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 |
|---|---|---|---|---|---|---|
4,300 | 59,579,316 | Can I use a str in np.mean()? | <pre><code>stdev = 3
value_1 = array
value_2 = array
value_3 = array
for h in range(1,4):
name = ('value' + str(h))
globals()['new_name_'+ str(h)] = np.mean(name) * stdev
</code></pre>
<p>It should give something like this: </p>
<pre><code>new_name_1 = #result1
new_name_2 = #result2
new... | <p>you can use a list to keep your arrays:</p>
<pre><code>stdev = 3
my_list = [array_1, array_2, array_3]
new_vars = []
for arr in my_list:
new_vars.append(np.mean(arr) * stdev)
</code></pre>
<p>also, you can keep your variables using a dict:</p>
<pre><code>stdev = 3
my_dict = {
'value_1': array_1, ... | python|string|numpy|for-loop|global-scope | 0 |
4,301 | 59,887,851 | "Error while extracting" from tensorflow datasets | <p>I want to train a tensorflow image segmentation model on COCO, and thought I would leverage the dataset builder already included. Download seems to be completed but <strong>it crashes on extracting the zip files.</strong></p>
<p>Running with TF 2.0.0 on a Jupyter Notebook under a conda environment. Computer is 64-b... | <p>I have a similar problem with Windows 10 & COCO 2017. My solution is simple. Extract the ZIP file manually according to the folder path in the error message.</p> | python|tensorflow|image-segmentation|tensorflow-datasets|mscoco | 2 |
4,302 | 40,441,631 | Get average counts per minute by hour | <p>I have a dataframe with a time stamp as the index and a column of labels</p>
<pre><code>df=DataFrame({'time':[ datetime(2015,11,2,4,41,10), datetime(2015,11,2,4,41,39), datetime(2015,11,2,4,41,47),
datetime(2015,11,2,4,41,59), datetime(2015,11,2,4,42,4), datetime(2015,11,2,4,42,11),
datetime(2015,11,2,4,42... | <p>Start by squeezing you DataFrame into a Series (after all, it only has one column):</p>
<pre><code>s = df.squeeze()
</code></pre>
<p>Compute how many times each label occurs by minute:</p>
<pre><code>counts_by_min = (s.resample('min')
.apply(lambda x: x.value_counts())
.unstack... | python|pandas|group-by | 1 |
4,303 | 40,432,081 | Check if numpy array is masked or not | <p>Is there an easy way to check if numpy array is masked or not? </p>
<p>Currently, I do the following to check if <code>marr</code> is masked or not:</p>
<pre><code>try:
arr = marr.data
except:
arr = marr
</code></pre> | <p>You can use the python function <code>isinstance</code> to check if an object is an instance of a class.</p>
<pre><code>>>> isinstance(np.ma.array(np.arange(10)),np.ma.MaskedArray)
True
>>> isinstance(np.arange(10),np.ma.MaskedArray)
False
</code></pre> | python|numpy | 9 |
4,304 | 40,663,543 | theory behind array manipulation | <p>I'm about to be asked to implement an array manipulation functionality somewhat akin to numpy (largish homogeneous arrays used to manipulate unpacked sequences of images and whatever our customers may derive from those images) for in-house scripting language. Naturally I'd like to limit it to the smallest amount of ... | <p>Travis Oliphant's 2006 book might be a good start.</p>
<pre><code>Guide to NumPy - Complexity Sciences Center
</code></pre>
<p><a href="https://www.google.com/url?sa=t&source=web&rct=j&url=http://csc.ucdavis.edu/~chaos/courses/nlp/Software/NumPyBook.pdf&ved=0ahUKEwjzlrv67bDQAhVUzWMKHV-tDToQFggjMAE&... | arrays|numpy | 1 |
4,305 | 40,572,910 | Most efficient method to combine pandas DataFrames which have the same column value | <p>For example, I have two dataframe which contain some identical sample name with different feature data. </p>
<p>I want to compare how many samples existed in both dataframe. </p>
<h3>data here</h3>
<p><a href="https://drive.google.com/file/d/0B7FE0kxAL8kQQlRFSmR6Q1RoNXM/view?usp=sharing" rel="nofollow noreferrer... | <p>Here's a vectorized approach using <a href="https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html" rel="nofollow noreferrer"><code>NumPy broadcasting</code></a> to get <code>hit</code> value -</p>
<pre><code>np.count_nonzero(df1.Sample_name.values[:,None] == df2.Sample_name.values)
</code></pre> | python|performance|python-2.7|pandas|join | 2 |
4,306 | 18,241,359 | "Error: setting an array element with a sequence" | <p>I am trying to convert Matlab code into Python, but I'm receiving an error when I append zeros in my array.</p>
<p>Matlab Code:</p>
<pre><code>N_bits=1e5;
a1=[0,1];
bits=a1(ceil(length(a1)*rand(1,N_bits)));
bits=[0 0 0 0 0 0 0 0 bits];
</code></pre>
<p>Python Code:</p>
<pre><code>a1=array([0,0,1])
N_bits=1e2
a2=... | <p>You want to join the list with the array, so try</p>
<pre><code>bits=concatenate(([0,0,0,0,0,0,0,0], bits))
</code></pre>
<p>where <code>concatenate()</code> is <a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.concatenate.html#numpy.concatenate" rel="nofollow noreferrer"><code>numpy.concatenate()... | python|matlab|numpy | 5 |
4,307 | 61,693,747 | How to specify dt.week to use north american week number in dataframe pandas? | <p>How can I specify dt.week to use north american week number?</p>
<p><em>2019 Calendar:</em></p>
<p><a href="https://i.stack.imgur.com/Ye5to.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/Ye5to.png" alt="enter image description here"></a></p>
<p>For example: <strong>Date -> 01.01.2020 is Week 1... | <p>You can do this:</p>
<pre><code>df['Date'] = pd.to_datetime(df['Date'])
df['Week'] = df['Date'].dt.strftime('%U').astype(np.int) + 1
</code></pre>
<p><strong>Output</strong>:</p>
<pre><code> SENDER_NAME Induction Date InductionPlant Week
0 0 b'XXXXXXXXXXXX' 2020-01-03 b'XXXXXXXXXX' ... | python|pandas|dataframe | 1 |
4,308 | 61,900,343 | Python np.logical_and operands could not be broadcast together with shapes | <p>I have <code>Y_1</code> and <code>X_1</code> as a matrix of (506,1) as shape, both. I'd like to know the Y_1 values when X_1 values are greater than 5 and less than 6. Just like this.</p>
<pre><code>np.logical_and(Y_1[X_1 > 5], Y_1[X_1 < 6])
</code></pre>
<p>But I got this error.</p>
<blockquote>
<p>Value... | <p>You are using the logical and in a wrong way here. The arguments you are passing here, namely, Y_1[X_1>5] and Y_1[X_1<6] have different shapes as given in the error. Their shapes have to be same. There are 490 elements in X_1 greater than 5 and 173 elements less than 6.</p>
<p>It should be like np.logical_and(X_... | python|numpy | 0 |
4,309 | 61,780,569 | How many addition operations are being performed by np.sum()? | <p>Lets consider I have an array of shape <code>(1, 3, 4, 4)</code> and I apply <code>numpy.sum()</code> on this and reduce against <code>axes [2,3]</code>. Below is a sample code --</p>
<pre><code>import numpy as np
data = np.random.rand(1, 3, 4, 4)
res = np.sum(data, axis=(2,3), keepdims=True)
</code></pre>
<p>How... | <pre><code>In [202]: data = np.arange(3*4*4).reshape(1,3,4,4)
</code></pre>
<p>do your sum:</p>
<pre><code>In [203]: res = np.sum(data, axis=(2,3), keepdims=True)
In [204]: res ... | python|numpy | 1 |
4,310 | 61,653,697 | Pandas str.replace() with regex | <p>Say I have this dataframe:</p>
<pre><code>df = pd.DataFrame({'Col': ['DDJFHGBC', 'AWDGUYABC']})
</code></pre>
<p>And I want to replace everything ending with <code>ABC</code> with <code>ABC</code> and everything ending with <code>BC</code> (except the <code>ABC</code>-cases) with <code>BC</code>. The output would ... | <p>You could match as least word chars using <code>\w*?</code> and then capture in group 1 matching an optional A followed by BC <code>(A?BC)</code> followed by a word boundary.</p>
<pre><code>\w*?(A?BC)\b
</code></pre>
<p><a href="https://regex101.com/r/2EaKua/1" rel="nofollow noreferrer">Regex demo</a></p>
<p>In t... | python|regex|string|pandas | 3 |
4,311 | 61,838,277 | Faster Outer-Product-Like find closest lat long with function pandas | <p>I have a pandas dataframe A, with latitude longitudes.</p>
<pre><code>import pandas as pd
df_a = pd.DataFrame([['b',1.591797,103.857887],
['c',1.589416, 103.865322]],
columns = ['place','lat','lng'])
</code></pre>
<p>I have another dataframe of locations B, also with latitude long... | <p>Using an approach from <a href="https://stackoverflow.com/questions/10549402/kdtree-for-longitude-latitude">KDTree for longitude/latitude</a></p>
<p>Based upon <a href="https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.BallTree.html" rel="nofollow noreferrer">sklearn.balltree</a></p>
<p><strong>C... | python|pandas | 1 |
4,312 | 57,982,376 | Pytorch tensor multiplication with Float tensor giving wrong answer | <p>I am seeing some strange behavior when i multiply two pytorch tensors.</p>
<pre class="lang-py prettyprint-override"><code>x = torch.tensor([99397544.0])
y = torch.tensor([0.1])
x * y
</code></pre>
<p>This outputs </p>
<pre><code>tensor([9939755.])
