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 |
|---|---|---|---|---|---|---|
357,800 | 50,157,416 | BGR8 raw image conversion to numpy python | <p>So far i have tested acquiring images from a camera in the raw BGR8 format into a numpy array, I am at the point in which i can get access to the data however the image seems to have visible image artifacts (vertical lines etc) and only displays in greyscale.</p>
<p>The following code is used to acquire the image i... | <p>Working example is:</p>
<pre><code>image=ctrl.GetImageWindow(0,0, w,h)
data = numpy.array(image, dtype=numpy.uint8).reshape(768,-1,3)
</code></pre> | python|numpy|opencv|python-imaging-library | 0 |
357,801 | 50,070,789 | Keyerror when trying to testsuite pytest with pandas | <p>Hi there all machine learning and python experts. I seek your help.</p>
<p>I have recently seen into google ml crash course. They recommended to get familiar with pandas before starting the crash course.</p>
<p>So, I have installed pip v8.1.1 using python v3.5.2 and pandas from pip. And also installed pytest 3.5.1... | <p>To run tests in pandas you need to install a lot of <a href="https://github.com/pandas-dev/pandas/blob/master/ci/requirements_dev.txt" rel="nofollow noreferrer">development requirements</a>:</p>
<pre><code>pip install -U [--user] ci/requirements_dev.txt
</code></pre> | python|python-3.x|pandas|machine-learning|pytest | 1 |
357,802 | 49,809,354 | Searching sub string through 3Million Records in python | <p>I have a huge Data frame which has 3M records which has column called description. Also I have possible sub string set of around 5k.</p>
<p>I want to get the rows in which the description contains any of the sub string.</p>
<p>i used the following looping</p>
<pre><code>for i in range(0,len(searchstring)):
ss=s... | <p>Should be faster if you use pandas <code>isin()</code> function</p>
<p>Example:</p>
<pre><code>import pandas as pd
a ='Hello world'
ss = a.split(" ")
df = pd.DataFrame({'col1': ['Hello', 'asd', 'asdasd', 'world']})
df.loc[df['col1'].isin(ss)].index
</code></pre>
<p>Returns a list of indexes:</p>
<pre><code>Int6... | python|pandas | 0 |
357,803 | 50,216,844 | Convert XML to DataFrames | <p>I'm trying to read the tables from a [web page][1] into pandas DataFrames. <code>pandas.read_html</code> returns a list of empty tables because the tables from the HTML are indeed empty. They're probably populated dynamically.</p>
<p>Someone <a href="https://stackoverflow.com/questions/50216844/python-convert-xml-t... | <p>Anytime one works with complex XML and needs simpler structures like flattened dataframes with two-dimensional row by column, one should consider <a href="https://stackoverflow.com/tags/xslt/info">XSLT</a>, the special purpose language designed to transform XML files into other XML, HTML, and as shown below even tex... | python|xml|pandas|xslt | 2 |
357,804 | 63,891,902 | How to do update rows in SQLite table using SQLite3 and Python | <p>I am new to python and I don't really understand the sql thing that well. Currently on the 6th week of team treehouse so please bare with me here if these are noob questions.</p>
<p><strong>Goal</strong></p>
<ol>
<li>Import CSV with stock_tickers and 5 other columns of data</li>
<li>Convert CSV into pandas dataframe... | <p>This is the <code>ON CONFLICT</code> clause of your query:</p>
<pre><code>ON CONFLICT (stock_ticker) DO UPDATE
SET (stock_status)
</code></pre>
<p>This is not valid SQLite syntax. If you want to update <code>stock_status</code> when another row already exists with the same <code>stock_ticker</code>, you can use ... | python|pandas|sqlite|sql-update|sql-insert | 2 |
357,805 | 63,938,847 | Python Django: Merge Dataframe performing Sum on overlapping columns | <p>I want to merge two DataFrames with exact the same column names. The overlapping columns can be added togheter. I'm having a bit of troubles because the grouping should be happening on the "index" called "Date" but I can't this index through using the 'Date' name.</p>
<p>Actually, I just need the... | <p>Let say you have 2 df like this:</p>
<pre><code>df1 = pd.DataFrame({'Adj Close':[1, 2]}, index=['2019-09-19','2019-09-20'])
df2 = pd.DataFrame({'Adj Close':[3, 4, 5]}, index=['2019-09-19','2019-09-20','2019-09-21'])
</code></pre>
<p>df1</p>
<pre><code> Adj Close
2019-09-19 1
2019-09-20 2... | python|django|pandas|dataframe|merge | 1 |
357,806 | 63,821,407 | Pandas selecting dataframe columns using a specific string and array/list | <p>I have a dataframe with hundreds of columns (stocks). My issue is that I need to always pull a specific column (date) followed by an array/list of others (dynamic).</p>
<p>Previously I was doing something like this:</p>
<pre><code>df = stocks[['date', 'AAPL', 'AMZN']]
</code></pre>
<p>but now if I need to dynamicall... | <p>Let us try</p>
<pre><code>df = stocks[['date'] + rowData['symbol'].iloc[0]]
</code></pre> | pandas | 0 |
357,807 | 63,885,376 | How to aggregate some of the levels in a deep nested `groupby` in pandas? | <p>I am doing the exercise on <a href="https://repl.it/@freeCodeCamp/fcc-medical-data-visualizer" rel="nofollow noreferrer">https://repl.it/@freeCodeCamp/fcc-medical-data-visualizer</a>, and the groupby problem stuck me:</p>
<p>Now I get a tree like nested-level <code>groupby</code>, I want to get the total count of so... | <p>Check <code>sum</code> and know your <code>level</code></p>
<pre><code>df = df.sum(level = [0, 6])
</code></pre> | pandas | 1 |
357,808 | 63,831,839 | What is the most efficient way to populate one pandas dataframe using another dataframe? | <p>I am wondering how I can most efficiently do the following operation so that I can also upscale it to dataframes with million rows+.
I have 2 panda dataframes:</p>
<p>Data1:</p>
<pre><code>Position Letter
1 a
2 b
3 c
4 b
5 a
</code></pre>
<p>Data2:</p>
<pre><code>... | <p>Best way is to use merge:</p>
<pre><code>df = df1.merge(df2, on=['Letter'])
print(df)
Position Letter Weight
0 1 a 1
1 5 a 1
2 2 b 2
3 4 b 2
4 3 c 3
</code></pre> | python|pandas|dataframe|apply | 1 |
357,809 | 64,144,121 | Pandas reshaping and stacking dataframe | <p>I have an excel sheet in this format:</p>
<pre><code>Source Hour Min1 Min2 Min3
online 0 0 0 0
online 1 1 2 0
online 2 3 4 5
</code></pre>
<p>How do I use pandas to transform it to this format?</p>
<pre><code>Hour 0 1 2
Min1 Min2 Min3 ... | <p>Just do <code>T</code>, notice I will recommend keep the <code>Source</code> as first level in the column</p>
<pre><code>out = stacked.to_frame(0).T
</code></pre> | python|pandas | 2 |
357,810 | 63,990,833 | Error when I try to calculate the normalization on Jupyter notebook (with '-' and df) | <p>I'm just discovering pandas, I tried to google the error but I found nothing.
