groupby('Ethnicity').agg(list)
In [3104]: res ... | python|pandas|dataframe | 3 |
12,884 | 62,063,372 | creating a new dataframe using boolean masks | <p>I have a dataframe containing text in a column called <code>text</code> and the respective language in which the text is written stored in the column <code>lang</code>. What I am trying to do is create a secondary dataframe containing only the text wrritten in english(so has the value <code>en</code> in the <code>la... | <p>Here <code>DataFrame</code> constructor is not necessary, filter by mask for <a href="http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#boolean-indexing" rel="nofollow noreferrer"><code>boolean indexing</code></a> and by columns names in list by <a href="http://pandas.pydata.org/pandas-docs/stable... | python|pandas|dataframe | 1 |
12,885 | 62,327,791 | Create a new column based off values in two others | <p>I'm trying to merge two columns. Merging is not working out and I've been reading and asking questions for two days trying to find a solution so I'm going to go a different route.
<br>
Since I have to change the column name after I merge anyway why not just create a new column and fill it based on the other two. <b... | <p>Does this answer your question:</p>
<pre><code>df['A']=df.apply(lambda x: x['B'] if x['A']=='-' else x['A'],axis=1)
</code></pre> | python|pandas|fillna | 3 |
12,886 | 62,292,034 | Does it make sense to maximize both training and validation accuracy? | <p>While training my CNNs I usually aim to maximize the <strong>validation</strong> accuracy to 1.0 (i.e. 100%). I know that on the other hand it would not make much sense to aim for a <strong>training</strong> accuracy of 1.0, because we don't want our model to memorize the training data itself.</p>
<p>However, what ... | <p>Let's first address what the purpose of validation is:</p>
<p>When we're training a neural net, we are trying to teach the neural net to perform well at a given task for the entire population of input/output pairs in the task. However, it is unrealistic to have the entire dataset, especially for high dimensional in... | tensorflow|keras|deep-learning|neural-network|conv-neural-network | 1 |
12,887 | 62,341,683 | Print out a specific set of rows of a dataset based on conditions | <p>What I am trying:</p>
<pre><code>import re
new_df = census_df.loc[(census_df['REGION']==1 | census_df['REGION']== 2) & (census_df['CTYNAME'].str.contains('^Washington[a-z]*'))& (census_df['POPESTIMATE2015']>census_df['POPESTIMATE2014'])]
new_df
</code></pre>
<p>It returns this error:</p>
<pre><code>Val... | <p>You need to set brackets around each logical expression in filt_1:</p>
<pre><code>filt_1 = (census_df['REGION'] == 1) | (census_df['REGION'] == 2)
</code></pre>
<p>Note that my data for census_df is semi-fictitious but shows the functionality. Everything from the filt_1 assignment operation and downwards will sti... | python|pandas|dataframe|data-science | 1 |
12,888 | 62,138,620 | What is the best practice for looping through a dictionary of pandas dataframes and making modifications? | <p>I have a dictionary of DataFrames with the key referring to the year of the data. I would like to iterate through the dict and make modifications to the DataFrames. I make modifications to both the column names and the contents of the dfs.</p>
<pre><code>for year, df in df_data.items():
cols = df .columns
n... | <p>As @juanpa.arrivillaga describes above, <code>drop_duplicates</code> <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop_duplicates.html" rel="nofollow noreferrer">returns a dataframe</a>, which you're assigning to the local variable <code>df</code>. Consider the following examp... | python|pandas | 1 |
12,889 | 51,210,090 | How to return same data frame in 'for loop' after passing some function on it, without appending etc.? | <p>I have three dataframes that I would like to crop, I have defined a function;</p>
<pre><code>def croping(data, start_date='2017-04-10 00:00:00', end_date='2018-05-31 21:55:00' ):
return data.loc[start_date:end_date]
</code></pre>
<p>I know this is a bit extra but I am trying to learn how to use user-defined fu... | <p>You have a couple of options. You can index your dataframes by location in a list. In this case, you can use a list comprehension. Using <a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.pipe.html" rel="nofollow noreferrer"><code>pd.DataFrame.pipe</code></a> would be the Pandorable met... | python|list|pandas|dictionary|dataframe | 3 |
12,890 | 51,373,919 | The purpose of introducing nn.Parameter in pytorch | <p>I am new to Pytorch and I am confused about the difference between <code>nn.Parameter</code> and <code>autograd.Variable</code>. I know that the former one is the subclass of <code>Variable</code> and has the gradient. But I really don't understand why we introduce <code>Parameter</code> and when we should use it?<... | <p>From the documentation:</p>
<p><code>Parameters</code> are <code>Tensor</code> subclasses, that have a very special property when used with <code>Module</code>s - when they’re assigned as <code>Module</code> attributes they are automatically added to the list of its parameters, and will appear e.g. in <code>paramet... | python|neural-network|deep-learning|pytorch | 5 |
12,891 | 48,153,251 | scipy.optimize.basinhopping. Object function with argument(s) | <p>So, I have a function f=(x,a,b), which I want to minimize using scipy.optimize.basinhopping. x - is the variable I'm optimizing over and a,b are parameters. It's not really clear how to pass values of a,b to the object function. Or this is not possible? For example, in scipy.optimize.minimize there is a special par... | <p>What's unclear about the <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.basinhopping.html" rel="nofollow noreferrer">docs</a>?</p>