</code></pre>
<p>However, the answer should be <code>9939754.4</... | <p>In default, the tensor dtype is <code>torch.float32</code> in pytorch. Change it to <code>torch.float64</code> will give the right result. </p>
<pre class="lang-py prettyprint-override"><code>x = torch.tensor([99397544.0], dtype=torch.float64)
y = torch.tensor([0.1], dtype=torch.float64)
x * y
# tensor([9939754.400... | pytorch | 2 |
4,313 | 58,095,242 | Numpy: Understanding Array to the power of Array | <p>I was reviewing operators with numpy arrays and I found something I did not expect and that I do not know how to interpret.</p>
<p>The operation I am performing is an array A to the power of an array B, clearly with A and B having the same shape. The behaviour I am expecting is 'element-wise power': each element of... | <p><code>dtype=int32</code> you're overflowing integers and looping back:</p>
<pre><code>1099511627776 % (2**32) == 0
298023223876953125 % (2**32) == 167814181
...
</code></pre> | python|arrays|numpy|matrix | 1 |
4,314 | 58,089,395 | Usage of str.contains() applied to pandas data frame | <p>I am new to Python and Jupyter Notebook and I am currently following this tutorial: <a href="https://www.dataquest.io/blog/jupyter-notebook-tutorial/" rel="nofollow noreferrer">https://www.dataquest.io/blog/jupyter-notebook-tutorial/</a>. So far I've imported the pandas library and a couple other things, and I've ma... | <p>The expression <code>[^0-9.-]</code> is a so-called <em>regular expression</em>, which is a special text string for describing a search pattern. With regular expressions (or in short '<em>RegEx</em>') you can extract specific parts of a string. For example, you can extract <code>foo</code> from the string <code>123f... | python|string|pandas|jupyter | 3 |
4,315 | 58,152,368 | Graph a multi index dataframe in pandas | <p>I have a multi indexed file with values such as these</p>
<p><a href="https://i.stack.imgur.com/5OjXc.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/5OjXc.png" alt=""></a></p>
<p>How could I plot a dataframe that has separate lines for each symbol in the same graph?</p> | <p>I believe you need <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.pivot.html" rel="nofollow noreferrer"><code>pivot</code></a> for all unique <code>symbol</code> values :</p>
<pre><code>df1 = df.pivot(index='4.timestamp', columns='1.symbol', values='2.price')
</code></pre>
<p>If possible... | python|pandas | 1 |
4,316 | 57,886,871 | Variables are same for all Epochs | <p>I am experimenting the image classifier using CNN with keras</p>
<p>My code -</p>
<pre><code>model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same', input_shape=(224, 224, 3), activation="relu"))
model.add(Conv2D(32, (3, 3), padding='same', activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
... | <p>Try changing this</p>
<pre><code>model.compile(loss = "categorical_crossentropy", optimizer = "rmsprop")
</code></pre>
<p>to </p>
<pre><code>model.compile(loss = "categorical_crossentropy", optimizer = 'adam')
</code></pre> | python|tensorflow|keras|neural-network|conv-neural-network | 1 |
4,317 | 34,178,751 | Extract unique values and number of occurrences of each value from dataframe column | <p>I'm trying to extract the number of each unique entry from one dataframe column and store it as a new dataframe, something like this:</p>
<p><strong>Input</strong></p>
<pre><code>sample_name
sample1
sample2
sample2
sample3
sample3
sample3
</code></pre>
<p><strong>Desired output</strong></p... | <p>You want <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.value_counts.html" rel="noreferrer"><code>value_counts</code></a>:</p>
<pre><code>In [142]:
df['sample_name'].value_counts()
Out[142]:
sample3 3
sample2 2
sample1 1
Name: sample_name, dtype: int64
</code></pr... | python|pandas | 5 |
4,318 | 37,091,953 | array manipulation from MATLAB to Python | <p>from MATLAB code:</p>
<pre><code>a = rand(1,120);
d=zeros(1,124);
state=[1:120];
fibre = [1 5 9 13 17 2 6 10 14 18 3 7 11 15 19 4 8 12 16 20 21 69 65 61 57 22 68 64 60 56 71 67 63 59 55 70 66 62 58 54 53 49 45 41 37 52 48 44 40 36 51 47 43 39 ... | <p>Your python script has two problem regarding to Matlab code:
In the second line you should generate random numbers such as:</p>
<pre><code>a = np.random.rand(120)
</code></pre>
<p>and in the last line, like in comments said, you should know indexing in Matlab starts with 1 and python starts with 0, so your last li... | python|arrays|matlab|numpy | 1 |
4,319 | 55,098,643 | list augmentation in python with numpy or other library | <p>I would like to augment list from</p>
<pre><code>[1, 2, 3, 4, 5]
</code></pre>
<p>to</p>
<pre><code>[1, 1, 2, 2, 3, 3, 4, 4, 5, 5]
</code></pre>
<p>If I want to augment likewise n times (like 100 or 500 times), how can I do it? I do not want to do it with regular loop, but using some library like numpy. Any help... | <p>You can do this with numpy's <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.repeat.html" rel="nofollow noreferrer"><code>np.repeat</code></a>:</p>
<pre><code>import numpy as np
a = np.array([1, 2, 3, 4, 5])
np.repeat(a,2)
# array([1, 1, 2, 2, 3, 3, 4, 4, 5, 5])
</code></pre> | python|list|numpy|data-augmentation | 2 |
4,320 | 49,626,150 | Python list-dictionary algorithm with big data | <p>I'm trying to make a program using two python dictionaries.
'multiple dic1 and dic2 value if dic1 and dic2 key is common, otherwize 0'
the key order and the length of the output list is the same as those of dic1.</p>
<pre><code>dic1 = {'foo': 100,'bar': 200,'baz': 300,'qux': 400,'quux': 500}
dic2 = {'foo': 1,'quux'... | <p>Hmm, I don't think there is much you can do. Where is this data coming from? If it's a csv or something then a <code>pandas</code> solution would probably be quicker. If they must be <code>dict</code>s then I think the best thing I can think of is to change it to a comprehension</p>
<pre><code>output = [v * dic2[k]... | python|python-3.x|list|numpy|python-3.6 | 4 |
4,321 | 73,189,955 | Serializing complex object containing multiple nested objects with data frames | <p>Below should be a runnable sample of code. i have a Chart1 object which can contain many panes, and each pane can can contain many series. I would like to serialize this to json so i can send to a flask application to render. To do deal with the dataframes, i am using a custom encoder (ChartEncoder below):</p>
<... | <p>For starters, the JSON that is emitted is perfectly parsble by Javascript <code>JSON.load</code>, the issue is that your <code>default</code> implementation returns a <code>str</code> object, so that gets serialized as a JSON <code>str</code>.</p>
<p>You probably want to use <code>to_dict</code> (which returns a <co... | python|json|pandas | 1 |
4,322 | 35,212,271 | Numpy create two arrays using fromiter simultaneously | <p>I have an iterator that looks something like the following</p>
<pre><code>it = ((x, x**2) for x in range(20))
</code></pre>
<p>and what I want is two arrays. one of the <code>x</code>s and the other of the <code>x**2</code>s but I don't actually know the number of elements, and I can't convert from one entry to th... | <p>You could use <code>np.fromiter</code> to build one array with all the values, and then slice the array:</p>
<pre><code>In [103]: it = ((x, x**2) for x in range(20))
In [104]: import itertools
In [105]: y = np.fromiter(itertools.chain.from_iterable(it), dtype=float)
In [106]: y
Out[106]:
array([ 0., 0., ... | python|arrays|numpy | 2 |
4,323 | 59,920,461 | Python 3.8.1: ModuleNotFoundError: No module named '_pywrap_tensorflow_internal' - Is tensorflow only supported up to 3.7? | <p>I am using Python 3.8.1 on Windows 10 and am trying to install TensorFlow. </p>
<p>I have tried many methods to install it, but I keep getting the following error upon importing TensorFlow. This time, I installed it using</p>
<pre><code>conda install -c conda-forge tensorflow
</code></pre>
<p>Here is the stack tr... | <p>Per the installation documents, only Python 3.5-3.7 are supported. <a href="https://www.tensorflow.org/install" rel="nofollow noreferrer">https://www.tensorflow.org/install</a></p> | python-3.x|tensorflow|anaconda|conda | 0 |