When I try to calculate this: <code>X = (X - X.min()) / (X.max() - X.min()) </code>knowing that (<code>X = titanic[['sex','age','fare','class','embark_town','alone']].copy())</code>
I get this : <a href="https://i.stack.imgur.com/QQ2FY.png... | <p>You should normalize numerical features only (like fare in the Titanic dataset).</p> | python|pandas|machine-learning | 0 |
357,811 | 63,995,370 | Doing a for loop on bunch of tuples? | <p>I have 1002 tuples in which there are an index and an 11 columns data. I would like to write a for loop to make each of those columns one numpy array based on changing the column "T". In the sense that each of theses data frames becomes stored in a separated data frame.</p>
<pre><code>index "T&q... | <p>Possibly it's because you calling "T", that is a float number, you should try:</p>
<pre><code>for index in df_train_list:
df_train_array = np.array(df_train_list["T"][1])
</code></pre> | python|numpy | 0 |
357,812 | 63,866,156 | tf__norm() takes 1 positional argument but 2 were given | <p>I a trying to pass a function to my tf dataset to normalize the non numerical data in my data frame, however I keep getting this error:</p>
<p>TypeError: in user code:
TypeError: tf__norm() takes 1 positional argument but 2 were given</p>
<pre><code>def norm(dataframe):
for header in dataframe._get_numeric_data(... | <p>Follow your code, the type of <em>ds</em> is BatchDataSet , Its element_spec is tuple which size is 2, but your norm function only needs one parameter, That's why it raise</p>
<p><code>takes 1 positional argument but 2 were given</code></p>
<p>More detail, the type of first elements is dict, the other is TensorSpec.... | tensorflow|keras | 0 |
357,813 | 63,822,730 | Why do we need the custom dataset class and use of _getitem_ method in NLP, BERT fine tuning etc | <p>I am a newbie in NLP and has been studying the usage of BERT for NLP tasks. In many notebooks, I See that a custom dataset class is defined and <em>getitem</em> method is defined (along with len).</p>
<p>Tweetdataset class in this notebook - <a href="https://www.kaggle.com/abhishek/roberta-inference-5-folds" rel="no... | <p>It is a recommended abstraction in pytorch to define <code>datasets</code> by inheriting <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset" rel="nofollow noreferrer"><code>torch.utils.data.Dataset</code></a>. Those objects define how many elements are there (<code>__len__</code> method) and... | nlp|pytorch|bert-language-model | 1 |
357,814 | 63,859,568 | get rows by time regardless of date in pandas | <p>I have data as follows:</p>
<pre><code>Col1,ColDate
a,2020-09-11 08:43:00
b,2020-09-12 09:43:00
c,13-09-2020 09:43:00
d,09/16/2020 10:43:00
e,09/19/2020 12:43:00
f,09/12/2020 15:43:00
</code></pre>
<p>Intention is to get all rows between 0000 and 0900 hours, regardless of the date and its format. In pandas</p>
<p>I ... | <p>Use <code>pandas.DataFrame.between_time</code>:</p>
<pre><code>df["ColDate"] = pd.to_datetime(df["ColDate"]) # If not in datetime already
new_df = df.set_index("ColDate").between_time("00:00:00", "09:00:00")
print(new_df.reset_index())
</code></pre>
<p>Or other way a... | python|python-3.x|pandas | 2 |
357,815 | 63,767,829 | What am I doing wrong in calculating quartiles? | <pre><code>x = np.array([1, 3, 7, 11])
print(np.quantile(x, 0.75))
print(np.quantile(x, 0.25))
</code></pre>
<pre><code>8.0
2.5
</code></pre>
<p>How am I getting these as answers? What am I doing wrong? Am I being really dumb or is q1 and q3 9 and 2?</p> | <p>What you're doing wrong is not reading the <a href="https://numpy.org/doc/stable/reference/generated/numpy.quantile.html" rel="nofollow noreferrer">documentation</a>. The default interpolation is <code>linear</code>; you seem to expect <code>midpoint</code>.</p>
<pre><code>x = np.array([1, 3, 7, 11])
print(np.quant... | python|numpy | 3 |
357,816 | 64,131,836 | Is there anyway to get efficientnet pre-trained weight in hdf5 format? | <p>I have some problems with getting pre-trained weights in efficientnet. So I googled for efficientnet.hdf5 but cannot find it. So is there anyway to get pre-trained weight in hdf5 format. Thank u.</p> | <pre><code>from tensorflow.keras.applications.efficientnet import EfficientNetB0, EfficientNetB5
model = EfficientNetB0(include_top=True, weights="imagenet", input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation="softmax")
model.summary()
# Keras H5 format(older me... | tensorflow|keras|conv-neural-network|efficientnet | 1 |
357,817 | 64,055,945 | Calculation new column in pandas dataframe from row by row calculation | <p>I am learning python and have come up with a way to calculate values row by row, but I am sure there is a more elegant (and quicker) solution. Here is simple example:</p>
<pre><code>df = pd.DataFrame(np.random.rand(10,3), columns=list('abc'))
df.head()
a b c
0 0.207455 0.257266 0.453369
1 0.518193... | <p>Yes we have <code>shift</code> with <code>diff</code> and no for loop</p>
<pre><code>df['d'] = ((df['a'] - df['b']) ** 2 + (df['a'].shift() - df['b'].shift()) ** 2)**0.5
df['e'] = (df['c'].diff()) * 1609
df
a b c d e
0 0.207455 0.257266 0.453369 NaN NaN
... | python|pandas|dataframe | 0 |
357,818 | 64,145,932 | Filtering Pandas DataFrame by value in a column's lists | <p>I have a DataFrame that has a column of lists. I would like to return a subset of the Dataframe of those rows whose list contains a specified value.</p>
<pre><code>test = pd.DataFrame({'detail_id': [10000, 10001, 10002],
'tokens': [['A', 'B', 'C'], ['A', 'D'], ['C', 'E', 'F', 'H']]})
</code></p... | <p>A good night's sleep and a re-reading of the <a href="https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html" rel="nofollow noreferrer">selection documentation</a> has helped me figure it out. The documentation says that selection can accept a "boolean array". So that's what I've done.</p>
<... | pandas | 0 |
357,819 | 63,923,672 | Integrate sum of functions vs sum of integrated functions with scipy solve_ivp | <p>So I was wondering: Shouldn’t the sum of the integrals of some functions be equivalent to the integral of the sum of the functions?</p>
<p>Here I integrate three arbitrary functions with the help of <code>scipy</code>’s <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.solve_ivp.html" rel... | <ol>
<li>The initial values are inconsistent. You have to sum initial values too.</li>
<li>If you sum solutions of</li>
</ol>
<pre><code>dy/dt = -y
dy/dt = y
y(0)=1
</code></pre>
<p>you do not get the solution of</p>
<pre><code>dy/dt = 0
</code></pre>
<p>you get</p>
<pre><code>cos(t) + y^t
</code></pre>
<p>But</p>
<... | python|numpy|math|scipy|integral | 0 |
357,820 | 63,875,039 | Tensorflow Probability Sampling Take Long Time | <p>I am trying to use tfp for sampling process. draw samples from beta distribution and feed the result as probability input to draw sample from Binominal distribution. It took forever to run.</p>
