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 |
|---|---|---|---|---|---|---|
369,600 | 72,085,899 | How to use all() and any() function in pandas with multiple columns | <p>I need one help regarding: How to use <code>all</code> and <code>any</code> function in Pandas with multiple columns. Below is my data frame:</p>
<pre><code> ResolutionCodeMapID CauseCodeMapID TicketTypeMapID multiple
ApplicationID
1292... | <p>Solution generate <code>True</code> if all values are greater or equal like <code>10</code> with <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.all.html" rel="nofollow noreferrer"><code>DataFrame.all</code></a>:</p>
<pre><code>cols = ['CauseCodeMapID','CauseCodeMapID','TicketType... | python|pandas | 1 |
369,601 | 71,817,573 | Keras Model early stops even though min_delta condition is not achieved | <p>I am training a Keras Sequential Model as follows. It is for the mnist dataset for 5 numbers. In goes the 28x28 images flattened and out comes a one hot notation for the class that they belong to.</p>
<pre><code>model = keras.Sequential([
keras.layers.InputLayer(input_shape = (784, )),
keras.layers.Dense(32, activat... | <p>You should set <code>patience</code> to <code>1</code> in the callback definition. If you don't, it defaults to <code>0</code>.</p>
<pre><code>es = keras.callbacks.EarlyStopping(monitor='loss', min_delta=1e-4, verbose=2, patience=1)
</code></pre> | tensorflow|keras | 1 |
369,602 | 71,879,050 | How to extract the last year (YYYY) from a YYYY-YY format column in Pandas | <p>I am trying to extract the last year (YY) of a fiscal date string in the format of YYYY-YY. e.g The last year of this '1999-00' would be 2000.</p>
<p>Current code seems to cover most cases other than this.</p>
<pre><code>import pandas as pd
import numpy as np
test_df = pd.DataFrame(data={'Season':['1996-97', '1997... | <p>This should work too:</p>
<pre><code>pd.to_numeric(test_df['Season'].str.split('-').str[0]) + 1
</code></pre>
<p>Output:</p>
<pre><code>0 1997
1 1998
2 1999
3 2000
4 2001
5 2002
6 2003
7 2004
8 2005
9 2006
10 2007
11 2008
12 2009
13 2010
14 2011
15 2012
</cod... | python|pandas|string|datetime|apply | 2 |
369,603 | 71,883,566 | Pandas counting number of rows based on data of two columns | <p>I am working on a dataset with format similar to this :-</p>
<pre><code>Name Sex Survived random_cols . . . .
Akshit Male 1 rand_val .......
Hema Female 0 .................
Rekha Female 1 .................
.
.
.
</code></pre>
<p>I want to ... | <p>You can use boolean indexing to filter by the <code>Survived</code> column to get only survived rows then <code>value_counts</code> on <code>Sex</code> column:</p>
<pre class="lang-py prettyprint-override"><code>s = df[df['Survived'].eq(1)].value_counts(subset=['Sex'])
</code></pre>
<pre><code>print(s)
Sex
Female ... | python|pandas|dataframe | 1 |
369,604 | 72,084,171 | How to create dummy variable for specifc values in a column? | <p>I want to create a dummy variable for a specific value in a column.
Let's say my database looks like this :</p>
<p><img src="https://i.stack.imgur.com/AFTeR.png" alt="database" /></p>
<p>I want a dummy variable just for the museums.</p>
<pre><code>pd.get_dummies (df,['Buildings'])
</code></pre>
<p>gives me a dummy ... | <p>If need only one column simpliest is create it manually with casting boolean to integers:</p>
<pre><code>df['museum'] = df['Buildings'].eq('museum').astype(int)
</code></pre>
<p>With your solution is possible replace non <code>museum</code> values to missing values, then <code>pd.get_dummies</code> omit missing valu... | pandas|dummy-variable | 0 |
369,605 | 71,917,358 | Why can not pass the validation of type of the series | <p>I do not know why it can not pass the validation of each variable.</p>
<pre><code>marvel_df = rate_df.loc[rate_df['Company']== "Marvel"]
mean_marvel =marvel_df[['Rate']].mean()
std_marvel =marvel_df[['Rate']].std()
n_marvel = marvel_df[['Rate']].count()
dc_df = rate_df.loc[rate_df['Company']== "DC&qu... | <p>Try it with this cleaned up code:</p>
<pre><code>marvel_df = rate_df[rate_df['Company'] == "Marvel"]
mean_marvel = marvel_df['Rate'].mean()
std_marvel = marvel_df['Rate'].std()
n_marvel = marvel_df['Rate'].count()
dc_df = rate_df[rate_df['Company'] == "DC"]
mean_dc = dc_df['Rate'].mean()
std_dc ... | python|pandas | 0 |
369,606 | 71,941,228 | More Epoch make loss rising | <p>I have a time-series dataset and I trained it using LSTM. I train using 200 epochs and the result is the loss value and val_loss value is pretty good (IMO)</p>
<p><a href="https://i.stack.imgur.com/u3828.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/u3828.png" alt="enter image description here" ... | <p>This is probably because your lr(learning rate) is too large. You could try to reduce your lr. From the graph, the training loss is increased so I think this case is not the problem of overfitting.</p> | tensorflow|machine-learning|keras|time-series|lstm | 0 |
369,607 | 71,873,074 | DF return a date column with 2 formats | <p>I have a df with a column with date, but the outcome is different for the lines, some lines outcomes <strong>ddmmyy</strong> some lines <strong>mmddyy</strong>. The database is all equal <strong>ddmmyy</strong>.</p>
<blockquote>
<p>12/20/2021 12/21/2021 12/22/2021 12/22/2021 12/27/2021 12/27/2021
12/27/2021 12/27/20... | <p>you can simply change the format of the entire column to dd-mm-yy format</p>
<pre><code>import datetime as dt
import pandas as pd
df = pd.DataFrame({'DOB': {0: '26/1/2016', 1: '1/26/2015'}})
df['DOB_1'] = pd.to_datetime(df.DOB).dt.strftime('%d/%m/%Y')
df
DOB DOB_1
0 26/1/2016 26/01/2016
1 1/26/201... | python|pandas | 0 |
369,608 | 72,092,973 | Error applying a weighted rolling average by group in Python | <p>I have the following dataframe for which I'm trying to compute a weighted rolling average:</p>
<pre><code>import pandas as pd
df = pd.DataFrame({'player_ID': {0: 123,
1: 123,
2: 123,
3: 123,
4: 123,
5: 456,
6: 456,
7: 456,
8: 456,
9: 456},
'hole_sg': {0: 3.14,
1: 2.70,
2: 5.20,
3: -0.02,
4... | <p>When you <code>groupby</code> and use <code>rolling</code> you get a MultiIndex. To align with the original DataFrame, you can use:</p>
<pre><code>df["rolling"] = df.groupby('player_ID')['hole_sg'].rolling(3).apply(lambda x: (np.dot(x, weights))/weights.sum()).droplevel(0)
>>> df
player_ID ho... | python|pandas|statistics|rolling-computation | 2 |
369,609 | 71,898,481 | Dask map_blocks is running earlier with a bad result for overlap and nested procedures | <p>I'm using Dask to create a simple pipeline of data manipulation. I'm basically using 3 functions. The first two uses a simple <code>map_blocks</code> and the third one uses a <code>map_blocks</code> also but for an overlapped data.</p>
<p>For some reason, the third <code>map_blocks</code> is executing earlier than I... | <p>Like many dask operations, da.overlap operations can either be passed a <code>meta</code> argument specifying the output types and dimensions, or dask will execute the function with a small (or length zero) subset of the data.</p>
<p>From the <a href="https://docs.dask.org/en/stable/array-overlap.html" rel="nofollow... | python|numpy|dask | 1 |
369,610 | 71,962,073 | extract emotions from text in dataframe in senticnet | <p>I am very novice in python and I treat to extract emotions from sentence in datafram though senticNet<br />
this my code but its not correct<br />
I don't know what's the wrong</p>
<pre><code>from senticnet.senticnet import SenticNet
def emotion_list1(text):
Emotion_list=[]
Emotion = pd.DataFrame(columns=... | <p>Are you facing any specific errors? I am able to extract the emotions using sn.moodtags() from a sentence.</p>
<pre><code># import
from senticnet.senticnet import SenticNet
from nltk.tokenize import word_tokenize
# define sentinet()
sn = SenticNet()
# create empty list to store results
emotion_list = []
# tokeni... | python|pandas|dataframe | 2 |
369,611 | 72,047,493 | Type error on Python: not all arguments converted during string formatting | <p>i am trying to multiply the image for image data set using pytorch random transform.</p>
<p>the code used to work however today it seems to produce error for formatting.</p>
<p>the loop for the data into a larger sample.</p>
<pre><code>or _ in range(80):
for img, label in dataset:
save_image(img, 'img'+s... | <p>When you use the <code>%</code> operator on a string, the first string needs to have formatting placeholders that will be replaced by the values after <code>%</code>. But you have no <code>%s</code> in the first string.</p>
<p>When you're creating pathnames, you should use <code>os.path.join()</code> rather than str... | python|loops|pytorch|data-augmentation | 0 |
369,612 | 71,842,607 | MUJOCO_PY:Computed torque control for kuka iiwa14 robot | <p>I'm new with mujoco_py.I already installed it successfully on linux and I have the URDF file of the robot(kukaiiwa14) but I don't know how can I manipulate the joints.For example I want to know the commands of how can I apply a force on a joint .