4,324 | 60,087,418 | Iterate over pandas dataframe and apply condition | <p>Consider I have this dataframe, wherein I want to remove toy as a topic from the topics column and if there is row with a single topic as a toy , remove that row. How can we do that in pandas?</p>
<pre><code>+---+-----------------------------------+-------------------------+
| | Comment ... | <p>Try using <code>str.replace</code> with <code>str.rstrip</code> and <code>ne</code> inside <code>[...]</code>:</p>
<pre><code>df['topic'] = df['topic'].str.replace('toy', ' ').str.replace(' , ', '').str.rstrip()
print(df[df['topic'].ne('')])
</code></pre> | python|pandas | 1 |
4,325 | 59,971,737 | Numpy Gaussian from /dev/urandom | <p>I have an application where I need <code>numpy.random.normal</code> but from a crypgoraphic PRNG source. Numpy doesn't seem to provide this option.</p>
<p>The best I could find was <code>numpy.random.entropy.random_entropy</code> but this is only uint32 and it's buggy, with large arrays you get "RuntimeError: Unabl... | <p>I came across <code>scipy.stats.rvs_ratio_uniforms</code> and adapted their code for my purpose. It's only 3 times slower than <code>np.random.normal</code> despite sampling twice the randomness from a cryptographic source.</p>
<pre><code>import numpy as np
import os
def uniform_0_1(size):
return np.frombuffe... | python|numpy|random|gaussian|normal-distribution | 1 |
4,326 | 50,020,683 | which one is effecient, join queries using sql, or merge queries using pandas? | <p>I want to use data from multiple tables in a <code>pandas dataframe</code>. I have 2 idea for downloading data from the server, one way is to use <code>SQL</code> join and retrieve data and one way is to download dataframes separately and merge them using pandas.merge.</p>
<h1>SQL Join</h1>
<p>when I want to down... | <p>To really know which is faster, you need to try out the two queries using your data on your databases.</p>
<p>The rule of thumb is to do the logic in a single query. Databases are designed for queries. They have sophisticated algorithms, multiple processors, and lots of memory to handle them. So, relying on the ... | python|sql|postgresql|pandas | 3 |
4,327 | 64,080,487 | How to groupby, aggregate and plot a bar plot? | <p>I used this code from starting</p>
<pre><code>import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('odi.csv')
df=pd.DataFrame(dataset)
</code></pre>
<p>I am using dataset and then i groupby in a column by country and then i took average run scored by each country. I used this ... | <ul>
<li><a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html" rel="nofollow noreferrer"><code>pandas.read_csv</code></a> creates a DataFrame, so it's not correct to create <code>dataset</code>, and then <code>df = pd.DataFrame(dataset)</code></li>
<li><a href="https://pandas.pydata.... | python|pandas|matplotlib|bar-chart | 1 |
4,328 | 64,099,194 | AttributeError: 'Concatenate' object has no attribute 'shape' | <p>I'm trying to do some image segmentation in tensorflow, here is my model :</p>
<pre><code>inputs = Input((IMAGE_HEIGHT, IMAGE_WIDTH, 3))
s = Lambda(lambda x: x / 255) (inputs)
conv1 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (inputs)
conv1 = BatchNormalization() (conv1)
c... | <p>Try to use:</p>
<pre><code>upsample7 = Concatenate()([upsample7, conv3])
</code></pre>
<p>and update each upsample and where you use the Concatenate function.</p>
<p>Note the <code>()</code> in Concatenate ^^</p> | python|tensorflow|keras|tensorflow2.0 | 3 |
4,329 | 64,002,915 | While running tensorboard command and i am getting error? | <p><code>!tensorboard --logdir=drive/My Drive/Proj/fer/checkpoint/logs/</code></p>
<p>**i am running this command in google colab **</p>
<p><a href="https://i.stack.imgur.com/ZQhgw.png" rel="nofollow noreferrer">getting this error</a></p> | <p>You get this error because of the empty space in 'My Drive'. So you need to escape the whitespace or rename it:</p>
<pre><code>!tensorboard --logdir=drive/My\ Drive/Proj/fer/checkpoint/logs/
</code></pre> | python|tensorflow|tensorboard | 0 |
4,330 | 64,020,403 | Pandas multiply selected columns by previous column | <p>Assume I have a 3 x 9 Dataframe with index from 0 - 2 and columns from 0 - 8</p>
<pre><code>nums = np.arange(1, 28)
arr = np.array(nums)
arr = arr.reshape((3, 9))
df = pd.DataFrame(arr)
</code></pre>
<p>I want to multiply selected columns (example [2, 5, 7]) by the columns behind them (example [1, 4, 6])
My obstacle... | <p>Let's try working with the numpy array:</p>
<pre><code>cols = np.array([2,5,7])
df[cols] *= df[cols-1].values
</code></pre>
<p>Output:</p>
<pre><code> 0 1 2 3 4 5 6 7 8
0 1 2 6 4 5 30 7 56 9
1 10 11 132 13 14 210 16 272 18
2 19 20 420 22 23 552 25 650 27
</... | python|pandas | 3 |
4,331 | 63,902,183 | How do I plot a beautiful scatter plot with linear regression? | <p>I want to make a beautiful <code>scatter plot</code> with <code>linear regression</code> line using the data given below. I was able to create a scatter plot but am not satisfied with how it looks. Additionally, I want to plot a <code>linear regression</code> line on the data.</p>
<p>My data and code are below:</p>
... | <p>Please check the snippet. You can use <code>numpy.polyfit()</code> with degree=1 to calculate slope and y-intercept of line to <code>y=m*x+c</code>
<a href="https://i.stack.imgur.com/TZBA3.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/TZBA3.png" alt="graph" /></a></p>
<pre><code>import numpy as ... | python|pandas|matplotlib|seaborn|python-ggplot | 3 |
4,332 | 64,102,826 | Efficient method comparing 2 different tables columns | <p><img src="https://i.stack.imgur.com/Lopgz.png" alt="Example_List" /></p>
<p>Hi all guys,</p>
<p>I have got 2 dfs and I need to check if the values from the first are matching on the second, only for a specific column on each, and save the values matching in a new list. This is what I did but it is taking quite a lot... | <p>Let's create both dataframes:</p>
<pre><code>df1 = pd.DataFrame({
'Building_Name': ['Exces', 'Excs', 'Exec', 'Executer', 'Executor']
})
df2 = pd.DataFrame({
'Source_String': ['Executer', 'Executor', 'Executor Of', 'Executor For', 'Exeutor']
})
</code></pre>
<p>Perform inner merge between dataframes and conv... | python|python-3.x|pandas|list|string-matching | 0 |
4,333 | 47,040,238 | Cleanest way to bin using Pandas.cut | <p>The purpose of this post is discussion primarily, so even loose ideas or strings to pull would be appreciated. I'm trying to bin some data for analysis, and was wondering what is the cleanest way to bin my data using <code>Pandas.cut</code>. For some context, I'm specifically trying to bin ICD-9 diagnostic data into... | <p>I would simply write a small helper function. Here's one idea:</p>
<pre><code>import pandas as pd
def bin_helper(code_dict):
break_points = [0] + sorted(code_dict) #0 added for lower bound on binning
labels = [code_dict[value] for value in sorted(code_dict)]
return break_points, labels
# Setting up so... | python|pandas|icd | 2 |
4,334 | 46,693,557 | Finding closest point in array - inverse of KDTree | <p>I have a very large ndarray A, and a sorted list of points k (a small list, about 30 points).</p>
<p>For every element of A, I want to determine the closest element in the list of points k, together with the index. So something like:</p>
<pre><code>>>> A = np.asarray([3, 4, 5, 6])
>>> k = np.asar... | <p>Update:</p>
<p>The builtin function <a href="https://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.digitize.html" rel="nofollow noreferrer"><code>numpy.digitize</code></a> can actually do exactly what you need. Only a small trick is required: <code>digitize</code> assigns values to <em>bins</em>. We can... | python|arrays|algorithm|numpy|scipy | 2 |
4,335 | 32,816,410 | Parallelize loop over numpy rows | <p>I need to apply the same function onto every row in a numpy array and store the result again in a numpy array.</p>
<pre><code># states will contain results of function applied to a row in array
states = np.empty_like(array)
for i, ar in enumerate(array):
states[i] = function(ar, *args)
# do some other stuff o... | <h3>Dask solution</h3>
<p>You could do with with dask.array by chunking the array by row, calling <code>map_blocks</code>, then computing the result</p>
<pre><code>ar = ...