<p>Am I supposed to run it this way or is there an optimal way?</p>
<p>'''</p>
<pre><code>import tensorflow_probability as ... | <p>TFP Distributions support a concept we call "batch shape". Here, by giving <code>probs=phi</code> with <code>phi.shape = [100000]</code>, you are effectively creating a "batch" of 100k Binomials. Then you're sampling 100k times from those, which is trying to create 1e10 samples, which is gonna ta... | tensorflow|sampling|montecarlo|tensorflow-probability | 0 |
357,821 | 63,801,646 | Drop rows if specific word is not present in a column where column have links and word need to be compare require splitting python | <p>here I am trying to analyze and practicing pandas.dataframe functions. Now I am trying to drop all rows which have not a specific word in the given column of links. <a href="https://i.stack.imgur.com/hGxwg.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/hGxwg.png" alt="enter image description here... | <p>This is a much nicer solution - will only leave rows contain 'Microsoft':</p>
<pre><code>df = df[df['post_link'].str.contains('Microsoft')]
</code></pre>
<p>Following your request for multiple search terms, here you go:</p>
<pre><code>searchfor = ['MicrosoftLife', 'MicrosoftTeam']
df = df[df['post_link'].str.contain... | python|pandas|dataframe | 0 |
357,822 | 64,011,749 | Tensorflow: 'Error: No gradients provided for any variable' with custom loss | <p>I'm getting an error when I try to run my code</p>
<pre><code>ValueError: No gradients provided for any variable
</code></pre>
<p>Here's what my code looks like</p>
<pre><code>optimizer = tf.keras.optimizers.Adam(learning_rate=1e-2)
while True:
#...other stuff
if(isTimeToBackprop()):
vStates = mod... | <p>Supposing you are working with Tensorflow 2.x, If you are trying to create a custom training step, to track the model gradients, you have to invoke the model under the <code>tf.GradientTape</code> context manager.</p>
<p>Here you have your code updated to correctly work with the <code>GradientTape</code>:</p>
<pre c... | python|tensorflow|gradient-descent|backpropagation | 1 |
357,823 | 63,785,159 | Pandas columns of lists, check if lists are intersecting | <p>I have a data frame, called <code>a</code>, that has the following structure:</p>
<pre><code>df = pd.DataFrame({
'id': [1, 2, 3],
'numbers_a': [[2, 3, 5], [1, 2, 4], [4, 6, 9]],
'numbers_b': [[2, 1, 3], [10, 11], [4, 5, 7]]
})
df
| id | numbers_a | numbers_b |
|----|-----------|-----------|
| 1 | [2, 3... | <p>Try set intersection:</p>
<pre><code>df['numbers_a'].map(set) & df['numbers_b'].map(set)
0 True
1 False
2 True
dtype: bool
</code></pre>
<p>This works well with the overloaded pandas boolean operators, although it isn't particularly performant.</p>
<hr />
<p>Another method involves list comprehension... | python|pandas|dataframe | 3 |
357,824 | 64,133,919 | Plot multiple line graph from Pandas into Seaborn | <p>I'm trying to plot a multi line-graph plot from a pandas dataframe using seaborn. Below is a .csv of the of the data and the desired plot. In excel I simply selected the whole dataset and swapped the axis. Technically there are 110 lines (rows) on this, but many aren't visible because they only contain 0's.</p>
<p><... | <p>The seaborn <code>lineplot()</code> <a href="https://seaborn.pydata.org/generated/seaborn.lineplot.html" rel="nofollow noreferrer">documentation</a> says:</p>
<blockquote>
<p>Passing the entire wide-form dataset to <code>data</code> plots a separate line for each column</p>
</blockquote>
<p>Since you want a line for... | python|pandas|plot|seaborn | 1 |
357,825 | 64,153,348 | add element to a nested array in a dictionary converting series to readable dictionary with nested arrays python | <p>I need to add an element to an array that is inside a dictionary, This is my code:</p>
<pre class="lang-py prettyprint-override"><code>indexes9 = []
dataInfected9 = {}
for index, value in data9.items():
if index[0] not in indexes9:
indexes9.append(index[0])
dataInfected9[index[1]].append(value)
</cod... | <p>Using <code>dict.setdefault</code></p>
<p><strong>Ex:</strong></p>
<pre><code>dataInfected9 = {}
indexes = set() #Using set to prevent dups.
for (k, v), n in data9.items():
indexes.add(k)
dataInfected9.setdefault(v, []).append(n)
</code></pre> | python|arrays|python-3.x|pandas|dictionary | 1 |
357,826 | 64,102,481 | Is there an easy way to find the 'coordinates' of an element in a pandas dataframe? | <p>I have a dataframe 'ptable' which looks like this:</p>
<p><img src="https://i.stack.imgur.com/b8ODY.png" alt="A 188 x 32 dataframe of the periodic table containing varying types of data." /></p>
<p>We were given a very easy task of finding one value in the dataframe, the boiling point of argon. Their sample solution... | <p>Here is my solution:</p>
<pre class="lang-py prettyprint-override"><code>import pandas as pd
def locate(data, query, value, output):
df = pd.DataFrame(data = data)
# create a list of values in the query (column)
values = df[query].tolist()
row = 0
if value in values:
row = values.index(v... | python|pandas|dataframe | 1 |
357,827 | 63,958,039 | How do I interpret tf.keras.Model.predict() output? | <p>I am having trouble finding the documentation I need on this. To summarize the issue, I have trained a tf.keras model using two classes of images, labeled as '0' or '1'. I now want to use this model to predict whether new images are a '0' or '1'. My question is as follows: <code>model.predict()</code> returns a numb... | <blockquote>
<p>is <code>pred</code> the probability the image is a 1, and <code>1 - pred</code> the probability the image is a 0?</p>
</blockquote>
<p>Yes, that is correct. If you want to get hard class (i.e., 0 or 1), then you can threshold the output. 0.5 is a common threshold, but I have also seen 0.3. This is some... | python-3.x|tensorflow|keras|computer-vision | 1 |
357,828 | 64,047,518 | How to specify a columns dtype by its index rather than its name in pandas pd.read_excel | <p>I need to read data from Excel but while doing it I should not specify the columns by their names. How can I set data types using indexing?</p>
<p>For instance:</p>
<pre><code>df = pd.read_excel('file.xlsx',
sheet_name='sheet1',
index_col=None,
dtype={'column_x':s... | <p>use <code>header=None</code> then access the columns by their index position.</p>
<pre><code>df = pd.DataFrame({'A' : [0,1,2,3], 'B' : ['A','B','C','A']})
print(df.dtypes)
A int64
B object
dtype: object
df.to_excel('file.xlsx'index=False)
df = pd.read_excel('file.xlsx',index_col=None)
print(df.dtypes)
A ... | python|excel|pandas | 0 |
357,829 | 63,781,059 | Sorting a list of ordered dictionaries in Pandas for csv output | <p>I have added a dbf file into a Pandas series dataframe. The original data is in a listed dictionary like so.</p>
<pre><code>0 {'a': 'av1','b': 'bv1', 'c' : 'cv1',...
1 {'a': 'av2', b': 'bv2', 'c' : 'cv2',...