<blockquote>
<p>func : callable f(x, *args)</p>
<p>Function to be optimized. args can be passed as an optional item in the dict minimizer_kwargs</p>
</block... | python|numpy|scipy | 2 |
12,892 | 48,703,761 | Resample panda time series to have the end time stamp for the bin name? | <p>I generate a sample 5 minute time series:</p>
<pre><code>index = pd.date_range('1/1/2000', periods=10, freq='5T')
data=range(10)
ser = pd.Series(data, index=index)
</code></pre>
<p>What it looks like:</p>
<pre><code>2000-01-01 00:00:00 0.0
2000-01-01 00:05:00 1.0
2000-01-01 00:10:00 2.0
2000-01-01 00:15:... | <p>You can use the <code>label</code> argument in <a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.resample.html#pandas.DataFrame.resample" rel="nofollow noreferrer">resample</a>,</p>
<pre><code>ser.resample('15T', label='right', closed='right').mean()
</code></pre>
<p>this shifts the ... | python|pandas | 2 |
12,893 | 48,592,731 | Creating a random matrix in python | <p>I am trying to create a random square matrix of nxn random numbers with numpy. Of course I am able to generate enough random numbers but I am having trouble using numpy to create a matrix of variable length. This is as far as I have gotten thus far:</p>
<pre><code>def testMatrix(size):
a = []
for i in range... | <p>Try </p>
<p><code>np.random.randint(0, 5, size=(s, s))</code></p> | python|numpy | 4 |
12,894 | 70,794,682 | Compile time distribution strategy issue | <p>i have following code- which tries to implement simple Reinforcement learning environment with keras</p>
<pre><code>import gym
from gym import Env
import numpy as np
from gym.spaces import Discrete,Box
import random
#create a custom class
class ShowerEnv(Env):
def __init__(self):
self.action_space =Discr... | <p>i found solution - instead of declaring model before the putting it to the DQNAgent, i just use functional form like this</p>
<pre><code>dqn = build_agent(build_model(states,actions), actions)
dqn.compile(optimizer=Adam(learning_rate=1e-3), metrics=['mae'])
dqn.fit(env, nb_steps=50000, visualize=False, verbose=1)... | tensorflow|keras|reinforcement-learning | 2 |
12,895 | 71,042,871 | How can I save the output of a convolution layer with and without a dilation rate as images | <p>I want to save a image file to see about difference using convolution layer with dilation rate and without that.</p>
<p>Of course I can search images about that, but I want to see difference of my dataset.</p>
<p>Is there a special function? or Can I make code with <code>opencv</code> like a <code>keras</code> layer... | <p>You can use <code>matplotlib</code> and a custom <code>Callback</code> to save the feature maps of a <code>CNN</code> layer after every epoch. Here is a working example:</p>
<pre class="lang-py prettyprint-override"><code>import tensorflow as tf
import pathlib
import matplotlib.pyplot as plt
dataset_url = "htt... | python|tensorflow|keras|dataset|conv-neural-network | 1 |
12,896 | 70,855,700 | Change value if it repeats a certain number of times in a month | <p>I have a dataframe with time data in the format:</p>
<pre><code> date values
0 2013-01-01 00:00:00 0.0
1 2013-01-01 01:00:00 0.0
2 2013-01-01 02:00:00 -9999
3 2013-01-01 03:00:00 -9999
4 2013-01-01 04:00:00 0.0
.. ... ...
8754 2016-12-31 18:00:00 427.5
8... | <p>You can do this using a <code>transform</code> call where you calculate the number of values per month in the same dataframe. Then you create a new column conditionally on this:</p>
<pre><code>import numpy as np
MISSING = -9999
THRESHOLD = 175
# Create a month column
df['month'] = df['date'].dt.to_period('M')
# Co... | python|pandas|dataframe | 1 |
12,897 | 51,643,004 | Apply MinMaxScaler() on a pandas column | <p>I am trying to use the sklearn MinMaxScaler to rescale a python column like below:</p>
<pre><code>scaler = MinMaxScaler()
y = scaler.fit(df['total_amount'])
</code></pre>
<p>But got the following errors:</p>
<pre><code>Traceback (most recent call last):
File "/Users/edamame/workspace/git/my-analysis/experiments... | <p>The input to <a href="http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html" rel="nofollow noreferrer">MinMaxScaler</a> needs to be array-like, with <code>shape [n_samples, n_features]</code>. So you can apply it on the column as a <em>dataframe</em> rather than a <em>series</em> (... | python-3.x|pandas|scikit-learn | 12 |
12,898 | 42,065,007 | Masking array X based on a condition with array Y of different size | <p>I have two 1-dim arrays <code>X</code> and <code>Y</code> of different size. I am trying to build the 2-dim array resulting from a condition on <code>X</code> and <code>Y</code>. For instance:</p>
<pre><code>X = np.array([0.3, 2.1, 4.3])
Y = np.array([1.5, 3.5])
mask = X > Y[:,np.newaxis]
</code></pre>
<p>and n... | <p>In this particular case, where you wish to add 1 wherever <code>mask</code> is True,
perhaps the simplest way is to take advantage of broadcasting and dtype
promotion -- that is, booleans are treated as ints in numeric context.</p>
<pre><code>In [49]: X + mask
Out[49]:
array([[ 0.3, 3.1, 5.3],
[ 0.3, 2.1... | python|arrays|numpy|array-broadcasting | 1 |
12,899 | 64,475,519 | How to read a limited number of columns plus the rest of line as a string into a Pandas dataframe? | <p>I have datafiles that looks like this:</p>
<pre><code> 1 97289.7040474555 4115155.1896845801 0.00 !CBBT
2 110001.7354024933 4137233.7577695986 0.00 !Kipp
3 74939.1481210588 4112567.6513698865 0.00 !Sewell
4 ... | <p>First read file to one column with some separator which is not in file like <code>|</code> and then processing in next steps by <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.split.html" rel="nofollow noreferrer"><code>Series.str.split</code></a>, assign new columns and <a href=... | python|pandas|csv | 1 |
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