x = da.from_array(ar, chunks=(1, arr.shape[1]))
x.map_blocks(function, *args)
states = x.compute()
</code></pre>
<p>By default this will use thr... | python|numpy|dask | 6 |
4,336 | 38,657,341 | Pandas: groupby by date and transform nunique returning too many entries | <p>I am trying to do a simple group-by in Pandas and it is not working as it should:</p>
<pre><code>url='https://raw.githubusercontent.com/108michael/ms_thesis/master/raw_bills'
bills=pd.read_csv(url)
bills.date.nunique()
11
bills.dtypes
date float64
bills object
id.thomas int64
dtype: object
... | <p>I'm not sure what you're asking, but don't you want to use:</p>
<pre><code>bills[['date', 'bills']].groupby('date').bills.nunique()
date
2005.0 6820
2006.0 3738
2007.0 7454
2008.0 3627
2009.0 7324
2010.0 3297
2011.0 5787
2012.0 4647
2013.0 5694
2014.0 3211
2015.0 5
Name: bills, ... | python|pandas|group-by | 2 |
4,337 | 38,588,668 | Why is prediction not plotted? | <p>Here is my code in Python 3:</p>
<pre><code>from sklearn import linear_model
import numpy as np
obj = linear_model.LinearRegression()
allc = np.array([[0,0],[1,1],[2,2],[3,3],[4,4],[5,5],[6,6]])
X=allc[:,0]
X=X.reshape(-1, 1)
Y=X.reshape(X.shape[0],-1)
obj.fit(X, Y)
print(obj.predict(7))
import matplotlib.pyplot as... | <p>The plot method is taking an array for the X-axis and an array for the Y-axis, and draws a <strong>line</strong> according to those arrays. You tried to draw a <strong>point</strong> using a method for <strong>lines</strong>...<br></p>
<p>For your code to work (I have tested it and it worked) switch this line:</p>
... | python|python-3.x|numpy|matplotlib|linear-regression | 0 |
4,338 | 63,296,247 | print column values of one dataframe based on the column values of another dataframe | <p><img src="https://i.stack.imgur.com/U32iE.png" alt="enter image description here" /> <img src="https://i.stack.imgur.com/VWHgj.png" alt="enter image description here" /></p>
<p>So here is my first dataframe df1. In the columns, Starting DOY and Ending DOY, for example, 3.0 and 6.0, I want to print column values By, ... | <p>Here is a simple tutorial how you can do it:</p>
<pre><code>from pandas import DataFrame
if __name__ == '__main__':
data1 = {'Starting DOY': [3.0, 3.0, 13.0],
'Ending DOY': [6.0, 6.0, 15.0]}
data2 = {'YEAR': [1975, 1975, 1975],
'DOY': [1.0, 3.0, 6.0],
'HR': [0, 1, 2],... | python|pandas|numpy|matplotlib|math | 0 |
4,339 | 63,107,329 | Generation of 3-D array | <p>I want to generate a 3-D array by assigning an array to an array.<br/>
Following are codes written by me.<br/></p>
<pre><code>import numpy as np
def func01(a):
b = np.array([[a, 3],
[4, 5]])
return b
a = np.array([1, 2])
b = func01(a)
print(b)
</code></pre>... | <pre><code>In [210]: res = np.zeros((2,2,2),int)
In [211]: res[:] = np.array([[0,3],[4,5]])
In [212]: res
Out... | python|arrays|numpy|scipy | 1 |
4,340 | 68,017,780 | Stack rows over two columns | <p>i want to stack the rows with the same ID and OP Date in one row toghether.</p>
<p>Source:</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th>ID</th>
<th>OP Date</th>
<th>OP Code</th>
</tr>
</thead>
<tbody>
<tr>
<td>1</td>
<td>01.01.2021</td>
<td>X1</td>
</tr>
<tr>
<td>1</td>
<td>01.01.2021... | <p>You can parse it:</p>
<pre><code>import pandas as pd
from collections import defaultdict
data=[{"id": 1, "OP Date": "01.01.2021", "OP Code": "X1"},
{"id": 1, "OP Date": "01.01.2021", "OP Code": "X2"}]
parsed_op... | python|pandas | 0 |
4,341 | 67,645,828 | Passing arguments to extended keras.model without init | <p><a href="/questions/tagged/keras" class="post-tag" title="show questions tagged 'keras'" rel="tag">keras</a> documentation provides an <a href="https://keras.io/guides/customizing_what_happens_in_fit/" rel="nofollow noreferrer">example</a> of extending the <a href="/questions/tagged/keras" class="post-tag" t... | <p>The CustomModel example allows one to override specific methods of the Model class (<code>train_step</code>) in the example.
When you call</p>
<pre><code>model = CustomModel(inputs, outputs)
</code></pre>
<p>inputs is a tensor or list of input tensors to the custom model and outputs the output tensor(s).</p>
<p>You... | python|tensorflow|keras | 0 |
4,342 | 67,704,886 | pytorch : retain_graph=True error even though i add this | <p>I keep getting this error
<strong>"Trying to backward through the graph a second time, but the saved intermediate results have already been freed. Specify retain_graph=True when calling .backward() or autograd.grad() the first time."</strong></p>
<p>in the beginning, it was without retain_graph=True and th... | <p>Basically you can only call <code>optimizer.step()</code> after these three lines.</p>
<pre><code> output = net2(inputs)
loss = criterion(outputs, labels)
loss.backward()
</code></pre>
<p>I don't know the rest of your code so I can only guess.</p>
<p>But I think you didn't have <code>net(input... | python|machine-learning|pytorch | 0 |
4,343 | 41,429,956 | Python: Create a model for reports (using pandas) | <p>This is more of a model design question with python.</p>
<p>I need to parse and extract data from several log files into a pandas DataFrames.
From these dataframes I need to create reports (as csv, excel and so on).</p>
<p>One way of design such is to create a file with 2 functions:
1. function to extract data fro... | <p>You need <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.core.groupby.SeriesGroupBy.nlargest.html" rel="nofollow noreferrer"><code>SeriesGroupBy.nlargest</code></a>:</p>
<pre><code>df = names.groupby(['year', 'sex'])['births'].nlargest(1000)
</code></pre>
<p>Sample:</p>
<pre><code>names = pd... | python|pandas | 2 |
4,344 | 61,412,939 | Python Tensorflow-GPU: "Successfully opened dynamic libraray cudart64_101.dll" | <p>I am currently just trying to get tensorflow-gpu to work on my PC. When I run my script, consisting only of:</p>
<pre class="lang-py prettyprint-override"><code>import tensorflow
print("Test")
</code></pre>
<p>... then I get the output:</p>
<pre><code>2020-04-24 18:16:53.660911: I tensorflow/stream_executor/platf... | <p>I have the same problem , that's the only solution works with me:</p>
<ol>
<li><p>check the folder path <code>C:\Users\user\Anaconda3\Lib\site-packages</code> . if any folder begins with <code>~</code>, delete it.</p>
</li>
<li><p>in the anaconda prompt run the following commands :</p>
<pre class="lang-sh prettyprin... | python|tensorflow | 0 |
4,345 | 61,537,916 | Map column birthdates in python pandas df to astrology signs | <p>I have a dataframe with a column that includes individuals' birthdays. I would like to map that column to the individuals' astrology sign using code I found (below). I am having trouble writing the code to creat the variables. </p>
<p>My current dataframe looks like this</p>
<pre><code> birthdate answer YEAR... | <p>Change previous answer by <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.dt.month_name.html" rel="nofollow noreferrer"><code>Series.dt.month_name</code></a> with lowercase strings:</p>
<pre><code>def zodiac_sign(day, month):
# checks month and date within the valid range
... | python|pandas | 1 |
4,346 | 61,472,491 | For Loop and The truth value of a Series is ambiguous | <p>Looked through the answers to similar queries here but still unsure. Code below produces:</p>
<pre><code> for i in range(len(df)):
if df[0]['SubconPartNumber1'].str.isdigit() == False :
df['SubconPartNumber1'] = df['SubconPartNumber1'].str.replace(',', '/', regex = True)
... | <p>In pandas you can <a href="https://stackoverflow.com/a/55557758/2901002">avoid loops</a> if possible. Your solution should be replace by <code>boolean mask</code> with <code>~</code> for invert instead <code>== False</code> and passed to <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataF... | python|pandas|series|valueerror | 2 |
4,347 | 61,233,759 | Find value close to number in python array and index it | <p>This is the code I have so far:</p>
<pre><code>import numpy as np
#make amplitude and sample arrays
amplitude=[0,1,2,3, 5.5, 6,5,2,2, 4, 2,3,1,6.5,5,7,1,2,2,3,8,4,9,2,3,4,8,4,9,3]
#print(amplitude)
#split arrays up into a line for each sample
traceno=5 #number of traces in file
samplesno=6 ... | <p>IIUC, you could do the following:</p>
<pre><code># find the indices of the min absolute difference
indices = np.argmin(np.abs(amplitude_split - amp_90[:, None]), axis=1)
# get the values at those positions
result = amplitude_split[np.arange(5), indices]
print(result)
</code></pre> | python|arrays|numpy | 1 |
4,348 | 61,239,125 | Join Dataframes by column and create new columns by value Pandas Python | <p>I have 2 dataframes df1 and df2, I'm trying to merge then by column (product). </p>
<pre><code>df1
product name Exist
0 1 foo False
1 2 bar True
2 3 lorem False
3 4 ipsum False
.