2 {'a': 'av3', b': 'bv3', 'c' : 'cv3',...
3 {'a': 'av4', b': 'bv4', 'c' : 'cv4',...
4... | <p>We need to do <code>ast</code> then pass to <code>DataFrame</code></p>
<pre><code>import ast
yourdf = pd.DataFrame(df['col_name'].apply(ast.literal_eval).tolist())
Out[191]:
a b c
0 av1 bv1 cv1
1 av1 bv1 cv1
</code></pre> | python|pandas|csv|dictionary|ordereddictionary | 0 |
357,830 | 64,145,423 | Improving Performance of Inflating data by generating missing sequential data | <p>I have a dataset which has missing data - around 10,000 to 500,000 rows.</p>
<pre><code>1 2 3 13 14 15 18 26 ...
</code></pre>
<p>I need to fill the data in between so that it is continuous for subsequent processing.</p>
<pre><code>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 ...
</code></pre... | <p>Possibly I was looking at something like this - this runs 23 times faster. Not tested on the entire dataset though!</p>
<pre><code># Optimization 2: Try out using Numpy array :
start_time = time.time_ns()
start_time_s = time.time()
delta = 100
times = numpy.arange(start = start_time_s, stop =start_time_s + delta, s... | python|pandas|performance | 0 |
357,831 | 64,032,945 | Cast decimal number to datetime in Pandas | <p>I am attempting to convert a decimal number to a datatime. An example dateframe is:</p>
<pre><code> timestep_time vehicle_angle vehicle_id vehicle_pos vehicle_speed vehicle_x vehicle_y Cluster
0 0.00 113.79 0 5.10 0.00 295.36 438.47 1
1 ... | <p>I received a <code>Decimal</code> Epoch time in milliseconds from an AWS response. I didn't see a simple way to convert that to a <code>datetime</code> with Panda unless you first cast it to an int or string.</p>
<p>I used this:</p>
<pre><code># cast from Decimal to int
df["creationDate"] = df["creat... | python|pandas|time | 0 |
357,832 | 63,867,452 | Normalization of dictionary values | <p>For normalization of elements in numpy arrary, we can use the sklearn normalize function:</p>
<pre><code>import numpy as np
from sklearn.preprocessing import normalize
b=np.array([[0, 0.2, 0.2, 0.2, .30, .24, 0]])
print(type(b))
normalized = normalize(b)
print("Normalized Data = ", normalized)
</code></pr... | <p>By default, the <code>Normalizer</code> function considers the <code>L-2</code> norm normalization, but in the following example we will additionally consider <code>L-1</code> norm normalization. Taking as example, the array you provided this will be</p>
<pre><code>X = np.array([val for val in xy.values()])
# If yo... | python|numpy|dictionary|scikit-learn|normalization | 1 |
357,833 | 63,769,671 | How to subtract two columns of lists from each other in pandas? | <p>I have data in a tab-separated value text file that look like this:</p>
<pre><code>FileName Onsets Offsets
FileName1 [9, 270, 763] [188, 727, 1252]
FileName2 [52, 634, 1166, 1775, 2104] [472, 1034, 1575, 1970, 2457]
FileName3 [180, 560, 1332, 1532] [356, 1286, 1488, 2018]
</code></pre>
<p>These ... | <ul>
<li>The first issue is, you have columns of strings that must be converted to lists, using <a href="https://docs.python.org/3/library/ast.html#ast.literal_eval" rel="nofollow noreferrer"><code>ast.literal_eval</code></a></li>
<li>In order to perform array subtracting, convert the values in <code>'Onsets'</code> an... | python|arrays|pandas|numpy|text | 2 |
357,834 | 63,746,426 | Tensorflow eigenvalue decomposition is extremely slow | <p>I am using eigendecomposition in Tensorflow and find that it is extremely slow. Here's the code to show Tensorflow's speed vs numpy and scipy:</p>
<pre><code>import numpy as np
import scipy as sp
import tensorflow as tf
from time import time
A = np.random.randn(400, 400)
A_tf = tf.constant(A)
cur = time()
d, v = s... | <p>Try wrapping <code>tf.linalg.eig</code> in a <code>@tf.function</code> and you can observe improvement in speed.
This is because it converted to graph mode and there will be some optimizations can be done.</p>
<p>Incase of eager mode these may not preformed and it is default behavior in TF 2.x.</p>
<p>You can wrap y... | python|tensorflow|eigenvalue|eigenvector | 0 |
357,835 | 63,775,235 | str.contains search in a pandas column for multiple strings separated by a comma | <p>I have a dataframe that looks like the following:</p>
<pre><code>Company keywords
A SOFTWARE, IOT, PLATFORM, ENERGY, OPEN SOURCE
B ENERGY, PUBLIC UTILITIES, HARDWARE, SOFTWARE
C ENERGY, SOFTWARE, ELECTROMOBILITY, EMISSIONS
D ... | <p>Try:</p>
<pre class="lang-py prettyprint-override"><code>import numpy as np
# Boolean indices of rows including word SOFTWARE
ind_df_software=df["keywords"].str.contains("SOFTWARE")
# Boolean indices of rows including word HARDWARE
ind_df_hardware=df["keywords"].str.contains("HAR... | pandas|dataframe | 1 |
357,836 | 63,760,836 | pandas dataframe update based on date values in 2 dataframes | <p>I have two dataframes, a snippet looks like this:</p>
<pre><code>year1 = {'DAY':['MON', 'MON', 'MON', 'TUE', 'TUE', 'TUE'],
'TEMP':[12, 13, 14, 15, 15, 18],
'DATE':['01/01/20', '02/01/20', '03/01/20', '06/01/20', '07/01/20', '08/01/20']}
df1 = pd.DataFrame(year1)
year2 = {'DAY':['MON', 'MON', 'MON', 'TUE', ... | <p>You can use <code>pd.merge</code> on the <code>DATE</code> and <code>DAY</code> columns since the same dates will have the same day. Take the average of the <code>TEMP_x</code> and <code>TEMP_y</code> columns created from the merge and call it <code>AVG_TEMP</code>, then drop the <code>TEMP_x</code> and <code>TEMP_y... | python|pandas|dataframe|merge | 2 |
357,837 | 64,128,947 | how to access tf.data.Dataset within a keras custom callback? | <p>I have written a custom keras callback to check the augmented data from a generator. (See <a href="https://stackoverflow.com/a/63910397/1295595">this answer</a> for the full code.) However, when I tried to use the same callback for a <code>tf.data.Dataset</code>, it gave me an error:</p>
<pre><code> File "/pat... | <p>What ended up working for me was the following, using <a href="https://www.tensorflow.org/datasets/api_docs/python/tfds" rel="nofollow noreferrer"><code>tfds</code></a>:</p>
<p>the <code>__init__</code> function:</p>
<pre><code>def __init__(self, logdir, train, validation=None):
super(TensorBoardImage, self).__i... | python|tensorflow|keras|callback|tf.data.dataset | 1 |
357,838 | 63,920,897 | Conversion between "pandas.Series" to numpy array | <p>I have a <code>pandas.Series</code> that every element is a <code>numpy.array</code>,
For example:</p>
<pre class="lang-py prettyprint-override"><code>p = pandas.Series([numpy.array([1,2]), numpy.array([2,4])])
</code></pre>
<p>I try to convert the whole <code>Series</code> into a multi-dimensional (2,2) <code>numpy... | <p>I would suggest following conversion:</p>
<pre><code>import numpy as np
import pandas as pd
p = pd.Series([np.array([1,2]), np.array([2,4])])
np.array(p.values.tolist()).shape
</code></pre> | python|pandas|numpy | 0 |
357,839 | 64,031,601 | Dataframe: adding a column with mean by other column group | <p>Say I have the following DataFrame:</p>
<pre><code>data = pd.DataFrame({'id' : ['1','2','3','4','5'], 'group' : ['1','1','2','1','2'],
'state' : ['True','False','False','True','True'], 'value' : [11,12,5,8,3]})
</code></pre>