I have to apply optimal control on this robot so that he throws a ball... | <p><code>mujoco_py</code> is unsupported and deprecated, you should probably use MuJoCo's <a href="https://mujoco.readthedocs.io/en/latest/python.html" rel="nofollow noreferrer">native Python bindings</a>.</p>
<p>Regarding applying forces to joints, the actuation model is described <a href="https://mujoco.readthedocs.i... | python|numpy|controls|robotics|mujoco | 1 |
369,613 | 72,078,224 | How to get Centroid in GeoPandas | <p><strong>Centroid in Geopandas</strong></p>
<p>I have two location so I want get centroid from geopandas by python? How I do it?</p> | <p>You can use <a href="https://geopandas.org/en/stable/docs/reference/api/geopandas.GeoSeries.centroid.html" rel="nofollow noreferrer"><code>geopandas.GeoSeries.centroid</code></a>:</p>
<pre class="lang-py prettyprint-override"><code>import geopandas as gpd
df = gpd.read_file("polygons.shp")
df["centro... | python|jupyter-notebook|geopandas | 2 |
369,614 | 71,931,102 | Trying to find a graph in matplotlib | <p>I have data that show the difference of temperatures from 1955 to 2020 from an average. I want to make a graph in matplotlib that looks like this:
<a href="https://i.stack.imgur.com/7SbkV.jpg" rel="nofollow noreferrer">It shows temperature differences.</a></p>
<p>My data look like this:</p>
<pre><code>DATE TAVG
... | <p>You can use the pandas plotting (basicly, it's matplotlib). For the plot, I just created some fake data. I also assumed the line plot is a moving average.</p>
<pre><code>import random
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import seaborn as sns
# Cre... | python|pandas|matplotlib|plot|graph | 1 |
369,615 | 71,833,877 | Using a function in a loop and storing all the results | <p>So I created a function that returns the returns of quantile portfolios as a time series.</p>
<p>If I call Quantile_Returns(2014), the result (DataFrame) looks like this.</p>
<pre><code>Date Q1 Q2 Q3 Q4 Q5
2014-02-28 6.20 4.87 5.41 5.04 4.91
2014-03-31 -0.50 0.05 ... | <p>Try replacing the whole loop with</p>
<pre><code>Quantile = pd.concat(Quantile_Returns(j) for j in range(1960, 2021))
</code></pre>
<p><code>pd.concat</code> is expecting a sequence of pandas objects, and in the second pass through your loop you are giving it a DataFrame as the first argument (not a sequence of Data... | python|pandas|function|loops|concatenation | 1 |
369,616 | 72,042,131 | Model cannot fit on Tensorflow data pipline with unknown TensorShape | <p>I have a data loader pipeline for video data. Although I specify the output of the pipeline, I still get the following error when calling model.fit. "ValueError: as_list() is not defined on an unknown TensorShape". I searched for the error and most people say it is because of the tf.numpy_function that ret... | <p>Okay I found another solution. I do not exactly know why it works, just calling the following function does the job.</p>
<pre><code>
def set_shape(video, label):
video.set_shape((40,160,160, 3))
label.set_shape([])
return video, label
</code></pre> | python|tensorflow|pipeline|dataloader | 1 |
369,617 | 72,123,367 | Remove specific data from a Python pandas dataset | <p>We wrote this code in order to plot a charge spectrum like this (histo from HG (columun) for a specific CH (another column)):</p>
<p><a href="https://i.stack.imgur.com/SJxwn.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/SJxwn.png" alt="enter image description here" /></a></p>
<p>This plot was ob... | <p>You can delete them basing on boolean conditions. In your case this should work:</p>
<pre><code>df = df[df['Ts(ns)'] != '-'] # or change the '-' with the value you want filter on
</code></pre>
<p>You can have a look at <a href="https://datascienceparichay.com/article/pandas-delete-rows-based-on-column-values/" rel=... | python|pandas|dataframe|matplotlib | 1 |
369,618 | 71,899,677 | Extract specific words from dataframe | <p>I have the following dataframe named marketing where i would like to extract out source= from the values. Is there a way to create a general regex function so that i can apply on other columns as well to extract words after equal sign?</p>
<pre><code>Data
source=book,social_media=facebook,ads=Facebook
source=b... | <p>You can split the column value of string type into dict then use <code>pd.json_normalize</code> to convert dict to columns.</p>
<pre class="lang-py prettyprint-override"><code>out = pd.json_normalize(marketing['Data'].apply(lambda x: dict([map(str.strip, i.split('=')) for i in x.split(',')]))).dropna(subset='source'... | python|pandas|dataframe | 1 |
369,619 | 71,946,460 | Pandas Merge On Multiple Columns | <p>I need to merge the below two dataframes to yield the below result.</p>
<p>Table_1</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th>foo1</th>
<th>foo2</th>
<th>date</th>
<th>value1</th>
<th>value2</th>
</tr>
</thead>
<tbody>
<tr>
<td>a</td>
<td>b</td>
<td>4/20</td>
<td>6</td>
<td>NaN</td>... | <p>you could do this:</p>
<pre><code>pd.merge(Table_1, Table_2, how="outer", on=['foo1','foo2','date'])
</code></pre> | python|pandas|dataframe | 0 |
369,620 | 71,806,589 | Write a function that takes one row and returns a list of 2-dimension tuples: song title and points database | <p>I need to preprocess some data so that I can start analyzing it. I currently have a data frame which contains data of Eurovision winners. I need to create a new data frame which contains the words from each of the songs, with the points of each song assigned to each word in a tuple. For example, if the song name is ... | <p>You have the right idea, only right now you are iterating over every character in the string <code>row["Song"]</code>. You need to split this string up into a sequence of substrings where each substring represents a word from the song. Then iterate over this sequence. This code shows how one might do that<... | python|pandas|dataframe | 0 |
369,621 | 71,867,499 | AttributeError: 'tuple' object has no attribute 'set_xlim' matplotlib python | <p>I am trying to plot plot hist with dates in x axes and adjust dates.
My code is</p>
<pre><code> ax=plt.hist(df[ (df['disease']==1) & (df['FARM_NUM']==1282000)]['DATE'],bins=20)
ax.set_xlim([datetime.date(2020, 3, 15), datetime.date(2021, 7, 1)])
plt.xticks(rotation=90)
plt.show()
</code></pre>
<p>and... | <p><code>plt.hist</code> does not return the axis, it returns the bins of the histogram and other metadata. Just call xlim on <code>plt</code> itself.</p>
<pre><code>plt.xlim(left=leftValue, right=rightValue)
</code></pre>
<p>Caution: This solves the problem when the axes has numbers... I do not know how it will behave... | python|pandas|matplotlib | 2 |
369,622 | 71,848,640 | Pandas Timeseries reindex producing NaNs | <p>I am surprised that my reindex is producing NaNs in whole dataframe when the original dataframe does have numerical values init. Don't know why?</p>
<p>Code:</p>
<pre><code>df =
A ... D
Unnamed: 0 ...
2... | <p>From the documentation you can see that df.reindex() will <code>Places NA/NaN in locations having no value in the previous index.</code></p>
<p>However you can also provide a value that you want to replace missing values with (It defaults to NaN):</p>
<pre><code>df.reindex(onesec_idx, fill_value='')
</code></pre>
<p... | python|pandas|dataframe|reindex | 1 |
369,623 | 71,916,043 | Creating multiple dataframe using loop or function | <p>I'm trying to extract the hash rate for 3 cryptocurrencies and I have attached the code for the same below. Now, I want to pass three urls and in return I need three different different dictionaries which should have the values. I'm stuck and I don't understand how should I go about it. I have tried using loops but ... | <p>You can use next example how to get data from all 3 URLs and create a dataframe/dictionary from it:</p>
<pre class="lang-py prettyprint-override"><code>import re
import requests
import pandas as pd
url = {
"Bitcoin": "https://bitinfocharts.com/comparison/bitcoin-hashrate.html#3y",
"... | python|pandas|database|dataframe|dictionary | 0 |
369,624 | 72,070,520 | Is there a way to plot a histogram with given bin widths with Mathplotlib? | <p>I have two lists given.