.
df2
product date_search sold
0 1 2020-04-10 10
1 1 2020-04-... | <p>@Quang Hoang nailed it -- just do this:</p>
<pre><code>import pandas as pd
dict1 = {"product" : [1,2,3,4], "name" : ['foo','bar','lorem','ipsum'], "exists": ['false','true','false','false']}
dict2 = {"product" : [1,1,2,2,3,3,4,4], "date": ['2020-01-01','2020-01-02','2020-01-01','2020-01-02','2020-01-01','2020-01-0... | python|pandas|dataframe|join|merge | 0 |
4,349 | 61,370,108 | tf.data: Parallelize loading step | <p>I have a data input pipeline that has:</p>
<ul>
<li>input datapoints of types that are not castable to a <code>tf.Tensor</code> (dicts and whatnot)</li>
<li>preprocessing functions that could not understand tensorflow types and need to work with those datapoints; some of which do data augmentation on the fly</li>
<... | <p>You can try to add <code>batch()</code> before <code>map()</code> in your input pipeline.</p>
<p>It is usually meant to reduce the overhead of the map function call for small map function, see here:
<a href="https://www.tensorflow.org/guide/data_performance#vectorizing_mapping" rel="nofollow noreferrer">https://www... | python|tensorflow|tensorflow2.0|tensorflow-datasets | 2 |
4,350 | 61,211,861 | Why my autoencoder model is not learning? | <p>I'm trying to solve captcha dataset using autoencoder. The <a href="https://www.kaggle.com/fournierp/captcha-version-2-images" rel="nofollow noreferrer">dataset</a> is RGB images.</p>
<p>I converted the RGB images to one channel, i.e.:</p>
<p><a href="https://i.stack.imgur.com/1DXbH.png" rel="nofollow noreferrer">... | <ol>
<li>Your architecture doesn't have any sense. If you want to create an autoencoder you need to understand that you're going to reverse process after encoding. That means that if you have three convolutional layers with filters in this order: 64, 32, 16; You should make the next group of convolutional layers to do ... | keras|conv-neural-network|tensorflow2.0|autoencoder | 1 |
4,351 | 68,505,527 | Pandas Sales Analysis Help - ValueError: could not convert string to float: '' | <p>I'm currently running a sales analysis on an excel file with roughly 500 transactions. I have a category called "Sale Price" which should be read in as a float. Pandas read in the dtype as an object, and when trying to change the dtype to a float using:</p>
<pre><code>df['Sale Price'].fillna(0).astype(floa... | <p>You could replace your empty strings with 0 before changing it to float:</p>
<pre><code>df["Sale Price"] = df["Sale Price"].astype(str).str.strip().replace("",0).astype(float)
</code></pre> | python|pandas | 1 |
4,352 | 53,300,337 | variable_scope does not get reused when using default scope name | <p>I have a question regarding sub-scopes when reusing variables. This</p>
<pre><code>import tensorflow as tf
def make_bar():
with tf.variable_scope('bar'):
tf.get_variable('baz', ())
with tf.variable_scope('foo') as scope:
make_bar()
scope.reuse_variables()
make_bar()
</code></pre>
<p>works perfectly ... | <p>You're not actually using the scope 'foo' in your example. You need to pass the parameter to <code>tf.variable_scope('foo', 'bar')</code> or <code>tf.variable_scope(scope, 'bar')</code>.
you're calling the method <code>make_bar</code> without the parameter in either case, which means in your first example <code>name... | python|tensorflow | 4 |
4,353 | 52,922,647 | Rotate covariance matrix | <p>I am generating 3D gaussian point clouds. I'm using the scipy.stats.multivariate.normal() function, which takes a mean value and a covariance matrix as arguments. It can then provide random samples using the rvs() method.</p>
<p>Next I want to perform a rotation of the cloud in 3D, but rather than rotate each point... | <p>I edited the question with the answer, but again it is</p>
<pre><code>new_cov = rotation_matrix @ cov @ rotation_matrix.T
</code></pre> | numpy|scipy|linear-algebra|covariance|covariance-matrix | 2 |
4,354 | 63,342,185 | Crosstab Output in Python | <p>Firstly I am pretty new to Python and I'm trying to output the view from a crosstab:</p>
<pre><code>import pandas as pd
Data_18['age_50_flag'] = (Data_18['age'] > 50).astype(int)
pd.crosstab(Data_18.age_50_flag, Data_18.age)
</code></pre>
<p>The output however, is being buried:</p>
<pre><code>age 16 17 18 19... | <p>assume you have person and age pairs of data then find the individuals whose age is greater than 50 and display the results in a crosstab</p>
<pre><code>age=[16, 17, 20, 24,96]
name=['A','B','C','D','E']
df=pd.DataFrame({'name':name,'age':age})
df['greater50']=df['age'].apply(lambda person: 1 if person>50 else 0)... | python|pandas|crosstab | 0 |
4,355 | 63,532,399 | Incompatible Shapes: Tensorflow/Keras Sequential LSTM with Autoencoder | <p>I am trying to set up an LSTM Autoencoder/Decoder for time series data and continually get <code>Incompatible shapes</code> error when trying to train the model. Following steps and using toy data from <a href="https://towardsdatascience.com/step-by-step-understanding-lstm-autoencoder-layers-ffab055b6352" rel="nofol... | <p>As the message clearly says, it's the shape issue which you are passing to the model for fit.</p>
<p>From the above data which you have given X is having the shape of <code>(6, 3, 2)</code> and Y is having the shape of <code>(6, 2)</code> which is incompatible.</p>
<p>Below is the modified code with the same input a... | python|tensorflow|keras|deep-learning|lstm | 0 |
4,356 | 63,381,217 | Make lists out of column values from one column, filtering on values from another | <p>I have a pandas dataframe like :</p>
<pre><code> Fields Player bio Team
0 Name 1 2
1 city 2 2
2 state 1 1
3 stage 0 0
4 effec 2 2
5 points 1 2
</code></pre>
<p>I would like to make lists named... | <p>You can do it like this:</p>
<pre><code>selected_fields = ['Player bio', 'Team']
s = (df==2).T.dot(','+df['Fields']).str.strip(',')\
.str.split(',').reindex(selected_fields)
s
</code></pre>
<p>Output:</p>
<pre><code>Player bio [city, effec]
Team [Name, city, effec, points]
dtype:... | python|pandas | 3 |
4,357 | 63,682,850 | Can't reference column by using predefined parameter as part of string | <p>I have a dataset where I would like to reference my column by using predefined parameter as a part of the string. The reason for this is that the columns I want to keep will change depending on the time of the year and the year.</p>
<p>My parameter are:</p>
<pre><code>year = '20'
</code></pre>
<p>This is working fin... | <p>I found the my mistake, I was trying to test the code in an instance before I did the required cleaning of the data.</p>
<p>When I replaced the actual code with my parameter it worked. I do however still not understand why I didn't get any KeyError when I was writing out the full string.</p>
<p>Thanks a lot everybod... | python|pandas | 0 |
4,358 | 63,506,954 | Why is this dictionary comprehension so slow? Please suggest way to speed it up | <p>Hi Please help me either: speed up this dictionary compression; offer a better way to do it or gain a higher understanding of why it is so slow internally (like for example is calculation slowing down as the dictionary grows in memory size). I'm sure there must be a quicker way without learning some C!</p>
<p><code>... | <p>Here is one approach:</p>
<pre><code>import pandas as pd
# create data frame
df = pd.DataFrame({'idx': [1, 2, 3, 4], 'col': ['1|2', '1|2|3', '2|3', '1|4']})
# split on '|' to convert string to list
df['col'] = df['col'].str.split('|')
# explode to get one row for each list element
df = df.explode('col')
# create... | python|pandas|list|list-comprehension|dictionary-comprehension | 3 |
4,359 | 21,769,162 | Running a groupby on a pivot table with Pandas | <p>I have a pivot table that looks like this:</p>
<pre><code>In [41]: counts
Out[41]:
SourceColumnID 3029903181 3029903182 3029903183 3029903184 ResponseCount
ColID QuestionID RowID
3029903193 316923119 3029903189 ... | <p>You'll want to groupby <code>'RowID'</code>. Since it's a level on the MultiIndex you pass <code>'RowID'</code> to the <code>level</code> keyword.</p>
<pre><code>In [5]: df.groupby(level='RowID').sum()
Out[5]:
3029903181 3029903182 3029903183 3029903184 ResponseCount
RowID ... | python|pandas|pivot-table | 2 |
4,360 | 24,922,315 | Merging pandas dataframe based on relationship in multiple columns | <p>Lets say you have a DataFrame of regions (start, end) coordinates and another DataFrame of positions which may or may not fall within a given region. For example:</p>
<pre><code>region = pd.DataFrame({'chromosome': [1, 1, 1, 1, 2, 2, 2, 2], 'start': [1000, 2000, 3000, 4000, 1000, 2000, 3000, 4000], 'end': [2000, 30... | <p>One solution would be to do an inner-join on <code>chromosome</code>, exclude the violating rows, and then do left-join with <code>position</code>:</p>
<pre><code>>>> df = pd.merge(position, region, on='chromosome', how='inner')
>>> idx = (df['BP'] < df['start']) | (df['end'] < df['BP']) # ... | python|pandas|merge | 5 |
4,361 | 17,595,912 | Gaussian Smoothing an image in python | <p>I am very new to programming in python, and im still trying to figure everything out, but I have a problem trying to gaussian smooth or convolve an image. This is probably an easy fix, but I've spent so much time trying to figure it out im starting to go crazy. I have a 3d .fits file of a group of galaxies and have ... | <p>Something like this perhaps?</p>
<pre><code>import numpy as np
import scipy.ndimage as ndimage
import matplotlib.pyplot as plt
img = ndimage.imread('galaxies.png')
plt.imshow(img, interpolation='nearest')
plt.show()