<p>I would like to add to the previous dataframe, a new column with the average <code... | <p>IIUC change <code>state</code> column back to boolean so you can <code>sum</code>, then <code>groupby</code> and <code>transform</code>:</p>
<pre><code>df["avg_state"] = (df.assign(state=df["state"].map({"True":True, "False":False}))
.groupby("group&q... | python|pandas|dataframe|pandas-groupby|mean | 2 |
357,840 | 63,863,773 | Speeding Up DataFrame .mask() Iteration | <p>Is there a faster way to perform conditional calculations to certain DataFrame columns than using .mask()? My code shown below seems to work, but it can be slow when I use a large dataset.</p>
<pre><code>def inversing(column):
DF = pd.read_csv('DataFrame.csv')
DF[column] = DF[column].mask(DF[column] !=0, 1/D... | <p>There are multiple ways to go about this, but a straight forward one would be using a lambda:
<code>df[column].apply(lambda x: 1/x if x != 0 else x)</code></p>
<p>Full code in jupyter notebook with timings:</p>
<pre><code>import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randint(-50, 50, 100))
%tim... | python|pandas|iteration|conditional-statements | 0 |
357,841 | 64,146,136 | Differentiate between Circles and Radio buttons Opencv python | <p>I have a task to detect Circles and Radio buttons in an Image. For this I tried Hough circles by having different parameters.</p>
<p><strong>Issues</strong>: If the circles in the Image are of same radius of Radio buttons both are detected, but in our case it should only detect only one.</p>
<p>Is there a way to di... | <ol>
<li>Find and draw all the contours in the answer-sheet.</li>
</ol>
<hr />
<ol start="2">
<li>Apply <code>HoughCircles</code></li>
</ol>
<hr />
<p><strong>Step #1:</strong> We could start with finding all contours in the given <a href="https://content.zipgrade.com/static/pdfs/ZipGrade20QuestionV2.pdf" rel="nofollow... | python|numpy|opencv | 1 |
357,842 | 64,036,162 | Store dataframe in variable python | <p>I'm still learning python and I have a simple problem and i made a reasearch already with no success.</p>
<p>I have this simple code:</p>
<pre><code>import pandas as pd
def readDF():
df = **reads a df from excel**
return df
DF = readDF()
</code></pre>
<p>The problem is that everytime i try to access the DF v... | <p>You could load the data into a data-storage class which calls the function once, or doesn't use a function at all:</p>
<pre><code>import pandas as pd
# Option 1: call function
class DataStorage:
def __init__(self):
self.df = self.readDF()
def readDF(self):
df = pd.read_excel('test.xls... | python|pandas | 0 |
357,843 | 63,820,894 | Calculate the average between the same month in a time series | <p>I have a dataset between 2002 - 2018 which contains 1 value per month, 198 rows in total.</p>
<p>I want to know how I can average all the values from the same month (e.g. January/2003 + ... + January/2018)</p>
<pre><code>dateparse = lambda dates: pd.datetime.strptime(dates, '%Y-%m-%d')
df = pd.read_csv('turbidez.cs... | <p>Use <code>pandas.to_datetime</code> and <code>pandas.Series.dt.month</code>:</p>
<pre><code># Sample data
date x
0 2002-07-31 8.466111
1 2003-07-31 6.234259
2 2002-09-30 8.160763
3 2003-09-30 4.927685
4 2002-11-30 8.125012
df["date"] = pd.to_datetime(df["date"] )
new_df = df... | python|pandas | 1 |
357,844 | 64,144,956 | What is wrong with this Binomial Tree Backwards Induction European Call Option Pricing Function? | <p>The function below works perfectly and only needs one thing:
Removal of the for loop that creates the 1000 element array arr.</p>
<p>Can you help me get rid of that for loop?
Code is below</p>
<pre><code>#Test with european call
import numpy as np
T = 1
N = 1000
sigma = 0.5
r = 0.02
S = 1
K = 0.7
u = np.exp(sigma*np... | <p>I am note sure if you can omit that loop in an efficient way, but when we do not use <code>np.delete()</code> but just use the indices to shrink <code>payoff</code> in every iteration I already got a big speed increase on my machine:</p>
<pre class="lang-py prettyprint-override"><code># ...
arr = np.full(N+1, d/u)
a... | python|numpy | 0 |
357,845 | 63,910,662 | Best way to save extracted features for future training deep learning | <p>I am using the VGG19 architecture to extract features from my images. Here is my code to do so:</p>
<pre><code>model = VGG19(include_top=False)
image_paths = glob.glob('train/*/*')
def extract_features(model, path):
img_path = path
img = image.load_img(img_path, target_size=(224,224))
x = image.img_to_array(i... | <p>I have two suggestions:</p>
<p>Save the features per file, e.g. for cat.png save it as cat.npy; and as you go over your list of files (cat.png, dog.png, snake.png), first check if the feature was already created and directly load the .npy file.</p>
<p>The second approach is using a dictionary data structure, where y... | python|tensorflow|keras|deep-learning | 0 |
357,846 | 64,051,278 | pandas filter if string in row A contains row b element | <p>Let's say we have this df</p>
<pre><code>d = pd.DataFrame({'year': [2010, 2020, 2010], 'colors': ['red', 'white', 'blue'], "shirt" : ["red shirt", "green and red shirt", "yellow shirt"] })
</code></pre>
<p>like this:</p>
<pre><code> year colors shirt
0 2010 red ... | <p>I believe you need <code>df.apply</code></p>
<p><strong>Ex:</strong></p>
<pre><code>df = pd.DataFrame({'year': [2010, 2020, 2010], 'colors': ['red', 'white', 'blue'], "shirt" : ["red shirt", "green and red shirt", "yellow shirt"] })
print(df[(df.year == 2010) & df.apply(la... | python|python-3.x|pandas | 2 |
357,847 | 64,122,927 | Get unique values along axis in a Numpy 4d array | <p>Given an array (representing an image with 3 rows of 19 RGB pixels) like the following:</p>
<pre><code>test_nested_array = np.array([[[
[ 6., 11., 14.],
[ 6., 11., 14.],
[ 6., 11., 14.],
[ 6., 11., 14.],
[ 7., 12., 15.],
[ 7., 12., 15.],
[ 7., 12.,... | <p>Even though the tip by @Divakar helpt me further, I ended up using <code>numpy_indexed</code> package to extract the unique entries like this:</p>
<pre><code>import numpy_indexed as npi
uniq = npi.unique(data)
</code></pre>
<p>This turned out much faster for my use-case.</p> | python|arrays|numpy|image-processing|multidimensional-array | 0 |
357,848 | 63,998,611 | Pandas: Updating a rolling average only every minute for one second data | <p>I have a dataframe where rows of data are in one second intervals, so 08:00:00, 08:00:01, etc. I want to take a rolling average over a period of 10 minutes, but I only want the rolling average to update on a minute by minute basis. So the rolling average values for 08:10:00 - 08:10:59 would all be the same value, an... | <p>I have another column for the seconds value called df['sec']. I got the indices of rows where seconds = 0 (the zeroth second of each minute) and replaced every other row with np.nan. Then I used fillna(method='ffill') to copy values downward.</p>
<pre><code>df['counts-avg'] = df['counts'].rolling(window=600).mean()
... | python|pandas | 0 |
357,849 | 63,819,258 | StopIteration issue in pandas dataframe with dictionary python | <p>I have 3 column (DM1_ID, DM2_ID, pairs) pandas dataframe with 1 million records.Also, I have a dictionary containing key and multiple values.