One, named "bin_edge", represents the lower and upper borders of 24 bins by 25 values. The second, named "counts", represents the according counts (=values) of each bin.</p>
<p>My aim is, if possible, to get a Matplotlib histogram that should look like somewhat that:</p>
<... | <p>As you already have the heights for each bin, you should create a bar plot.</p>
<p>The x-values should be the bin edges, except for the last. By default, the bars are centered; you need <code>align='edge'</code> to align them with the bin edges. The widths of the bars are the differences of the bin edges.</p>
<pre ... | python|numpy|matplotlib|histogram|bokeh | 2 |
369,625 | 71,859,978 | Pandas Step function with rank | <p>I am trying to rank a column with the following function:</p>
<p><code>f(x) = if x=0, then y=0 else if x<0 then y=0.5 else y=rank(x) </code>
Any ideas on how can I achieve this?</p> | <p>You can use basic indexing</p>
<pre><code>df = pd.DataFrame({"x": [2, 3, 1, -1, 0]})
df["y"] = df["x"].rank()
df["y"][df["x"] == 0] = 0
df["y"][df["x"] < 0] = .5
</code></pre>
<p>or <code>loc</code></p>
<pre><code>df["y"] = df["x... | python|pandas|rank | 1 |
369,626 | 71,810,148 | Using numpy to construct an array with rows extracted from another 2D array as 2x2 blocks | <p>Suppose I have the following 2D array:</p>
<pre><code>x = np.array([[10,20,30,40], [50,60,70,80],[90,100,110,120]])
print(x)
array([[ 10, 20, 30, 40],
[ 50, 60, 70, 80],
[ 90, 100, 110, 120]])
</code></pre>
<p>I would like to construct a new array, <code>y</code>, where each row has the value... | <p>First, create a <a href="https://numpy.org/doc/stable/reference/generated/numpy.lib.stride_tricks.sliding_window_view.html" rel="nofollow noreferrer"><code>sliding_window_view</code></a> into <code>x</code> with the 2x2 boxes you want to see:</p>
<pre><code>b = np.lib.stride_tricks.sliding_window_view(x, (2, 2))
</c... | python|numpy|sliding-window | 3 |
369,627 | 17,044,808 | Python - Pandas: AttributeError: 'numpy.ndarray' object has no attribute 'start' | <p>Python - Pandas: AttributeError: 'numpy.ndarray' object has no attribute 'start'</p>
<p>Code that generates the error:</p>
<pre><code>import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from datetime import time
data = pd.read_csv('/temp/zondata/pvlog.csv', delimiter=';', parse_dates=True, inde... | <p>It looks like it's important to include the <code>0</code>:</p>
<pre><code>In [11]: df1['2010-7':'2010-10']
Out[11]:
Empty DataFrame
Columns: [value]
Index: []
In [12]: df1['2010-07':'2010-10']
Out[12]:
value
date
2010-08-31 12:36:53 30.37
2010-08-31 12:45:08 28.03
2010-08-31 12:55:09 25.16... | python|numpy|matplotlib|pandas | 2 |
369,628 | 16,705,598 | Python 2.7 - statsmodels - formatting and writing summary output | <p>I'm doing logistic regression using <code>pandas 0.11.0</code>(data handling) and <code>statsmodels 0.4.3</code> to do the actual regression, on Mac OSX Lion.</p>
<p>I'm going to be running ~2,900 different logistic regression models and need the results output to csv file and formatted in a particular way.</p>
<p... | <p>There is no premade table of parameters and their result statistics currently available.</p>
<p>Essentially you need to stack all the results yourself, whether in a list, numpy array or pandas DataFrame depends on what's more convenient for you. </p>
<p>for example, if I want one numpy array that has the results f... | python|python-2.7|pandas|statsmodels | 8 |
369,629 | 16,988,526 | Pandas reading csv as string type | <p>I have a data frame with alpha-numeric keys which I want to save as a csv and read back later. For various reasons I need to explicitly read this key column as a string format, I have keys which are strictly numeric or even worse, things like: 1234E5 which Pandas interprets as a float. This obviously makes the key c... | <p><em>Update: this has <a href="https://github.com/pydata/pandas/issues/3795" rel="noreferrer">been fixed</a>: from 0.11.1 you passing <code>str</code>/<code>np.str</code> will be equivalent to using <code>object</code>.</em></p>
<p>Use the object dtype:</p>
<pre><code>In [11]: pd.read_csv('a', dtype=object, index_c... | python|pandas|casting|type-conversion|dtype | 63 |
369,630 | 16,923,281 | Writing a pandas DataFrame to CSV file | <p>I have a dataframe in pandas which I would like to write to a CSV file.</p>
<p>I am doing this using:</p>
<pre><code>df.to_csv('out.csv')
</code></pre>
<p>And getting the following error:</p>
<pre><code>UnicodeEncodeError: 'ascii' codec can't encode character u'\u03b1' in position 20: ordinal not in range(128)
</cod... | <p>To delimit by a tab you can use the <code>sep</code> argument of <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_csv.html"><code>to_csv</code></a>:</p>
<pre><code>df.to_csv(file_name, sep='\t')
</code></pre>
<p>To use a specific encoding (e.g. 'utf-8') use the <code>encoding</cod... | python|csv|pandas|dataframe | 1,356 |
369,631 | 19,161,512 | Numpy extract submatrix | <p>I'm pretty new in <code>numpy</code> and I am having a hard time understanding how to extract from a <code>np.array</code> a sub matrix with defined columns and rows:</p>
<pre><code>Y = np.arange(16).reshape(4,4)
</code></pre>
<p>If I want to extract columns/rows 0 and 3, I should have:</p>
<pre><code>[[0 3]
[12... | <p>Give <a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.ix_.html" rel="noreferrer"><code>np.ix_</code></a> a try:</p>
<pre><code>Y[np.ix_([0,3],[0,3])]
</code></pre>
<p>This returns your desired result:</p>
<pre><code>In [25]: Y = np.arange(16).reshape(4,4)
In [26]: Y[np.ix_([0,3],[0,3])]
Out[26]... | python|numpy | 107 |
369,632 | 18,876,022 | How to format IPython html display of Pandas dataframe? | <p>How can I format IPython html display of pandas dataframes so that</p>
<ol>
<li>numbers are right justified</li>
<li>numbers have commas as thousands separator</li>
<li>large floats have no decimal places</li>
</ol>
<p>I understand that <code>numpy</code> has the facility of <code>set_printoptions</code> where I c... | <p>HTML receives a custom string of html data. Nobody forbids you to pass in a style tag with the custom CSS style for the <code>.dataframe</code> class (which the <code>to_html</code> method adds to the table).</p>
<p>So the simplest solution would be to just add a style and concatenate it with the output of the <cod... | python|html|pandas|ipython | 26 |
369,633 | 18,774,388 | re-import aliased/shadowed python built-in methods | <p>If one has run </p>
<pre><code>from numpy import *
</code></pre>
<p>then the built-in <code>all</code>, and several other functions, are shadowed by <code>numpy</code> functions with the same names. </p>
<p>The most common case where this happens (without people fully realizing it) is when starting <code>ipython... | <p>you can just do</p>
<pre><code>all = __builtins__.all
</code></pre>
<p>The statement <code>from numpy import *</code> basically do two separate things</p>
<ol>
<li>imports the module <code>numpy</code></li>
<li>copies all the exported names from the module to the current module</li>
</ol>
<p>by re-assigning the ... | python|numpy | 12 |
369,634 | 19,178,762 | Python using lambda to apply pd.DataFrame instead for nested loop is it possible? | <p>I'm trying to avoid nested loop in python here by using lambda apply to create a new column
using this argument below : </p>
<pre><code>from pandas import *
import pandas as pd
df = pd.DataFrame((np.random.rand(100, 4)*100), columns=list('ABCD'))
df['C'] = df.apply(lambda A,B: A+B)
</code></pre>
<p>TypeError:... | <p>Do you want to add column <code>A</code> and column <code>B</code> and store the result in <code>C</code>? Then you can have it simpler:</p>
<pre><code>df.C = df.A + df.B
</code></pre>
<hr>
<p>As @EdChum points out in the comment, the argument to the function in <code>apply</code> is a series, by default on axis ... | python|performance|nested|pandas | 11 |
369,635 | 18,795,489 | How do I construct a tuple in Cython? | <p>I am new to cython and I am just looking for an easy way of casting a numpy array to a tuple that can then be added to and/or looked up in a dictionary.</p>
<p>In CPython, I can use PyTuple_New and iterate over the values of the array (adding each one to the tuple as though I were appending them to a list). </p>
<... | <p>Cython is a superset of Python so any valid Python code is a valid Cython code. In this case, if you have a NumPy array, just passing it to a <code>tuple</code> class constructor should work just fine (just as you would do in regular Python).</p>
<pre><code>a = np.array([1, 2, 3])
t = tuple(a)
</code></pre>
<p>Cyt... | python|arrays|numpy|cython|cpython | 3 |
369,636 | 18,986,822 | How to use crosstab/pivot with multi dimensions | <p>I tried using pivot tables to have more than one values in the 'values' field for the pivot_table function but it doesnt work, so im trying to see if i can do it with crosstabs.
Here is my code</p>
<pre><code>table=pandas.pivot_table(xl2, values='Applications', rows='Sub-Product',cols='Application Date',aggfunc=n... | <p>Looks like you're really close to where you want to be. <code>table.stack(0)</code> will move the first level of the column index to the row index.</p>
<pre><code>In [1]: import pandas as pd
In [2]: from StringIO import StringIO
In [3]: df = pd.read_csv(StringIO("""\
...: Application-Date Sub-Product A... | python|csv|pandas|pivot-table|crosstab | 2 |
369,637 | 22,169,783 | Find list of values available in pandas dataframe with binary values | <p>I have a <code>DataFrame</code> like following:</p>
<pre><code> session p1 p2 p3 p4 p5 p6 p7 p8 p9 p10
0 1 1 0 0 1 1 0 1 0 1 0
1 2 1 0 0 0 1 0 1 0 1 1
2 3 1 0 1 0 1 0 0 0 1 0
3 4 0 1 1 1 0 1 0 1 ... | <p>Assuming by "all list values are included", you mean that the corresponding columns are 1:</p>
<pre><code>>>> df.session[df[listvals].sum(axis=1) == len(listvals)]
0 1
1 2
2 3
4 5
7 8
Name: session, dtype: int64
>>> df.session[df[listvals].sum(axis=1) >= 2]
0 1
1 2
2 ... | python|pandas|dataframe | 2 |
369,638 | 22,340,999 | Converting dates from HDF5 dataset to numpy array | <p>I have a HDF5 dataset having dates matrix which I'm loading in my Python script and want to use it as numpy array -</p>
<pre><code>>>> mat = h5py.File('xyz.mat')
>>> dates = mat['dates']
>>> dates
<HDF5 dataset "dates": shape (11, 285), type "<u2">
</code></pre>
<p>If I try to c... | <p>It seems that your dates are stored… <em>strangely</em>.