# Note the 0 sigma for the last axis, we don't wan't to blurr the color planes together!
img = ndim... | python|numpy|scipy|gaussian|smoothing | 30 |
4,362 | 20,112,760 | python pandas convert dataframe to dictionary with multiple values | <p>I have a dataframe with 2 columns Address and ID. I want to merge IDs with the same addresses in a dictionary</p>
<pre><code>import pandas as pd, numpy as np
df = pd.DataFrame({'Address' : ['12 A', '66 C', '10 B', '10 B', '12 A', '12 A'],
'ID' : ['Aa', 'Bb', 'Cc', 'Dd', 'Ee', 'Ff']})
AS=df.set_inde... | <p>I think you can use <code>groupby</code> and a dictionary comprehension here:</p>
<pre><code>>>> df
Address ID
0 12 A Aa
1 66 C Bb
2 10 B Cc
3 10 B Dd
4 12 A Ee
5 12 A Ff
>>> {k: list(v) for k,v in df.groupby("Address")["ID"]}
{'66 C': ['Bb'], '12 A': ['Aa', 'Ee', 'Ff'],... | python|dictionary|pandas | 19 |
4,363 | 15,889,998 | Pandas force matrix multiplication | <p>I would like to force matrix multiplication "orientation" using Python Pandas, both between DataFrames against DataFrames, Dataframes against Series and Series against Series.</p>
<p>As an example, I tried the following code:</p>
<pre><code>t = pandas.Series([1, 2])
print(t.T.dot(t))
</code></pre>
<p>Which output... | <p>Here:</p>
<pre><code>In [1]: import pandas
In [2]: t = pandas.Series([1, 2])
In [3]: np.outer(t, t)
Out[3]:
array([[1, 2],
[2, 4]])
</code></pre> | python|pandas|matrix-multiplication|dot-product|dataframe | 4 |
4,364 | 15,933,045 | Numpy max slow when applied to list of arrays | <p>I carry out some computations to obtain a list of numpy arrays. Subsequently, I would like to find the largest values along the first axis. My current implementation (see below) is very slow and I would like to find alternatives.</p>
<p><strong>Original</strong></p>
<pre><code>pending = [<list of items>]
mat... | <p>You can use <code>reduce(np.maximum, matrix)</code>, here is a test:</p>
<pre><code>import numpy as np
np.random.seed(0)
N, M = 1000, 1000
matrix = [np.random.rand(N) for _ in xrange(M)]
%timeit np.max(matrix, axis = 0)
%timeit np.max(np.vstack(matrix), axis = 0)
%timeit reduce(np.maximum, matrix)
</code></pre>
... | python|numpy | 5 |
4,365 | 15,944,171 | Python: Differences between lists and numpy array of objects | <p>What are the advantages and disadvantages of storing Python objects in a <code>numpy.array</code> with <code>dtype='o'</code> versus using <code>list</code> (or <code>list</code> of <code>list</code>, etc., in higher dimensions)?</p>
<p>Are numpy arrays more efficient in this case? (It seems that they cannot avoid... | <p>Slicing works differently with NumPy arrays. <a href="http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html" rel="noreferrer">The NumPy docs devote a lengthy page on the topic.</a> To highlight some points:</p>
<ul>
<li>NumPy slices can slice through multiple dimensions</li>
<li>All arrays generated by Nu... | python|numpy | 11 |
4,366 | 12,140,417 | pandas: normalizing a DataFrame | <p>I have input data in a flattened file. I want to normalize this data, by splitting it into tables. Can I do that neatly with <code>pandas</code> - that is, by reading the flattened data into a <code>DataFrame</code> instance, and then applying some functions to obtain the resulting <code>DataFrame</code> instances?<... | <pre><code>In [30]: df = pandas.read_csv('foo1.csv', sep='[\s]{2,}')
In [30]: df
Out[30]:
ItemId ClientId PriceQuoted ItemDescription
0 1 1 10 scroll of Sneak
1 1 2 12 scroll of Sneak
2 1 3 13 scroll of Sneak
3 2 ... | python|pandas|database-normalization | 12 |
4,367 | 72,009,695 | How do I check for null or string values in columns in the whole dataset in python? | <pre><code>def check_isnull(self):
df = pd.read_csv(self.table_name)
for j in df.values:
for k in j[0:]:
try:
k = float(k)
Flag=1
except ValueError:
Flag = 0
break
if Flag==1:
QMessageBox.information(self, "Information",
... | <p>try</p>
<pre><code>pd.to_numeric(df['estimated'],errors='coerce')
</code></pre>
<p>then use this to get rid of those rows and also rows with NANs</p>
<pre><code> df.dropna(subset='estimated')
</code></pre> | python|pandas|dataframe | 0 |
4,368 | 72,000,915 | Warning when fitting the LSTM model | <p>When I try to fit my model i get an error. Here is the code:</p>
<pre><code>model = Sequential()
model.add(LSTM(128, activation='relu', input_shape=(trainX.shape[1], trainX.shape[2]), return_sequences=True))
model.add(LSTM(64, activation='relu', return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(trainY... | <p>You can use the following code to suppress the Warnings.</p>
<pre><code>import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
</code></pre>
<p>In detail:-</p>
<pre><code>0 = all messages are logged (default behavior)
1 = INFO messages are not printed
2 = INFO and WARNING messages are not printed... | python|tensorflow|keras|lstm|warnings | 0 |
4,369 | 55,392,853 | Dataframe value not updating when iterating over rows | <p>I am fairly new to Pandas, and am trying to use it to analyse a large dataset. I have read everything I can find about it, but just can't get it to work. I am trying to update values in a dataframe whilst iterating over it row by row, but the values are not being updated in the dataframe.</p>
<pre><code>for index, ... | <p>Use <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.at.html" rel="nofollow noreferrer"><code>DataFrame.at</code></a>:</p>
<pre><code>df.at[index, j] = this_value
</code></pre>
<p>instead combination <code>loc</code> and <code>at</code>:</p>
<pre><code>df.loc[index].at[j] = this... | python|pandas | 0 |
4,370 | 55,377,044 | What does this 'single' value represent in gradient? | <p>I tried to calculate gradient of output layer w.r.t. input and i am expecting a matrix of gradient (as gradient of different nodes in output layer w.r.t. each input) but i am getting a single value. I want to know what this value represent here?</p>
<p>My aim was to calculate gradient of categorical-cross-entropy l... | <p><code>k.gradients</code> is a wrapper that actually runs <code>tf.gradients</code>.
As described in the document</p>
<blockquote>
<p>Constructs symbolic derivatives of <strong>sum</strong> of ys w.r.t. x in xs.</p>
</blockquote>
<p>The result of <code>tf.gradients</code> is the sum of all <code>ys</code> deriva... | python|tensorflow|input|keras|gradient | 1 |
4,371 | 55,463,251 | Why does multi-class classification fails with sigmoid? | <p>MNIST trained with Sigmoid fails while Softmax works fine</p>
<p>I am trying to investigate how different activation affects the final results, so I implemented a simple net for MNIST with PyTorch.</p>
<p>I am using NLLLoss (Negative log likelihood) as it implements Cross Entropy Loss when used with softmax.</p>
... | <p>Sigmoid + crossentropy can be used for multilabel classification (assume a picture with a dog and a cat, you want the model to return "dog and cat"). It works when the classes aren't mutually exclusive or the samples contain more than one object that you want to recognize.</p>
<p>In your case MNIST has mutually exc... | deep-learning|pytorch|loss-function|multiclass-classification|activation-function | 0 |
4,372 | 7,656,665 | How to repeat elements of an array along two axes? | <p>I want to repeat elements of an array along axis 0 and axis 1 for M and N times respectively:</p>
<pre><code>import numpy as np
a = np.arange(12).reshape(3, 4)
b = a.repeat(2, 0).repeat(2, 1)
print(b)
[[ 0 0 1 1 2 2 3 3]
[ 0 0 1 1 2 2 3 3]
[ 4 4 5 5 6 6 7 7]
[ 4 4 5 5 6 6 7 7]
[ 8 ... | <p>You could use the <a href="https://en.wikipedia.org/wiki/Kronecker_product" rel="nofollow noreferrer">Kronecker product</a>, see <a href="https://numpy.org/doc/stable/reference/generated/numpy.kron.html" rel="nofollow noreferrer"><code>numpy.kron</code></a>:</p>
<pre><code>>>> a = np.arange(12).reshape(3,4)... | python|numpy | 16 |
4,373 | 56,624,946 | Calculating Quantiles based on a column value? | <p>I am trying to figure out a way in which I can calculate quantiles in pandas or python based on a column value? Also can I calculate multiple different quantiles in one output?</p>
<p>For example I want to calculate the 0.25, 0.50 and 0.9 quantiles for </p>
<p><strong>Column Minutes in df where it is <= 5 and w... | <p><code>DataFrame.quantile</code> accepts values in array,</p>
<p>Try</p>
<pre><code>df['minute'].quantile([0.25, 0.50 , 0.9])
</code></pre>
<p>Or filter the data first,</p>
<pre><code>df.loc[df['minute'] <= 5, 'minute'].quantile([0.25, 0.50 , 0.9])
</code></pre> | python|python-3.x|pandas|data-science | 3 |
4,374 | 56,590,300 | Simultaneously change occurrences in a numpy array | <p>I have a numpy array that looks something like this:</p>
<pre><code>h = array([string1 1
string2 1
string3 1
string4 3
string5 4
string6 2
string7 2
string8 4
string9 3
string0 2 ])
</code></pre>
<p>In the second column, I would like to change all occu... | <p>You can use a look up table and advanced indexing:</p>
<pre><code>A = np.rec.fromarrays([np.array("The quick brown fox jumps over the lazy dog .".split()), np.array([1,1,1,3,4,2,2,4,3,2])])
A
# rec.array([('The', 1), ('quick', 1), ('brown', 1), ('fox', 3),
# ('jumps', 4), ('over', 2), ('the', 2), ('lazy'... | python|numpy | 2 |
4,375 | 56,672,105 | Transpose column in a DataFrame into a binary matrix | <p><strong><em>Context</em></strong></p>