The function check the dictionary values and get the key and put that key in new_ID field.
Function working fine for small part of pandas dataframe but when I applied it to ... | <p>From your data, essentially you just need to look up one column, say "DM1_ID", as the corresponding "DM2_ID" should belong to the same key in jdic. In this case, it's quite easy to do. I would just reverse your dictionary.</p>
<pre><code>jdic = {10045: [1, 6, 7,10045, 15, 45, 55, 80], 11945: [119... | python|pandas|dataframe|dictionary|stopiteration | 1 |
357,850 | 63,923,352 | Can you filter a pandas dataframe based on a sum or count or multiple variables? | <p>I'm trying to filter a Pandas dataframe based on a set of or conditions, but they're all very similar, and I'm wondering if there's a more efficient way to write this.</p>
<p>Specifically, I want to include rows from the dataframe (df) where any of a set of variables is 1:</p>
<pre><code>df.query("Q50r5==1 or Q... | <p>You can use <code>any</code> with <code>axis = 1</code> to check that at least one value is <code>True</code> in a row.</p>
<p>For example, you can run</p>
<pre><code>df[(df[["Q20r1", "Q20r2", "Q20r3"]] == 1).any(axis = 1)]
</code></pre> | python|pandas | 1 |
357,851 | 63,953,309 | Reduce image channels in python | <p>I have an image with dimension of (128, 19, 3), i want to convert it to (128, 19, 1). I used this code (in python) but it converts the image size to (128, 19) not (128, 19, 1) which i want. thanks if anyone can help</p>
<pre><code>from PIL import Image
import glob
images = glob.glob('D:\\thesis\\Paper 3\\Feature
Ex... | <p>simply add a dimension to the end of your array:</p>
<pre><code>img = img[...,None]
</code></pre> | python|image|numpy|rgb | 0 |
357,852 | 63,870,418 | Why numpy arrays are slower than lists with for loops? | <p>Aren't arrays supposed to be faster since they consume less memory
and as I know with arrays python doesn't apply type method on the elements as it in the lists.</p>
<pre><code>import numpy as np
import time
length = 150000000
my_list = range(length)
list_start_time = time.time()
for item in my_list:
pass... | <p><code>my_list = range(length)</code> is a <code>range</code> object, more of a generator than a list</p>
<p>In the loop:</p>
<pre><code> for i in range(10):
pass
</code></pre>
<p>there's no significant memory use. But even if we did iterate on a list, each <code>i</code> would just be a reference to an item i... | python|numpy | 1 |
357,853 | 63,959,460 | Extract variables from XML to Pandas | <p>I am working on parsing XML variables to pandas dataframe. The XML files looks like (
<strong>This XML file has been simplified for demo</strong>)</p>
<pre><code><Instrm>
<Rcrd>
<FinPpt>
<Id>BT0007YSAWK</Id>
<FullNm>Turbo Car</FullNm>
... | <p>Assuming a <code><root></code> node in posted XML without namespaces, consider building a dictionary via list/dict comprehension and combining sub dictionaries (<a href="https://stackoverflow.com/a/26853961/1422451">available in Python 3.5+</a>) that parse to needed nodes. Then call the <code>DataFrame()</code... | python|xml|pandas|parsing|xml-parsing | 0 |
357,854 | 64,014,484 | apply function not working as expected in groupby | <p>I have a dataframe that looks like:</p>
<pre><code>ID | timestamp |Phase| current
========================================
001 | 2020-09-20 07:00 | A | 1.4
001 | 2020-09-20 07:00 | B | 2.0
001 | 2020-09-20 07:00 | C | 1.6
002 | 2020-09-20 09:00 | A | 1.4
002 | 2020-09-20 09:00 | B | 1.2... | <p>IIUC you can find the desired rows with pandas functions</p>
<pre><code>df['cng'] = (df.groupby('ID')['current'].pct_change() + 1).groupby(df.ID).cumprod()-1
df[df.groupby('ID')['cng'].transform(lambda x: x.fillna(x.max())) > .30]
</code></pre>
<p>Output</p>
<pre><code> ID timestamp Phase current ... | python|pandas|pandas-groupby|pandas-apply | 0 |
357,855 | 46,902,567 | Efficiently taking time slices of variable length in a dataframe | <p>I would like to efficiently slice a DataFrame with a DatetimeIndex (similar to a resample or groupby operation), but the desired time slices are different lengths.</p>
<p>This is relatively easy to do by looping (see code below), but with large timeseries the multiple slices quickly becomes slow. Any suggestions on... | <p>You can do this as an apply, which will concat the results rather than iteratively update the DataFrame:</p>
<pre><code>In [11]: slicer_df.apply((lambda row: \
df[(df.index >= row.start_window)
& (df.index <= row.end_window)].sum()), axis=1)
Out[11]:
1 36.381155
2 111.... | python|pandas | 3 |
357,856 | 46,846,828 | How to insert multiple IDs into a sql statement? | <p>New to python and pandas, Im facing the following issue:</p>
<p>I would like to pass multiple string into a sql query and struggle to insert the delimiter ',' :</p>
<pre><code>Example data
import pandas as pd
data = [['Alex',10],['Bob',12],['Clarke',13]]
df = pd.DataFrame(data,columns=['Name','Age'])
print (df)
... | <p>Demo:</p>
<pre><code>sql_ = """
SELECT *
FROM emptable
WHERE empID IN ({})
"""
sql = sql_.format(','.join([x for x in ['?'] * len(df)]))
print(sql)
new = pd.read_sql(query, conn, params=tuple(df['Name']))
</code></pre>
<p>Output:</p>
<pre><code>In [166]: print(sql)
SELECT *
FROM emptable
WHERE empID IN (?,?,?)... | python|pandas | 2 |
357,857 | 46,680,852 | reshaping df into multindex and concatenating along keys | <p>I have a dataframe called "df1":</p>
<pre><code> 0 1 2
2015-10-13 96 97.0 59.0
2008-03-18 90 91.0 92.0
</code></pre>
<p>and would like to reshape it to :</p>
<pre><code> 0
2015-10-13 96
97.0
59.0
2008-03-18 9... | <p>This should get you there, if you're OK with an extra level in the index:</p>
<pre><code>import pandas
data = {'0': {'2008-03-18': 90, '2015-10-13': 96},
'1': {'2008-03-18': 91.0, '2015-10-13': 97.0},
'2': {'2008-03-18': 92.0, '2015-10-13': 59.0}}
df1 = pandas.DataFrame(data)
df2 = df1
result = p... | python|pandas|dataframe | 2 |
357,858 | 46,820,705 | Rename specific columns with numbers with str+number | <p>I originally have r number of csv files.</p>
<p>I created one dataframe with 9 columns and r of them have numbers as headers.</p>
<p>I would like to target only them and change their name into ['Apple']+range(len(files)). </p>