Your dataset is a 11 x 285 matrix of 16 bit unsigned ints. (It smells like it was exported from Matlab).</p>
<p>Basically the problem is that Numpy tries (and fails) to convert <em>each</em> element of the matrix (a.k.a. each individual character of the dates... | python|numpy|hdf5 | 1 |
369,639 | 22,180,981 | Filtering content by whether field contains a value | <p>In my original code that processes csv files I was skipping the data from rows that contained a certain value:</p>
<pre><code>df = df[df["ORGANIZATION"]!="Org1"]
</code></pre>
<p>Now I need to skip data that <strong>contains</strong> that value. The following determines if it contains the value...</p>
<pre><code>... | <p>You can use <code>~</code> to negate your boolean Series:</p>
<pre><code>>>> df = pd.DataFrame({"ORGANIZATION": ["Org1", "Org1 - Dave", "Org1 - Lisa", "Org2 - Bob", "Org3 - Sally"]})
>>> df
ORGANIZATION
0 Org1
1 Org1 - Dave
2 Org1 - Lisa
3 Org2 - Bob
4 Org3 - Sally
[5 rows x 1... | python|csv|pandas|filtering | 3 |
369,640 | 22,015,363 | How to get the index value in pandas MultiIndex data frame? | <pre><code>df = pd.DataFrame({'a':[2,3,5], 'b':[1,2,3], 'c':[12,13,14]})
df.set_index(['a','b'], inplace=True)
display(df)
s = df.iloc[1]
# How to get 'a' and 'b' value from s?
</code></pre>
<p>It is so annoying that ones columns become indices we cannot simply use df['colname'] to fetch values.</p>
<p>Does it encou... | <p>When I print s I get </p>
<pre><code>In [8]: s = df.iloc[1]
In [9]: s
Out[9]:
c 13
Name: (3, 2), dtype: int64
</code></pre>
<p>which has a and b in the name part, which you can access with:</p>
<pre><code>s.name
</code></pre>
<p>Something else that you can do is</p>
<pre><code>df.index.values
</code></pre... | python|pandas | 18 |
369,641 | 21,978,584 | Replace elements in 2nd column of array with new value from 2nd column of smaller array when 1st column matches | <p>I have two 2D arrays, e.g., </p>
<pre><code>A = [[1,0],[2,0],[3,0],[4,0]]
B = [[2,0.3],[4,0.1]]
</code></pre>
<p>Although the arrays are much larger, with A about 10x the size of B, and about 100,000 rows in A. I want to replace rows in A with the row in B whenever the 1st elements of the rows match, and leave th... | <p>We will have to iterate through the entire array A once in any case, since we are transforming it. What we could speed up though, is the look-up if a particular first element of A exists in B. To that end, it would be efficient to create a dictionary out of B. That way, lookup will be constant time. I am assuming he... | python|numpy | 1 |
369,642 | 22,231,347 | Array slice maximum that depends on the index of the previous axis | <p>So I have a large 2D array, coming from a tiff image, in which I want to calculate the center of mass. To do that, I am using the indices of the image (as coordinates) and the average function:</p>
<pre><code>from PIL import Image
from numpy import *
Im = Image.open("32bit_grayscale.tif")
imArr = array(Im, dtyp... | <p>What happens if you set <code>imArr[i,j]=0</code> for all points on one side or the other of your line? This is the simplest masking approach. </p>
<pre><code>I = indx[0,...]*slope + indx[1,...]>=M
imArr1 = imArr.copy()
imArr1[I]=0
print np.average(indx[0,...],weights=imArr1)
print np.average(indx[1,...],weigh... | python|arrays|numpy|slice | 1 |
369,643 | 22,214,985 | MultiIndex Group By in Pandas Data Frame | <p>I have a data set that contains countries and statistics on economic indicators by year, organized like so: </p>
<pre><code>Country Metric 2011 2012 2013 2014
USA GDP 7 4 0 2
USA Pop. 2 3 0 3
GB GDP 8 7 ... | <p>In this case, you don't actually need a <code>groupby</code>. You also don't have a <code>MultiIndex</code>. You can make one like this:</p>
<pre><code>import pandas
from io import StringIO
datastring = StringIO("""\
Country Metric 2011 2012 2013 2014
USA GDP 7 4 0 2... | python|pandas|dataset|dataframe | 31 |
369,644 | 22,077,328 | Vectorized format function for Pandas series | <p>Say I start with a <code>Series</code> of unformatted phone numbers (as strings), and I would like to format them as (XXX) YYY-ZZZZ. </p>
<p>I can get the sub-components of my input using regular expressions and <code>str.match</code> or <code>str.extract</code>. And I can perform the formatting using the result ... | <p>You can do this directly with <code>Series.str.replace()</code>:</p>
<pre><code>In [47]: s = pandas.Series(["1234567890", "5552348866", "13434"])
In [49]: s
Out[49]:
0 1234567890
1 5552348866
2 13434
dtype: object
In [50]: s.str.replace(r"(\d{3})(\d{3})(\d{4})", r"(\1) \2-\3")
Out[50]:
0 (123) ... | python|string|formatting|pandas | 2 |
369,645 | 22,412,508 | Python: Export a matrix in csv | <p>I have a 2D matrix with 13 rows and 13 columns (with headers except for the first column) named <code>correl</code> in Python. This <code>correl</code> matrix was generated from a <code>DataFrame</code> and I wish to populate a matrix <code>correlation</code> with multiple <code>correl</code>. For example: </p>
<pr... | <p>It looks like <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.corr.html" rel="nofollow noreferrer"><code>correlation</code> is a DataFrame too</a>, so you can simply use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_csv.html" rel="nofollow noreferr... | python|pandas | 7 |
369,646 | 22,249,620 | Code optimisation cubic interpolation | <p>I've been reading for quite some time Stack questions and answers and find a lot of very useful optimisation.
I'm kind of facing a bottleneck on the optimisation of the following code which is "just" for converting a cartesian map into a polar map. But with the particularity of increasing the density of angular poin... | <p>You can probably vectorize the for loops into something like:</p>
<pre><code>nX,nY=data.shape
for i in np.arange(-1,3):
for j in np.arange(-1,3):
condx = np.logical_and((ix + i) < nX, (ix + i) >=0)
condx = np.logical_and((iy + j) < nY, (iy + j) >=0)
cub = cubic(i-dx) * cubic(... | python|optimization|numpy | 1 |
369,647 | 22,174,958 | Sort a 2D numpy array by the median value of the rows | <p>If I have a 2D list in python, I can easily sort by the median value of each sublist like this:</p>
<pre><code>import numpy as np
a = [[1,2,3],[1,1,1],[3,3,3,]]
a.sort(key=lambda x: np.median(x))
print a
</code></pre>
<p>Yielding...</p>
<pre><code>[[1, 1, 1], [1, 2, 3], [3, 3, 3]]
</code></pre>
<p>Is there a way... | <p>I guess the numpythonic way would be to use fancy-indexing:</p>
<pre><code>>>> a = np.array([[1,2,3],[1,1,1],[3,3,3,]])
>>> a[np.median(a,axis=1).argsort()]
array([[1, 1, 1],
[1, 2, 3],
[3, 3, 3]])
</code></pre> | python|numpy | 4 |
369,648 | 21,976,675 | an efficient equivalent to numpy isnan or where that looks over a window of N values | <p>I have an operation I want to do in python on a 1D array with a finite but fairly large explicit stencil -- in other words, the output at [n] depends on the input from [t-N] to [t+N].</p>
<p>My processing code doesn't deal graciously with nan values and an expedient way for me to handle the situation is to substitu... | <p>You could use <code>np.where</code> to find the index of the NaNs, then use <code>np.add.outer</code> to include all the neighboring indices:</p>
<pre><code>import numpy as np
x = np.arange(100, dtype='float')
x[x % 13 == 0] = np.nan
print(x)
# [ nan 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. ... | python|numpy|functional-programming | 1 |
369,649 | 22,439,929 | Python equivalent for R's 'zoo' package | <p>Are there Python or perhaps <code>pandas</code> equivalents to R's <code>zoo</code> package?</p>
<p>In particular, I'm looking for equivalents to:</p>
<pre><code>dataLag2 = lag(zoo(train$data), -2, na.pad=TRUE)
train$dataLag2 = coredata(dataLag2)
</code></pre>
<p>Are there equivalents on Python that would produce... | <p>Pandas has the TimeSeries class which implements all the functionalities available in zoo to manipulate and homogenize irregular time series data:</p>
<p>if 'ts' is a TimeSeries object containing irregular hourly timestamped data I'd first create an homogeneous time series doing:</p>
<pre><code>ts.resample('H').in... | python|r|pandas|time-series|zoo | 2 |
369,650 | 22,346,552 | Map string values in a Pandas Dataframe with integers | <p>In Pandas <code>DataFrame</code> how to map strings in one column with integers. I have around 500 strings in the <code>DataFrame</code> and need to replace them with integers starting with '1'. </p>
<p>Sample <code>DataFrame</code>. </p>
<pre><code> Request count
547 ... | <p>So what you could do is construct a temporary dataframe and merge this back to your existing dataframe:</p>
<pre><code>temp_df = pd.DataFrame({'Request': df.Request.unique(), 'Request_id':range(len(df.Request.unique()))})
</code></pre>
<p>Now merge this back to your original dataframe</p>
<pre><code>df = df.merge... | python|pandas|dataframe | 10 |