<p>Lets say I have a pandas-DataFrame like this:</p>
<pre class="lang-py prettyprint-override"><code>>>> data.head()
values atTime
date
2006-07-01 00:00:00+02:00 15.10 0000
2006-07-01 00:15:00+02:00 16.10 0015
2006-07-01 00... | <p>This is a use case for <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.get_dummies.html" rel="nofollow noreferrer"><code>get_dummies</code></a>:</p>
<pre><code>pd.get_dummies(df, columns=["atTime"])
</code></pre>
<pre><code> values atTime_0 atTime_15... | python|pandas|dataframe | 8 |
4,376 | 56,460,344 | How to iterate over the n-th dimenstion of a numpy array? | <p>I use to concatenate my numpy arrays of arbitrary shape to make my code cleaner, however, it seems pretty hard to me to iterate over it in a pythonesque way.</p>
<p>Lets consider a 4 dimension array x (thus <code>len(x.shape) = 4</code>), and that the index I want to iterate is 2, the naive solution that I usually ... | <p>One possible solution is to use a dict().</p>
<p>What you can do is:</p>
<pre><code>x = dict()
x['param1'] = [1, 1, 1, 1]
x['param2'] = [2, 2, 2, 2]
print(x['param1'])
# > [1, 1, 1, 1]
</code></pre> | python|numpy | 0 |
4,377 | 56,575,877 | shuffling two tensors in the same order | <p>As above. I tried those to no avail:</p>
<pre><code>tf.random.shuffle( (a,b) )
tf.random.shuffle( zip(a,b) )
</code></pre>
<p>I used to concatenate them and do the shuffling, then unconcatenate / unpack. But now I'm in a situation where (a) is 4D rank tensor while (b) is 1D, so, no way to concatenate.</p>
<p>I al... | <p>You could just shuffle the indices and then use <code>tf.gather()</code> to extract values corresponding to those shuffled indices:</p>
<p><strong>TF2.x (UPDATE)</strong></p>
<pre class="lang-py prettyprint-override"><code>import tensorflow as tf
import numpy as np
x = tf.convert_to_tensor(np.arange(5))
y = tf.conv... | tensorflow|tensorflow2.0|tensorflow2.x | 17 |
4,378 | 56,751,703 | Why .argmax returns 1 instead of the maximum? | <p>I'm looking for the max value of two arrays, and I tried to get the max of each and add to another 'np.array'. However, I got 1.</p>
<pre><code>maximums = [x_train.argmax(), x_test.argmax()]
print(maximums)
maximums = np.array(maximums)
print(maximums)
maximum = maximums.argmax()
print(maximum)
</code></pre>
<p>I ... | <p><code>np.argmax</code> returns the index for the maximum value. In this case, the index for maximum value <code>577</code> is 1. Offcial docs reference: <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmax.html" rel="nofollow noreferrer">https://docs.scipy.org/doc/numpy/reference/generated/nump... | python|numpy | 1 |
4,379 | 67,140,409 | python pandas apply function in groupby, and add results as column in data frame | <p>IM practicing with sample data to learn pandas. I have sample data like the following:</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th style="text-align: center;">symbol</th>
<th style="text-align: center;">date_time</th>
<th style="text-align: center;">close</th>
<th style="text-align:... | <p>It was index related.</p>
<p>setting the series index before passing it back works:</p>
<pre><code>quote_data['rsi'] = quote_data.groupby("sym")["close"].apply(calc_rsi)
def calc_rsi(series):
rsi_arr=np.array(series)
RSI = talib.RSI(rsi_arr, timeperiod=14)
rsi_series=pd.Series(RSI,se... | python|pandas|dataframe | 0 |
4,380 | 47,356,322 | Pandas: fill column based on data in different column | <p>I Have the following Dataframe:</p>
<pre><code>test_df['A'] = [100, 100, 100, 0, 0, 100, 100, 0, 100, 100, 100]
test_df['B'] = [100, 0, 0, 0, 0, 0, 0, 0, 100, 0, 0]
</code></pre>
<p>What I want to achieve is a new column C where if I iterate on column B and I find a 100 value then I want to forward fill until the ... | <p>You can use the apply method. </p>
<pre><code>def my_func(input):
#do whatever
test_df['B'] = test_df['A'].apply(my_func)
</code></pre>
<p>You obviously have to fill it in with your own code...</p> | python|pandas | -2 |
4,381 | 68,125,847 | '>' not supported between instances of 'str' and 'int' pandas function for getting threshold | <p>I have a df</p>
<pre><code>import pandas as pd
df= pd.DataFrame({'ID': [1,2,3],
'Text':['This num dogs and cats is (111)888-8780 and other',
'dont block cow 23 here',
'cat two num: dog and cows here'],
... | <p>Since you're trying to change only the <code>Match</code> column, you might as well only pass that column to <code>apply</code>:</p>
<pre><code>df.Match.apply(threshold)
</code></pre>
<p>where we don't use <code>axis</code> argument anymore since it is a Series we are applying over and it has only one axis anyway.</... | python|pandas|list|function|tuples | 3 |
4,382 | 68,133,846 | ERROR: Could not install packages due to an OSError: [WinError 5] | <p>i was trying to install tensorflow-gpu on my pycharm (<code>pip install tensorflow-gpu</code>), but unfortunately im getting a Error Message. How can i install this package on my pycharm? What is wrong here? Should i install it directly with cmd? How can I install them with pycharm? However, I was able to install th... | <p>You need to run the command prompt or terminal as an administrator. This will permit you to install packages. And also, you need to upgrade pip to the latest version - <code>python -m pip install –-upgrade pip</code> in cmd or terminal.</p> | python|tensorflow|pip|pycharm | 14 |
4,383 | 68,090,432 | How to map the integer values in a column in a pandas datfarme to random n-digit numbers? | <p>I have a pandas data frame like df:</p>
<pre><code>df=pd.DataFrame([[111, 7,8], [409,6,4], [333, 9,0],[111,3,2],[111,0,0], [409,7,0]], columns=['A','B','C'])
df
A B C
0 111 7 8
1 409 6 4
2 333 9 0
3 111 3 2
4 111 0 0
5 409 7 0
</code></pre>
<p>How to map column A to 10-digit random integers ... | <p>One way via <code>hashlib</code>:</p>
<pre><code>import hashlib
df['A'] = df['A'].apply(lambda s: int(hashlib.sha1(str(s).encode("utf-8")).hexdigest(), 16) % (10 ** 8))
</code></pre>
<h4>OUTPUT:</h4>
<pre><code> A B C
0 22445762 7 8
1 63857454 6 4
2 61248669 9 0
3 22445762 3 2
4 224... | python|pandas|dataframe | 4 |
4,384 | 68,377,243 | Pandas transform rows to columns | <p>I have a pandas dataframe that looks like this:</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th style="text-align: left;">index</th>
<th>p1</th>
<th>a1</th>
<th>phase</th>
<th>file_number</th>
<th style="text-align: right;">e1</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: left;... | <pre class="lang-py prettyprint-override"><code>d = {'p1': {388: 19.288,389: 40.91,390: 31.31,391: 28.42,392: 17.94,393: 38.02,394: 31.23,395: 26.902},
'a1': {388: 21.63,389: 71.489,390: 43.952,391: 30.25,392: 22.0,393: 68.75,394: 48.352,395: 29.88},
'phase': {388: 0.0,389: 1.0,390: 2.0,391: 3.0,392: 0.0,393:... | python|pandas|dataframe | 1 |
4,385 | 68,117,724 | Tensorflow-gpu not detecting GPU | <p>I have reinstalled tensorflow-gpu many times.Cuda and Cudnn already installed with path added but available gpu for tensorflow is always 0 for me.</p> | <p>I reinstalled tensorflow-gpu again but with following command.</p>
<pre><code>conda install tensorflow-gpu=2.3 tensorflow=2.3=mkl_py38h1fcfbd6_0
</code></pre>
<p><a href="https://github.com/ContinuumIO/anaconda-issues/issues/12194" rel="nofollow noreferrer">For more info</a></p> | python|tensorflow | 0 |
4,386 | 59,166,618 | getting a histogram of a JaggedArray | <p>Hi I have a ROOT TTree with a bit of a complicated structure. When I use uproot to create an array: </p>
<pre><code>analysis = uproot.open("/b/LJ_data/02Oct2019/FRVZ/FRVZprompt2zd_mH125_mzd01.root")["analysis"]
el_Eratio = analysis.arrays(["el_Eratio"], cache=mycache);
print(el_Eratio)
</code></pre>
<p>I get a Jag... | <p>Because you said <code>analysis.arrays</code> (plural), you got back a Python dict. The only array it contains (because you asked for only one, <code>["el_Eratio"]</code>) has one key: <code>b"el_Eratio"</code>. Note that this is a bytestring (starts with <code>b</code>). If you know the encoding, such as <code>"utf... | python|numpy|matplotlib|uproot | 0 |
4,387 | 59,171,235 | Pandas data not being plotted | <p>I have the following simple code to plot specific data sets from this file <a href="https://easyupload.io/mdci9u" rel="nofollow noreferrer">https://easyupload.io/mdci9u</a></p>
<pre><code>import pandas as pd
import matplotlib.pyplot as plt
url=r'/path_to_file/Book.xlsx'
df1 = pd.read_excel(url, sheet_name=0,sep=... | <p>You made a mistake in the <strong>last line</strong>. It should be </p>
<blockquote>
<p>axs.plot(x,y)</p>
</blockquote>
<p>Instead, you have used an equal sign which is why you got an empty graph.</p> | python|pandas|matplotlib | 2 |
4,388 | 59,314,762 | Inconsistent matrices in SymPy | <p>I'm calculating reduced echelon forms in SymPy. I'm trying to get the pivot columns of the following matrix:</p>
<pre><code>exercise4 = Matrix([[1,3,5,7],[3,5,7,9],[5,7,9,1]])
</code></pre>
<p>I examine the matrix with the following:</p>
<pre><code>exercise4.rref()[0]
Matrix([
[1, 0, -1, 0],
[0, 1, 2, 0],
[0, 0... | <p>Doing your pivot on [2,3] produces the <code>rref</code> matrix:</p>
<pre><code>In [269]: M
Out[269]:
array([[ 1., 0., -1., -2.],
[ -0., 1., 2., 3.],
[ 0., 0., 0., -10.]])