<p>Example:
I have 3 csv files.</p>
<p>The current 3 targeted columns in my dataframe ... | <p>IIUC, you can initialise a <code>itertools.count</code> object and reset the columns in a list comprehension.</p>
<pre><code>from itertools import count
cnt = count(1)
df.columns = ['Apple{}'.format(next(cnt)) if
str(x).isdigit() else x for x in df.columns]
</code></pre>
<p>This will also work very well i... | python|pandas | 1 |
357,859 | 47,073,936 | Matrix of pairwise row operations on pandas.DataFrame | <p>I want to create a matrix of the results of operations on all pairs of rows in a DataFrame.</p>
<p>Here's an example of what I want:</p>
<pre><code>df = pandas.DataFrame({'val': [ 2, 3, 5, 7 ],
'foo': ['f1', 'f2', 'f3', 'f4']},
index= ['n1', 'n2', 'n3', 'n... | <p>You can just use broadcasted numpy operations:</p>
<pre><code>v = df.val.values[:, None] * df.val.values
v
array([[ 4, 6, 10, 14],
[ 6, 9, 15, 21],
[10, 15, 25, 35],
[14, 21, 35, 49]])
x = df.foo.values[:, None] + df.foo.values
x
array([['f1f1', 'f1f2', 'f1f3', 'f1f4'],
['f2f1', 'f2... | python|pandas|numpy|dataframe|pairwise | 1 |
357,860 | 47,050,161 | Gini coefficient with keras in python | <p>I want to calculate simple NN model with gini coefficient as its optimizer function. Here is the my gini function:</p>
<pre><code>def gini(actual, pred):
nT = K.shape(actual)[-1]
n = K.cast(nT, dtype='int32')
inds = K.reverse(tf.nn.top_k(pred, n)[1], axes=[0])
a_s = K.gather(actual, inds)
a_c = ... | <p>This error is typical for functions that are not differentiable. (It also happens when some var is <code>None</code> and shouldn't be. Sometimes it's the case that someone forgot to add the <code>return</code> statement to a custom function somewhere or something like that). </p>
<p>In your case, it's not differe... | tensorflow|neural-network|keras|gini | 0 |
357,861 | 46,723,478 | Convert Pandas Column to dataframe | <p>I have a pandas dataframe column called 'Date' with entries of the format: '%Y%m%d%H%M%H%M' (first %H%M is local time & the second %H%M is UTC). </p>
<p>I want to convert that to the format: %Y-%m-%d_%H%M (keeping the UTC %H%M).</p>
<pre><code>obs_df = pd.read_csv(obs, names= ['WBAN','Date','Extinc Coeff', 'D/... | <p>Use <code>format='%Y%m%d%H%M%S%f'</code></p>
<pre><code>In [1454]: pd.to_datetime(df.Date, format='%Y%m%d%H%M%S%f')
Out[1454]:
0 2014-10-01 08:48:13.480
1 2014-10-01 08:49:13.490
2 2014-10-01 08:50:13.500
3 2014-10-01 08:51:13.510
4 2014-10-01 08:52:13.520
Name: Date, dtype: datetime64[ns]
</code></pre>
... | python|pandas|datetime|dataframe|python-datetime | 1 |
357,862 | 46,736,803 | How to optimize for loops for generating a new random Poisson array in python? | <p>I want to read an grayscale image, say something with (248, 480, 3) shape, then use each element of it as the lam value for making a Poisson random value and do this for each element and make a new data set with the same shape. I want to do this as much as <code>nscan</code>, then I want to add them all together and... | <p><a href="https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.random.poisson.html#numpy-random-poisson" rel="nofollow noreferrer">numpy.random.poisson</a> can entirely replace your <code>genP()</code> function... This is basically guaranteed to be much faster.</p>
<blockquote>
<p>If size is None (def... | python|loops|numpy|random | 1 |
357,863 | 46,676,419 | Why can't I remove the default pandas plot logy yticklabels? | <p>Pandas creates default yticklabels for logy plots. I want to replace these labels with my own labels but for some reason I can't seem to remove the default labels.</p>
<p>If I specify the yticks inside the <code>plot</code> method, it just writes over the existing default label:</p>
<pre><code>x = [1, 1.5, 2, 2.5,... | <p>Matplotlib plots may have major and minor ticks and ticklabels. In cases where the logarithmic plot ranges over less than a decade, minor ticklabels are set on by default. You may set them to an empty list, via <code>ax.set_yticklabels([],minor=True)</code>, or you may turn them off completely via <code>ax.minortick... | pandas|matplotlib | 3 |
357,864 | 47,024,082 | What is a "grappler item" in tensorflow terminology? | <p>I am trying to understand the "grappler" module located at:
<a href="https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/grappler" rel="nofollow noreferrer">https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/grappler</a></p>
<p>Can somebody tell me what is meant by the "grappler i... | <p>Grappler is Tensorflow's optimization module. When a graph is run, it is optimized similar to how a compiler optimizes a program during compilation. According to <a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/grappler/grappler_item.h" rel="nofollow noreferrer">the source</a>, <code>gra... | tensorflow | 3 |
357,865 | 46,823,941 | Groupby cumulative operations in successive rows pandas | <p>I want to find the rolling sum and rolling max for the column <code>B</code> for the same values in <code>A</code> in <code>df</code></p>
<pre><code>df = pd.DataFrame({'A': ['a', 'a', 'a', 'b', 'b', 'b', 'b'], 'B': [5, 2, 4, 7, 1, 11, 3]})
df_result = pd.DataFrame({'A': ['a', 'a', 'a', 'b', 'b', 'b', 'b'], 'B': [5... | <p>Use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html" rel="nofollow noreferrer"><code>groupby</code></a> with aggregation by <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.core.groupby.DataFrameGroupBy.agg.html" rel="nofollow noreferrer"><code>agg</c... | python|pandas|pandas-groupby | 3 |
357,866 | 46,839,073 | Tensorflow object detection API RCNN is slow on CPU: 1 frame per min | <p>I am using a locally trained model from tensorflow object detection API. I am using the <code>faster_rcnn_inception_resnet_v2_atrous_coco_11_06_2017</code> checkpoint. I retrained a 1 class model and exported it to SavedModel</p>
<pre><code>python object_detection/export_inference_graph.py \
--input_type image_... | <p>Hopefully this will help other users choose models. Here is my reported average times for 3.1 Ghz CPU processor on OSX (more info above).</p>
<p>faster_rcnn_inception_resnet_v2_atrous_coco: 45 sec/image</p>
<p>faster_rcnn_resnet101_coco: 16 sec/image</p>
<p>fcn_resnet101_coco: 7 sec/image</p>