369,651 | 18,111,444 | Extract non-main diagonal from scipy sparse matrix? | <p>Say that I have a sparse matrix in scipy.sparse format. How can I extract a diagonal other than than the main diagonal? For a numpy array, you can use numpy.diag. Is there a scipy sparse equivalent?</p>
<p>For example:</p>
<pre><code>from scipy import sparse
A = sparse.diags(ones(5),1)
</code></pre>
<p>How wou... | <p>When the sparse array is in <code>dia</code> format, the data along the diagonals is recorded in the <code>offsets</code> and <code>data</code> attributes:</p>
<pre><code>import scipy.sparse as sparse
import numpy as np
def make_sparse_array():
A = np.arange(ncol*nrow).reshape(nrow, ncol)
row, col = zip(*n... | python|numpy|scipy|sparse-matrix | 2 |
369,652 | 18,218,355 | SciPy optimization with grouped bounds | <p>I am trying to perform a portfolio optimization that returns the weights which maximize my utility function. I can do this portion just fine including the constraint that weights sum to one and that the weights also give me a target risk. I have also included bounds for [0 <= weights <= 1]. This code looks as ... | <p>Not totally sure I understand, but I think you can add the following as another constraint:</p>
<pre><code>def w_opt(W):
def filterer(x):
v = x.range.values
tp = v[0]
lower, upper = tp
return lower <= x[column_name].sum() <= upper
return not W.groupby(level=0, axis=0).f... | python|optimization|pandas|scipy|finance | 3 |
369,653 | 18,062,135 | Combining two Series into a DataFrame in pandas | <p>I have two Series <code>s1</code> and <code>s2</code> with the same (non-consecutive) indices. How do I combine <code>s1</code> and <code>s2</code> to being two columns in a DataFrame and keep one of the indices as a third column?</p> | <p>I think <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.tools.merge.concat.html"><code>concat</code></a> is a nice way to do this. If they are present it uses the name attributes of the Series as the columns (otherwise it simply numbers them):</p>
<pre><code>In [1]: s1 = pd.Series([1, 2], inde... | python|pandas|series|dataframe | 535 |
369,654 | 4,543,201 | Trouble Installing Numpy and Scipy | <p>I'm running into difficulty what I run <code>python setup.py install</code> in the numpy directory. It seems to be running alright, until it gets to a folder where permission is denied. The error it throws is <code>copying build/scripts.macosx-10.6-universal-2.6/f2py -> /usr/local/bin
error: /usr/local/bin/f2py... | <p>If you type in <code>sudo easy_install numpy</code> that solves the same problem that I have encountered. This may have occurred due to using an Apple Mac which have already installed <code>easy_install</code> in your <code>/usr/bin</code>. Typing <code>sudo easy_install numpy</code> if the package tries installin... | numpy | 3 |
369,655 | 4,150,171 | How to create a density plot in matplotlib? | <p>In R I can create the desired output by doing: </p>
<pre><code>data = c(rep(1.5, 7), rep(2.5, 2), rep(3.5, 8),
rep(4.5, 3), rep(5.5, 1), rep(6.5, 8))
plot(density(data, bw=0.5))
</code></pre>
<p><img src="https://i.stack.imgur.com/YFEin.png" alt="Density plot in R"></p>
<p>In python (with matplotlib) the... | <p>Five years later, when I Google "how to create a kernel density plot using python", this thread still shows up at the top! </p>
<p>Today, a much easier way to do this is to use <a href="http://stanford.edu/~mwaskom/software/seaborn/">seaborn</a>, a package that provides many convenient plotting functions and good s... | python|r|numpy|matplotlib|scipy | 185 |
369,656 | 8,554,673 | How to implement "where" (numpy.where(...) )? | <p>I'm a functional programming newbie.
I'd like to know how to implement numpy.where() in python, scala or haskell.
A good explanation would be helpful to me.</p> | <p>In Haskell, doing it for n-dimensional lists, as the NumPy equivalent supports, requires a fairly advanced typeclass construction, but the 1-dimensional case is easy:</p>
<pre class="lang-hs prettyprint-override"><code>select :: [Bool] -> [a] -> [a] -> [a]
select [] [] [] = []
select (True:bs) (x:xs) (_:ys... | python|scala|haskell|functional-programming|numpy | 6 |
369,657 | 8,669,261 | Indices of k-minimum values along an axis of a numpy array | <p>Is there a way to return the indices of k-minimum values along an axis of a numpy array without using loops?</p> | <pre><code>import numpy as np
x = np.array([[5, 2, 3],[1, 9, 2]]) # example data
k = 2 # return the indices of the 2 smallest values
np.argsort(x, axis=1)[:,0:k] # by row
array([[1, 2],
[0, 2]])
</code></pre> | python|arrays|numpy|indices|minim | 7 |
369,658 | 8,802,916 | Using multi-threading to process an image faster on python? | <p>On a Python + Python Image Library script, there's a function called processPixel(image,pos) that calculates a mathematical index in function of an image and a position on it. This index is computed for each pixel using a simple for loop:</p>
<pre><code>for x in range(image.size[0)):
for y in range(image.size[1... | <p>You cannot speed it up using threading due to the <a href="http://docs.python.org/c-api/init.html#threads" rel="nofollow">Global Interpreter Lock</a>. Certain internal state of the Python interpreter is protected by that lock, which prevents different threads that need to modify that state from running concurrently.... | python|image-processing|numpy|gpu|python-imaging-library | 7 |
369,659 | 55,261,785 | NVidia drivers stopped working on AWS EC2 instance with Ubuntu 16.04 and Tesla K80 GPU | <p>I've been using an AWS EC2 instance, with a Tesla K80 GPU, for a while to run TensorFlow code.
I have CUDA 9.0 and cuDNN 7.1.4 installed, and I'm using TF 1.12, all of this on Ubuntu 16.04</p>
<p>Everything worked well up to yesterday, but today it seems that the NVidia drivers have stopped running for some reason ... | <p>I fixed this problem by updating to the latest Nvidia drivers. Use:</p>
<pre><code>nvcc --version
</code></pre>
<p>to get the cuda toolkit version number. For 9.0 the latest drivers are 384.183, and 410.104 for CUDA 10.0. </p>
<p>Then run:</p>
<pre><code> wget http://us.download.nvidia.com/tesla/384.183/NVIDIA-L... | amazon-web-services|tensorflow|amazon-ec2|gpu|nvidia | 12 |
369,660 | 55,306,920 | Make a plot by occurence of a col by hour of a second col | <p>I have this df :</p>
<p>and i would like to make a graph by half hour of how many row i have by half hour without including the day. </p>
<p>Just a graph with number of occurence by half hour not including the day. </p>
<pre><code>3272 8711600410367 2019-03-11T20:23:45.415Z d7ec8e9c5b5df11df8ec7ee1305529... | <p>One way you could do this is split up coding for hours and half-hours, and then bring them together. To illustrate, I extended your data example a bit:</p>
<pre><code>import pandas as pd
df = pd.DataFrame({'Created':['2019-03-11T20:23:45.415Z', '2019-03-11T20:23:51.072Z', '2019-03-11T20:33:03.072Z', '2019-03-11T21:... | pandas | 1 |
369,661 | 55,231,351 | New to tensorflow | <p>I want to learn tensorflow. I'm sorry for the questions but I learn In my own way. First, is there a list of definitions on terminology? Next, at my workplace we deal with a lot of flat files from different ecommerce sites. I want to build a bot that will do one of the following choices. I am not sure what is the be... | <p>I am taking this <a href="https://www.coursera.org/learn/introduction-tensorflow" rel="nofollow noreferrer">course</a>. I have taken a bunch of ml and nn courses that use tensorflow. This one is the easiest. It goes over using tensorflow and keras in a lot of detail using some mnist data sets.</p>
<p>There are a lo... | tensorflow|machine-learning|google-colaboratory | 0 |
369,662 | 55,317,559 | how to improve neural network prediction, classification | <p>I am trying to learn some neural networks for fun. I decided to try to classify some pokemon legendary cards, from a data set from kaggle. I read up on documentations and followed machine learning mastery guides, while reading up on medium to try to understand the process. </p>
<p>My problem/ question : i tried pr... | <h3>Problem:</h3>
<p>The problem is that, as you stated, your dataset is heavily <strong>imbalanced</strong>. This means that you have a lot more training examples for class 0 than class 1. This causes the network, during training, to develop a heavy bias towards predicting class 0.</p>
<h3>Evaluation:</h3>
<p>The f... | python|tensorflow|keras|neural-network | 1 |
369,663 | 55,172,965 | Python delete all rows between the first view and the first click? | <p>So I've been trying an failing and am hoping for some help. What I want to do is</p>
<ul>
<li>Group by users and sort by time stamp (which is the way the dataframe belowis set up)</li>
<li>Now I want to take every view prior to the first click, and group it into a single event with the earliest timestamp