In [271]: M[0]-=M[2]*(-2/-10) ... | python|numpy|matrix|linear-algebra|sympy | 0 |
4,389 | 45,180,564 | Unit Testing Pandas DataFrame | <p>I'm looking to develop a unit test where it compares two DataFrames and returns True if their lengths are the same and if not returns the difference in length as well as what the missing output rows are.</p>
<p>For instance:
Example 1:</p>
<pre><code>df1 = {0,1,2,3,4}
df2 = {0,1,2,3,4}
</code></pre>
<blockquote>
... | <p>I think first you must decide on what you want: either an unit test or a function that returns the difference between two data frames.</p>
<p>If the former case, you could use <code>pd.util.testing.assert_frame_equal</code>:</p>
<pre><code>first = pd.DataFrame(np.arange(16).reshape((4,4)), columns=['A', 'B', 'C', ... | python|unit-testing|pandas|dataframe|diff | 3 |
4,390 | 45,122,044 | Plot partial stacked bar chart in pandas | <p>I have a DataFarme df in the following form. I want to plot a graph with 4-pair bars. It means for each of the four days, there are two bars representing 0 and 1. For both of the 2 bars, I want to have a stacked bar.So each bar have 3 colors, representing <30, 30-70 and >70. I tried to use the "stacked = True" bu... | <p>You can use <code>bottom</code> parameter.
Here is the way to go</p>
<pre><code>>> import matplotlib.pyplot as plt
>> import numpy as np
>> import pandas as pd
>>
>> columns = pd.MultiIndex.from_tuples([(r, b) for r in ['<30', '30-70', '>70']
>> ... | python|pandas | 3 |
4,391 | 45,225,814 | Pandas: Best way to filter bottom 10% and top 25% of data within a groupby using quantile | <p>I have a time series in pandas with prices and times. I would like to group the dates by 1 month time intervals, calculate the 10-75% quantile of prices for each month and then filter the original dataframe using these values (so that only the prices that fall between 10% and 75% are left).</p>
<p>The dataframe loo... | <p>First, define a function to check whether a Series is between the specified quantiles:</p>
<pre><code>def in_qrange(ser, q):
return ser.between(*ser.quantile(q=q))
</code></pre>
<p>This returns a boolean array. If you pass this to resample.transform, you will have:</p>
<pre><code>df.resample('1M')['price'].tr... | python|pandas|numpy | 3 |
4,392 | 57,084,473 | Return rows with max/min values at bottom of dataframe (python/pandas) | <p>I want to write a function that can look at a dataframe, find the max or min value in a specified column, then return the entire datafrane with the row(s) containing the max or min value at the bottom.</p>
<p>I have made it so that the rows with the max or min value alone get returned.</p>
<pre><code>def findAggre... | <h3>Focus on the index values</h3>
<p>And use one <code>loc</code></p>
<pre><code>i = df.col2.idxmin()
df.loc[[*df.index] + [i]]
col1 col2 col3
0 blue 2 dog
1 orange 18 cat
2 black 6 fish
0 blue 2 dog
</code></pre>
<hr>
<p>Same idea but with Numpy and <code>iloc</code></p>
<p... | python|pandas|csv|max|min | 6 |
4,393 | 57,165,842 | Extract info from original dataframe after doing some analysis on it | <p>So I had a dataframe and I had to do some cleansing to minimize the duplicates. In order to do that I created a dataframe that had instead of 40 only 8 of the original columns. Now I have two columns I need for further analysis from the original dataframe but they would mess with the desired outcome if I used them i... | <p>You can merge the new "clean" dataframe with the other two variables by using the indexes. Let me use a pratical example. Suppose the "initial" dataframe, called "df", is:</p>
<pre><code>df
name year reports location
0 Jason 2012 4 Cochice
1 Molly 2012 24 Pima
2 Tina 2013 ... | python|pandas | 1 |
4,394 | 56,944,018 | One-hot encoding in pytorch/torchtext | <p>I have a <code>Bucketiterator</code> from <code>torchtext</code> that I feed to a model in <code>pytorch</code>. An example of how the iterator is constructed:</p>
<pre><code>train_iter, val_iter = BucketIterator.splits((train,val),
batch_size=batch_size,
... | <p>def get_one_hot_torch_tensor(in_tensor):
"""
Function converts a 1d or 2d torch tensor to one-hot encoded
"""</p>
<pre><code>n_channels = torch.max(in_tensor)+1 # maximum number of channels
if in_tensor.ndim == 2:
out_one_hot = torch.zeros((n_channels, in_tensor.shape[0], in_tensor... | python|machine-learning|deep-learning|pytorch|torchtext | 0 |
4,395 | 46,003,577 | pandas: how to save to hdf dataframe with string columns containing np.nan | <p>I am wondering if there is a good way to save a pandas dataframe to hdf when it contains string columns.</p>
<p>Given the dataframe :</p>
<pre><code>In [6]: df.head() ... | <p>Columns in an HDF file must be of a single dtype. <code>nan</code> is represented by a <code>float</code> internally to numpy. You could replace the <code>nan</code> values with empty strings via:</p>
<pre><code>df['src'].fillna('')
</code></pre>
<p>HDF performs much better on numeric types than strings, so it may... | python|pandas|hdf5 | 3 |
4,396 | 45,819,258 | numpy inverse matrix not working for full rank matrix - hessian in logistic regression using newtons-method | <p>I am trying to compute the inverse of a full-rank matrix using numpy, but when I test the dot product, I find that it does not result in the identity matrix - which means it did not invert properly. </p>
<p>My code:</p>
<pre><code>H = calculateLogisticHessian(theta, X) #returns a 5x5 matrix
Hinv = np.linalg.inv(H... | <p>Hessian matrix are often <a href="https://en.wikipedia.org/wiki/Condition_number" rel="nofollow noreferrer">ill-conditioned</a>. <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.cond.html" rel="nofollow noreferrer"><code>numpy.linalg.cond</code></a>
lets you compute the <em>condition numbe... | numpy|logistic-regression|matrix-inverse|newtons-method|hessian | 2 |
4,397 | 46,078,287 | Plotting lines and bars from pandas dataframe on same graph using matplotlib | <p>I want to plot temperature data as a line, with rainfall data as a bar. I can do this easily in <a href="https://ibb.co/hWy2Bv" rel="nofollow noreferrer">Excel</a> but I'd prefer a fancy python graph to show it in a nicer way. </p>
<p>Some sample code to illustrate the problem:</p>
<pre><code>import pandas as pd
i... | <p>A simple way would be to use the <a href="http://pandas.pydata.org/pandas-docs/stable/visualization.html#suppressing-tick-resolution-adjustment" rel="nofollow noreferrer"><code>x_compat</code></a> property:</p>
<pre><code>ax = df.plot(x=index, y="A", x_compat=True) # plot lines first
df.plot(x=index, y="B", kind="... | python|pandas|matplotlib | 2 |
4,398 | 23,190,799 | Capturing Datetime Objects in Pandas Dataframe | <p>If I'm reading the docs correctly for Pandas 0.13.1, read_csv should yield columns of datetimes when <code>parse_dates = [<col1>,<col2>...]</code> is invoked during the read. What I'm getting instead is columns of Timestamp objects. Even with the application of .to_datetime, I still end up with Timestamp... | <p>Timestamps are the way that pandas deals with datetime, you can <a href="https://stackoverflow.com/questions/13703720/converting-between-datetime-timestamp-and-datetime64">move between Timestamp, datetime64 and datetime</a>, but <strong>most of the time using Timestamp is what you want</strong> (and pandas just conv... | python|datetime|pandas | 3 |
4,399 | 23,298,751 | Filtering on data with a cast string->float type | <p>a few issues here but i think the code is relatively straight forward.</p>
<p>the code is as follows:</p>
<pre><code> import pandas as pd
def establishAdjustmentFactor(df):
df['adjFactor']=df['Adj Close']/df['Close'];
df['chgFactor']=df['adjFactor']/df['adjFactor'].shift(1);... | <p>So it appears as if I have found the issue after poking around a little bit, and changing the way I was thinking about the problem.</p>
<p>Any input on if there is a more efficient way to do this would be great.</p>
<pre><code> def operateOverSetToCreateEasyKey(df):
for i in df.index:
df.ix[... | python|pandas | 0 |
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