<ul>
<li>... | <p>Follow below steps </p>
<pre><code>s1=df.activity.eq('view').groupby(df['id']).transform('idxmax')
# using idxmax find the first view
s2=df.activity.eq('click').groupby(df['id']).transform('idxmax')
# same logic here find the index of first click
out=df.loc[(df.index<=s1)|(df.index>=s2)].copy()
# filter t... | python|pandas|dataframe|timestamp | 1 |
369,664 | 55,234,638 | Dataframe shift moving data into random columns? | <p>I'm using code to shift time series data that looks somewhat similar to this:</p>
<pre><code>Year Player PTSN AVGN
2018 Aaron Donald 280.60 17.538
2018 J.J. Watt 259.80 16.238
2018 Danielle Hunter 237.60 14.850
2017 Aaron Donald 181.0 ... | <p>I suggest use:</p>
<pre><code>#first aggregate for unique MultiIndex
res = df.groupby(['Player', 'Year']).sum()
#MultiIndex
idx = pd.MultiIndex.from_product(res.index.levels,
names=['Player', 'Year'])
#aded new missing years
res = res.reindex(idx).sort_index()
#shift all columns,... | python|pandas | 2 |
369,665 | 55,142,231 | Pandas Data Frame in Python. Proportions and Transpose | <p>I have the following data frame in Pandas. The idea is to generate an additional data frame IDs based on the proportion of the variable TYPE, transposing it into columns. Any help is appreciated!</p>
<pre><code>d = {'ID': [1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2], 'TYPE': ['A','A','A','B','B','B','B','C','C','C','A','A... | <p>Use <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.SeriesGroupBy.value_counts.html" rel="nofollow noreferrer"><code>SeriesGroupBy.value_counts</code></a> with parameter <code>normalize=True</code> and reshape by <a href="http://pandas.pydata.org/pandas-docs/stable/reference/ap... | python|pandas|dataframe | 1 |
369,666 | 55,170,175 | Need very different learning rate for manual updates vs. using model | <p>I am currently just trying to write some pedagogical material, in which I borrow from some common examples that have been reworked numerous times on the web.</p>
<p>I have a simple bit of code where I manually create tensors for layers, and update them within a loop. E.g.:</p>
<pre><code>w1 = torch.randn(D_in, H,... | <p>The crucial difference is <strong>the initialization</strong> of the weights. The weight matrix in a <code>nn.Linear</code> <a href="https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/linear.py#L58-L63" rel="nofollow noreferrer">is initialized smart</a>. I'm pretty sure that if you construct both the mo... | pytorch | 1 |
369,667 | 55,396,415 | Separate a Pandas dataframe based on contents of time column | <p>I have a pandas dataframe of which one column is datetime. The data spans over a month and is sorted on time in ascending order. Now I want to separate out weekend and weekday data, this is my code :</p>
<pre><code>data = pd.read_csv('Data.csv')
data.head()
Time A B C
0... | <p>Use:</p>
<pre><code>rng = pd.date_range('2019-03-29 11:00:00', periods=30, freq='3H')
data = pd.DataFrame({'Time': rng, 'a': range(len(rng))})
print (data)
Time a
0 2019-03-29 11:00:00 0
1 2019-03-29 14:00:00 1
2 2019-03-29 17:00:00 2
3 2019-03-29 20:00:00 3
4 2019-03-29 23:00:00 ... | python|python-3.x|pandas|datetime | 1 |
369,668 | 55,225,174 | Simple Data recall RNN in Pytorch | <p>I am learning Pytorch and am trying to make a network that can remember previous inputs.
I have tried 2 different input/output structures(see below) but haven't gotten anything to work the way I would like. </p>
<p>input 1:</p>
<p>in:[4,2,7,8]</p>
<p>output [[0,0,4],[0,4,2],[4,2,7],[2,7,8]]</p>
<p>code:</p>
<pr... | <p>The problem that your network presents, it's the fact that your input is of shape 1:</p>
<pre><code>for i in range(0, data_amount, batch_size):
inputs = data[i:i + batch_size]
labels = labs[i:i + batch_size]
print(inputs.shape,labels.shape)
>>>torch.Size([1]) torch.S... | pytorch|recurrent-neural-network | 0 |
369,669 | 55,357,561 | How do I unnest an array 2x2 within cells of a column in a Dataframe? | <p>I have a DataFrame that in one its column there are 2x2 np.arrays within each cell. I'm trying to extract these arrays to merge with the original Dataframe.</p>
<p>Suppose I have the following df:</p>
<pre><code>df=pd.DataFrame({'A':[101, 202],'B':[ [[1,2], [3,4]] ,[[5,6], [7,8]] ] })
</code></pre>
<p>and I need ... | <p>Also,</p>
<pre><code>df =df.set_index('A').B.apply(pd.Series).stack().reset_index().rename(columns={0:'B'})
df1 =pd.DataFrame(df.B.values.tolist()).add_prefix('B_')
pd.concat([df['A'], df1], axis = 1)
</code></pre> | python|pandas | 1 |
369,670 | 55,503,006 | 1064, “You have an error in your SQL syntax” inserting in MySql | <p>I have the following error on my IDE:</p>
<blockquote>
<p>MySQLdb._exceptions.ProgrammingError: (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near '2102@lionstate.edu', '88zlsj5j', 'Kristopher O'Connell', '21', 'F', 'CMPSC'... | <p>Try taking the <code>'</code> off from all the variables inside the values section.</p>
<p>Such as <code>values (%s, %s, %s .....)</code> instead of <code>values ('%s', '%s', ...)</code></p> | python|mysql|sql|pandas|pycharm | 0 |
369,671 | 55,433,251 | How can I select n rows preceding an index row in a DataFrame? | <p>I have a <code>DataFrame</code> and am trying to select a row (given a particular index) and the <code>n</code> rows preceding it.</p>
<p>I've tried something like:</p>
<pre><code>last_10 = self.market_data.iloc[index:-10]
</code></pre>
<p>But this appears to give everything from the <code>index</code> up until t... | <p>Use this:</p>
<pre><code>n = 10
last_10 = self.market_data.iloc[index-n:index+1]
</code></pre>
<p>When slicing arrays, Python returns everything up until the last index, so you need to add one to include it.</p> | python|pandas|dataframe | 2 |
369,672 | 55,269,547 | CNN OCR Machine readable zone | <p>I am training a Convolutional Neural Network to recognize MRZ(Machine Readable Zone) characters, on a smartphone. I want to know if in order to improve accuracy I should train it with multiple fonts, even if MRZ only uses OCR-B. Also, the model does not perform on device with the same level of accuracy as in the pyt... | <p>If MRZ use only one font, then you should use only this font to train your CNN.<br>
In order to improve results, you should preprocess the image before passing it to the CNN, for example, at first identify text zones in an image and then pass them through CNN.<br>
<br>
The accuracy of the model can change from a dev... | python|tensorflow|keras|conv-neural-network | 1 |
369,673 | 55,443,173 | Trying to matching a column of names in one df where they could be an exact or partial match of another df 'scolumn? | <p><strong>Goal</strong>: If the name in df2 in row i is a sub-string or an exact match of a name in df1 in some row N and the state and district columns of row N in df1 are a match to the respective state and district columns of df2 row i, combine.</p>
<p><strong>Break down of data frame inputs:</strong></p>
<ol>
<l... | <p>We can use <code>difflib</code> for this to create an artificial <code>key column</code> to merge on. We call this column <code>name</code>, like the one in <code>df2</code>:</p>
<pre><code>import difflib
df1['Name'] = df1['CandidateName'].apply(lambda x: difflib.get_close_matches(x, df2['Name'])[0])
df_merge = df1... | python|regex|pandas|python-2.7 | 0 |
369,674 | 55,334,026 | how to save image of loaded keras model as png/jpg? | <p>I have trained a keras model and saved it to later make predictions. However, I loaded the saved model using: </p>
<pre><code>from keras.models import load_model
#Restore saved keras model
restored_keras_model = load_model("C:/*******/saved_model.hdf5")
</code></pre>
<p>Now I would like to save an image of... | <p>Yes, in addition to doing a restored_keras_model.summary(), you can save the model architecture as a png file using the plot_model API.</p>
<pre><code>from keras.utils import plot_model
plot_model(restored_keras_model, to_file='model.png')
</code></pre>
<p><a href="https://keras.io/visualization/#model-visualizati... | python-3.x|tensorflow|keras | 8 |
369,675 | 55,466,634 | Why the input of `keras.experimental.SequenceFeature` must be a `SpareTensor`? | <p>I'm trying to migrate my seq2seq model to TensorFlow 2.0. However, I have an issue in the feature column input layer. </p>
<p>In TensorFlow 2.0, they provide an input layer for sequence data, <code>keras.experimental.SequenceFeatures</code>, but I HAVE TO PUT a SpareTensor.</p>
<p>Actually, all sequence data is no... | <p>They use <code>SpareTensor</code> to represent sequence data which have an arbitrary sequence length. However, input sequence data must have the same maximum sequence length.</p> | tensorflow2.0 | 0 |
369,676 | 55,214,862 | Pandas: Concatenating two Series to Pandas DataFrame | <p>How can I concatenate two Series and create one DataFrame ?
For example, I have series like:</p>
<pre><code>a=pd.Series([1,2,3])
b=pd.Series([4,5,6])
</code></pre>
<p>And, I want to get a data frame like:</p>
<pre><code>pd.DataFrame([[1,4], [2,5], [3,6]])
</code></pre> | <p>Shortest would be:</p>
<pre><code>pd.DataFrame([a,b]).T
</code></pre>
<p>Or:</p>
<pre><code>pd.DataFrame(zip(a,b))
0 1
0 1 4
1 2 5
2 3 6
</code></pre> | python|pandas | 4 |
369,677 | 55,350,988 | How to calculate sums across matrix diagonals in Tensorflow? | <p>Say, I have matrix <code>4x4</code> like:</p>
<pre><code>1 2 3 4
5 6 7 8
4 3 2 1
8 7 6 5`
</code></pre>
<p>I want to get matrix <code>2*4-1</code> with elements like:</p>
<pre><code>8
4+7
5+3+6
1+6+2+5
2+7+1
3+8
4
</code></pre>
<p>How can I do that in Tensorflow? With tensors, of course - I have tensor with shap... | <p>You can use <code>tf.py_func</code> to wrap a <code>numpy</code> function.</p>
<pre><code>import tensorflow as tf
import numpy as np
def np_all_trace_sum(a):
n = a.shape[-1]
all_trace_sum = [a.trace(i,axis1=-1,axis2=-2) for i in range(n-1,-n,-1)] # shape = (2*n-1,a,b,c,..,l)
return np.moveaxis(all_trac... | python|tensorflow | 1 |
369,678 | 55,235,099 | Iterating numpy array to find max value within subarray leaving the row index | <p>I wanted to find the max of 2D array along the axis=0 and I don't want to include the value at the row-index. I'm not happy with this solution because I need to run this on a million of rows and I don't want to use for-loop here. I tried <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmax.html... | <p>This should do the trick:</p>
<pre><code>a = np.array([[1, 0.5, 0.3, 0, 0.2],
[0, 1, 0.2, 0.8, 0],
[0, 1, 1, 0.3, 0],
[0, 0, 0, 1, 0]])
# Create an array of ones the same size as a
b = np.ones_like(a)
# Fill the diagonal of b with NaN
np.... | python|arrays|numpy|max | 3 |
369,679 | 55,147,039 | Anaconda tensorflow packages incomplete? (just few kilobytes filesize) | <p>I am following the instruction at <a href="https://anaconda.org/anaconda/tensorflow-gpu" rel="nofollow noreferrer">https://anaconda.org/anaconda/tensorflow-gpu</a> to install "tensorflow-gpu" (currently 1.12.0 for linux64) by running</p>
<pre><code>conda install -c anaconda tensorflow-gpu
</code></pre>
<p>in the ... | <p>Faced the same issue. You can get tensorflow working by installing it using pip instead of conda using:</p>
<pre><code>pip install --upgrade tensorflow
</code></pre> | python|linux|tensorflow|anaconda | 0 |
369,680 | 55,491,686 | Why are the parameters of my encoder and decoder not symmetric in my autoencoder? | <p>I'm trying to implement an autoencoder in Tensorflow using the Keras API. My code is inspired by examples on the Keras website: <a href="https://blog.keras.io/building-autoencoders-in-keras.html" rel="nofollow noreferrer">https://blog.keras.io/building-autoencoders-in-keras.html</a></p>
<p>The goal is to be able to... | <p>As you already thought the problem here are the biases. If you take for example the weights between Dense 12 and Dense 13 you have <code>1024*8 = 8192</code> normal weights + <code>8</code> biases (<code>8200</code> in total).</p>
<p>If you take the weights between Dense 17 and 18 you'll have <code>8*1024 = 8192</c... | python|tensorflow|keras|autoencoder | 0 |
369,681 | 55,267,619 | Tf Summary not giving the histograms but saves the session graph (pre-trained model) | <p>I am carrying out an analysis to visualize the distribution of weights for a pre-trained model available online. Its a Resnet18 model trained on CIFAR10. </p>
<p>I have the following code to restore the model from <code>meta</code> and <code>ckpt</code> and then I try to create a histogram of all the <code>weights<... | <p>It's not enough to pass the merged summary node to <code>sess.run</code>. You need to take that evaluated result and pass it to the <code>add_summary</code> method of your <code>FileWriter</code> instance.</p>
<pre><code># evaluate the merged summary node in the graph
output, summ = sess.run([softmax, tf_fp_summari... | python|tensorflow|histogram|tensorboard|summary | 1 |
369,682 | 55,577,773 | Multiple fields using Pandas and Quandl | <p>I am using Quandl to download daily NAV prices for a specific set of Mutual Fund schemes. However it returns a data object instead of returning the specific value</p>
<pre><code>import quandl
import pandas as pd
quandl.ApiConfig.api_key = <Quandl Key>
list2 = [102505, 129221, 102142, 103197, 100614, 100474,... | <p>By default, quandl Time-series API returns you a dataframe with date as index, even if there is only one row. </p>
<p>If you only need the value of first row, you can use <code>iloc</code>:</p>
<pre class="lang-py prettyprint-override"><code>if not nav.empty:
print (nav.iloc[0])
</code></pre>
<p>or just plain... | python-3.x|pandas|quandl | 1 |
369,683 | 55,331,148 | Compare multiple columns in a dataframe and generate a similarity matrix | <p>Suppose that I have a dataframe consisting of four columns <em>Col1, Col2, Col3 and Col4</em>.
Each column has 100 entries (assume timestamp), thus the overall shape of the dataframe is (100,4).
For a given particular timestamp, these columns have similar values, thus making their overall variation with time very s... | <pre><code>import pandas as pd
import numpy as np
Fs = 100
f = 5
sample = 100
x = np.arange(sample)
y = np.sin(2 * np.pi * f * x / Fs)
y1 = np.sin(3 * np.pi * f * x / Fs)
y2 = np.sin(4 * np.pi * f * x / Fs)
y3 = np.sin(5 * np.pi * f * x / Fs)
data=pd.DataFrame({"c":y,"c1":y1,"c2":y2,"c3":y3})
data.cov()
</code></pre>
... | python|pandas|dataframe | 1 |
369,684 | 55,147,511 | Invalid combination of arguments - eq() | <p>I'm using a code shared <a href="https://gist.github.com/johnolafenwa/96b3322aabb61d4d36fd870a77f02aa3" rel="nofollow noreferrer">here</a> to test a CNN image classifier. When I call the test function, I got this error on <a href="https://gist.github.com/johnolafenwa/96b3322aabb61d4d36fd870a77f02aa3#file-simplenet-p... | <p>Why do you have <code>.numpy()</code> here <code>prediction = prediction.cpu().numpy()</code>?
That way you convert PyTorch tensor to NumPy array, making it incompatible type to compare with <code>labels.data</code>.</p>
<p>Removing <code>.numpy()</code> part should fix the issue.</p> | python|numpy|image-processing|machine-learning|pytorch | 1 |
369,685 | 55,281,506 | Python: How to pad with zeros? | <p>Assuming we have a dataframe as below:</p>
<pre><code>df = pd.DataFrame({ 'Col1' : ['a', 'a', 'a', 'a', 'b', 'b', 'c', 'c'],
'col2' : ['0.5', '0.78', '0.78', '0.4', '2', '9', '2', '7',]
})
</code></pre>
<p>I counted the number of rows for all the unique values in <code>col1</code>. Like <code>a</co... | <p>You can create counter by <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.cumcount.html" rel="nofollow noreferrer"><code>GroupBy.cumcount</code></a>, create <code>MultiIndex</code> and <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.r... | python|pandas|numpy|zero-padding | 3 |
369,686 | 55,286,915 | Convert a dictionary which values are different-length lists into a dataframe | <p>I have a dictionary which the keys are years while the values are corresponding models. Below is a piece of data I printed out from the dictionary.</p>
<pre><code>1975: ['MODEL9808533471'],
1985: ['MODEL0912768548'],
1980: ['MODEL1006230072', 'MODEL7898438988'],
1987: ['MODEL0848444339'],
1977: ['MODEL788939572... | <p>Perfectly fit the usage of <code>MultiLabelBinarizer</code> from <code>sklearn</code></p>
<pre><code>from sklearn.preprocessing import MultiLabelBinarizer
s = pd.Series(d)
mlb = MultiLabelBinarizer()
yourdf=pd.DataFrame(mlb.fit_transform(s),columns=mlb.classes_, index=s.index).T
yourdf
Out[121]:
1... | python|pandas|dataframe|dictionary|matrix | 4 |
369,687 | 55,298,323 | TensorFlow 2.0 returns unexpected output on dtype=int32 with GradientTape | <p>The following code should output the gradient of y=x*x for x=2, i.e. the value of 4. However the code prints a value of None when using TensorFlow 2.0.0-alpha0. When the definition of x changes to use <code>tf.float32</code> instead of <code>tf.int32</code> as shown in the next snippet, the output changes to the cor... | <p>The reason is that <code>tf.gradient</code> doesn't propagate the gradients through integer tensors. This has been referenced in this github issue:</p>
<p><a href="https://github.com/tensorflow/tensorflow/issues/20524" rel="nofollow noreferrer">https://github.com/tensorflow/tensorflow/issues/20524</a></p> | tensorflow | 1 |
369,688 | 55,196,713 | Yolo Darkflow error. tensorflow.python.framework.errors_impl.InvalidArgumentError: Invalid name | <p>I am getting this output when i run my code:</p>
<pre><code> %Run run_img.py
/usr/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: compiletime version 3.4 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.5
return f(*args, **kwds)
/usr/lib/python3.5/importlib/_boo... | <p>Try this solution. I was having the exact same problem as you and this solved it for me.</p>
<pre><code>$ sudo apt-get install python-pip python3-pip
$ sudo pip3 uninstall tensorflow
$ git clone https://github.com/PINTO0309/Tensorflow-bin.git
$ cd Tensorflow-bin
$ sudo pip3 install tensorflow-1.11.0-cp35-cp35m-linu... | python|tensorflow|yolo|darkflow | 0 |
369,689 | 55,487,704 | Do I need a SWIG typemap to have a c function return a float to python? | <p>I'm trying to call a C function from python. This function takes a number of arrays as input and returns a float.</p>
<p>Do I need a SWIG typemap to do this? One concern is that python doesn't make a distinction between <code>floats</code>, <code>double</code>, etc and I'm specifically interested in returning only ... | <p>Returning <code>float</code> "just works". You don't need additional typemaps:</p>
<p><strong>test.i</strong></p>
<pre><code>%module test
%inline %{
float func(void) { return 1.5; }
%}
</code></pre>
<p>After running swig and compiling the result:</p>
<pre><code>>>> import test
>>> test.fu... | python|c|numpy|swig | 2 |
369,690 | 55,311,983 | How to select bunch of rows | <p>I have dataframe with multiple columns , i want to select bunch of rows if column B have consecutive 1 and check in these rows if column A have any value equal to 0.04 then need this bunch of rows and extract start value and end value of column A for this bunch of rows</p>
<p>Here is my dataframe
<a href="https://... | <p><strong>filtter</strong> Consecutive groups <code>.diff().abs().cumsum().bfill()</code> not following the specific considitons <code>(x['B'].eq(1).any() and x['A'].eq(0.04).any()</code></p>
<p><strong>agg</strong> first and last</p>
<p>followed by grouping consecutivity column to extract first and last rows with u... | python-3.x|pandas|pandas-groupby | 2 |
369,691 | 55,348,616 | Create a tensor by calling a function in a loop in TensorFlow | <p>I need to create a tensor by calling some function <code>fn</code> over two other tensors and indices in a loop as follows:</p>
<pre><code>tensor = [[fn(tensor1, tensor2, i, j) for i in range(3)] for j in range(4)]
</code></pre>
<p>Not sure how to approach this problem. Use <code>tf.map_fn</code> somehow?</p> | <p>So for your simple case your code will execute as it is.</p>
<pre><code>import tensorflow as tf
sess = tf.Session()
a = tf.constant([1,2,3])
b = tf.constant([3,4,5,6])
def fn( tensor1, tensor2, i, j ):
return tensor1[i] * tensor2[j]
tensor = [[fn(a, b, i, j) for i in range(3)] for j in range(4)]
init = tf.... | tensorflow | 1 |
369,692 | 55,576,608 | Extracting weights from best Neural Network in Tensorflow/Keras - multiple epochs | <p>I am working on a 1 - hidden - layer Neural Network with 2000 neurons and 8 + constant input neurons for a regression problem.</p>
<p>In particular, as optimizer I am using RMSprop with learning parameter = 0.001, ReLU activation from input to hidden layer and linear from hidden to output. I am also using a mini-ba... | <p>Use <code>ModelCheckpoint</code> callback from Keras.</p>
<pre><code>from keras.callbacks import ModelCheckpoint
checkpoint = ModelCheckpoint(filepath, monitor='val_mean_squared_error', verbose=1, save_best_only=True, mode='max')
</code></pre>
<p>use this as a callback in your <code>model.fit()</code> . This wil... | python|tensorflow|keras|neural-network|deep-learning | 1 |
369,693 | 55,270,726 | How to zip together lists of unequal length into a dictionary? | <p>I have three lists. </p>
<pre><code>import pandas as pd
author = ['mccoy.robert']
coauthors = [
'hola.lubica', 'kundu.subiman', 'ntantu.ibula',
'fletcher.peter', 'jain.tanvi', 'jindal.varun', 'bankston.paul',
'di-maio.giuseppe', 'dickman.raymond-f-jun', 'holy.dusan',
'slover.rebecca', 'curtis.dou... | <p>You can do this with nested <code>for</code> loop, looping over the authors and the zipped coauthors and frequencies. Like so:</p>
<pre><code>authors = ['mccoy.robert']
coauthors = [
'hola.lubica', 'kundu.subiman', 'ntantu.ibula',
'fletcher.peter', 'jain.tanvi', 'jindal.varun', 'bankston.paul',
'di-mai... | python|pandas | 0 |
369,694 | 55,306,040 | How to overcome the Could not convert String to Float? | <p>Hi Everyone I'm Having This Two Columns:</p>
<pre><code>Mi_Meteo['Measurement'] = Mi_Meteo['Measurement'].str.rstrip(' Measure')
Mi_Meteo['Measurement'].head()
0 0.8
1 0.6
2 0.4
3 0.4
4 0
Name: Measurement, dtype: object
</code></pre>
<p>And:</p>
<pre><code>Mi_Meteo['Sensor_ID'] = Mi_Meteo['Sens... | <p>One possible reason could be some white space in your data which didn't clear out. Add in <code>str.strip()</code> before converting to <code>float</code>.</p>
<pre><code>Mi_Meteo['Measurement'] = Mi_Meteo['Measurement'].str.rstrip(' Measure').str.strip()
Mi_Meteo['Measurement'] = Mi_Meteo['Measurement'].astype(flo... | python-3.x|string|pandas|multiple-columns | 1 |
369,695 | 55,385,497 | How can I convert my datetime column in pandas all to the same timezone | <p>I have a dataframe with a DataTime column (with Timezone in different formats). It appears like timezone is UTC but I want to convert the column to <code>pd.to_datetime</code> and that is failing. That is problem #1. Since that fails I cannot do any datetime operations on the time period such as group the column by ... | <p>I think that it is not necessary to apply lambdas:</p>
<pre><code>df_res['DateTime'] = pd.to_datetime(df_res['DateTime'], utc=True)
</code></pre>
<p>documentation: <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_datetime.html" rel="noreferrer">https://pandas.pydata.org/pandas-docs/sta... | python|pandas|datetime|type-conversion|timezone | 29 |
369,696 | 55,257,121 | Is there a way to convert data frame styler object into dataframe in python | <p>I have extracted xlsx data into pandas dataframe and used style.format to format particular columns into percentages and dollars. So now my dataframe is converted to styler object, because I need to parse this data into csv. I have to convert this object into dataframe please help.</p>
<p>below is the code and outp... | <p>You can retrieve the original dataframe from the styler object using the "data" attribute.</p>
<p>In your example:</p>
<p><code>df = final_df.data</code></p>
<p><code>type(df)</code> yields</p>
<p>pandas.core.frame.DataFrame</p> | python|pandas|pandas-styles | 9 |
369,697 | 55,284,019 | Create a month for every date between a period and make them columns | <p>I want to separate every month inside the period between the 'start' and 'end' column than I know I can use a pivot_table to make them columns:</p>
<pre><code>subscription|values| start | end
x |1 |5/5/2018 |6/5/2018
y |2 |5/5/2018 |8/5/2018
z |1 |5/5/2018 |9/5/2018
a ... | <p>Using simple <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.cumsum.html" rel="nofollow noreferrer"><code>pd.Series.cumsum</code></a></p>
<pre><code>import calendar
df2 = pd.DataFrame(np.zeros(shape=[len(df),13]),
columns=map(lambda s: calendar.month_abbr[s],
... | python|pandas|pivot-table | 1 |
369,698 | 55,567,688 | How to find the minimum distance .. when two points belongs to same distance | <p>I have a <code>dataframe</code> like this:</p>
<pre><code>A B
1 0.1
1 0.2
1 0.3
2 0.2
2 0.5
2 0.3
3 0.8
3 0.6
3 0.1
</code></pre>
<p>How can I find the minimum value belonging to each point 1,2,3 and there should be no conflict which means point 1 and 2 should not belong to same point 0.3..</p> | <p>If I understand correctly, you want to do two things:
- find the minimum <code>B</code> per distinct <code>A</code>, and
- make sure that they don't collide. You didn't specify what to do in case of collision, so I assume you just want to know if there is one.</p>
<p>The first can be achieved with Rarblack's answer... | python|pandas|matching|pairwise | 0 |
369,699 | 55,508,591 | How to identify outliers in a column data about test scores and return country names for outliers | <p>~What I've done~</p>
<p>In the first part of this assignment, I had to take data (from here: [a link] <a href="https://en.wikipedia.org/wiki/Programme_for_International_Student_Assessment_(2000_to_2012)" rel="nofollow noreferrer">https://en.wikipedia.org/wiki/Programme_for_International_Student_Assessment_(2000_to_... | <pre><code>tempDF = pd.DataFrame({'country': ['A']*1000+['B'], 'Income' : [10]*1000+[1000]})
def find_outlier(df, col):
return df[abs((df[col]-df[col].mean())/df[col].std())>1.8]['country'].values
# OR
#return df[np.abs((df[col]-np.mean(df[col]))/np.std(df[col]))>1.8]['country'].values
print ("The o... | python|pandas|dataframe|outliers | 0 |
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