<p>ssd_inception_v2... | tensorflow|object-detection | 10 |
357,867 | 46,672,168 | How to use tensorflow to approximate hessian matrix's norm | <p>I wonder is there any method to recompute gradients with updated weights within a graph or if there is any better way to do this. For example, for estimating hessian norm, we need to compute</p>
<pre><code>delta ~ N(0, I)
hessian_norm = 1/M \sum_{1}^{M} gradient(f(x+delta))- gradient(f(x-delta))/(2*delta)
</code></... | <p><code>b</code> is not a function of <code>a+delta</code>, so you get <code>None</code>s. You either need to create new value <code>b2</code> which depends on <code>a+delta</code>, or just move your <code>a</code> variable by <code>delta</code> and eval again to get second value.</p>
<p>This is similar to how you do... | python|tensorflow | 1 |
357,868 | 46,735,018 | Is this function creating a new TensorFlow graph each time? | <p>I am following <a href="https://rdipietro.github.io/tensorflow-scan-examples/" rel="nofollow noreferrer">this tutorial</a> on how to use <code>tf.scan</code> and I wrote a minimal working example (see code below). But each time the function <code>Model._step()</code> is called, isn't it creating another copy of the ... | <p>The <code>Model._step()</code> method will only be called once per <code>Model</code> object constructed. The <a href="https://www.tensorflow.org/api_docs/python/tf/scan" rel="nofollow noreferrer"><code>tf.scan()</code></a> function, like the <a href="https://www.tensorflow.org/api_docs/python/tf/while_loop" rel="no... | python|tensorflow | 1 |
357,869 | 47,013,530 | Tensorflow: gradient_override_map cannot override op tf.stack 's backward gradient | <p>I was trying to edit <code>tf.stack</code> op's backward gradient calculation mechanism with <code>tf.RegisterGradient</code>and<code>tf.gradient_override_map</code>, here are my codes:</p>
<pre><code>import tensorflow as tf
class SynthGradBuilder(object):
def __init__(self):
self.num_calls = 0
de... | <p>Tl;dr: The correct code should be:</p>
<pre><code>@tf.RegisterGradient(op_name)
def _grad_synth(op, grad):
x, y = tf.unstack(grad)
return [x, tf.zeros_like(y)]
g = tf.get_default_graph()
with g.gradient_override_map({"Pack": op_name}):
y = tf.stack([x, x])
</code></pre>
<hr>
<p>Because this is a quite comm... | python|tensorflow | 4 |
357,870 | 46,922,610 | Pandas Dataframe merge multi-key | <p>Everyone.</p>
<p>I have a question relate in DataFrame Merge.</p>
<p>I use DF1, DF2.</p>
<p>DF1 have UserID, ContentID, Genre Column.
DF2 have UserID, ContentID, Rating Column. </p>
<p>I want use multi coloumn-key( UserID, ContentID )
then match rows Rating display, none match row is NAN</p>
<p>Plz, check below... | <p>Simple <code>merge</code></p>
<pre><code>df1.merge(df2,on=['UserID','ContentID'],how='left')
Out[531]:
UserID ContentID Genre Rating
0 U-1 C-1 G-1 3.0
1 U-1 C-2 G-2 3.0
2 U-1 C-3 G-3 NaN
3 U-2 C-1 G-1 NaN
4 U-2 C-2 G-2 3.0
5 U-2 ... | python|pandas|dataframe|multiple-columns | 4 |
357,871 | 47,012,227 | Python numpy-like interface for tree structures | <p>I find myself often in need of a flexible data structure which is something between a dict and an array. I hope the following example will illustrate:</p>
<pre><code>a = ArrayStruct()
a['a', 'aa1'] = 1
a['a', 'aa2'] = 2
a['b', 0, 'subfield1'] = 4
a['b', 0, 'subfield2'] = 5
a['b', 1, 'subfield1'] = 6
a['b', 1, 'su... | <p>I made it myself. It's called a Duck. It currently lives in master branch of <a href="https://github.com/QUVA-Lab/artemis" rel="nofollow noreferrer">Artemis</a>. Here's some code demonstrating its use:</p>
<pre><code>from artemis.general.duck import Duck
import numpy as np
import pytest
# Demo 1: Dynamic assignm... | python|arrays|database|numpy|dictionary | 3 |
357,872 | 46,988,241 | Accessing json's elements with Python | <p>I use this code to load my file:</p>
<pre class="lang-python prettyprint-override"><code>with open('filepath') as myfile:
data = [next(myfile) for x in xrange(100)]
print data
print json.dumps(data, indent=1, sort_keys=False)
</code></pre>
<p>In the first case the structure I get, looks like:</p>
<pre><cod... | <p>This file is encoded twice in JSON. </p>
<p>In case you're using <code>json.dumps()</code> on a JSON file or if you use <code>json.dumps()</code> twice, then this will happen. Can you show us more about it?</p>
<p>Possible solution:</p>
<pre><code>import json
clear_json = json.loads(your_json)
</code></pre> | python|json|pandas|jupyter | 1 |
357,873 | 46,831,752 | Function take values from a dataframe as parameter | <p>I have a function which calculates the Holidays for a given year like this:</p>
<pre><code>holidays = bf.Holidays(year)
</code></pre>
<p>the problem is, there is no way to edit the Holidays function so i need another solutions.</p>
<p>I have a datafame with some years, example:</p>
<pre><code> year
0 2005
1 20... | <p>I think you can follow <a href="https://stackoverflow.com/questions/23586510/return-multiple-columns-from-apply-pandas">this example</a> and just write a little wrapper function to return the dates to their respective columns:</p>
<pre><code>def holiday_mapper(row):
holidays = bf.Holidays(row['year'],'HH').get_... | python|pandas|dataframe | 0 |
357,874 | 47,074,894 | how to calculate correlation between rows in python pandas data frame | <p>I have large data frame, and I need to calculate efficiently correlation between the data frame rows and given value list. for example:</p>
<pre><code>dfa= DataFrame(np.zeros((1,4)) ,columns=['a','b','c','d'])
dfa.ix[0] = [2,6,8,12]
a b c d
2.0 6.0 8.0 12.0
dfb= DataFrame([[2,6,8,12],[1,3,4,6],[-1,-3,-4,-6]],... | <p><strong>First of all, note that the last 2 correlations are 1 and -1 and not 0.5 and -0.5 as you expected.</strong></p>
<p><strong>Solution</strong></p>
<pre><code>dfb.corrwith(dfa.iloc[0], axis=1)
</code></pre>
<p><strong>Results</strong></p>
<pre><code>0 1.0
1 1.0
2 -1.0
dtype: float64
</code></pre> | python|performance|pandas|linear-regression|correlation | 6 |
357,875 | 47,036,922 | Handle surrogates with pandas | <p>When saving the data</p>
<pre><code>data.to_csv(outp_file, encoding='utf-8')
</code></pre>
<p>I sometimes get errors like this</p>
<blockquote>
<p>UnicodeEncodeError: 'utf-8' codec can't encode characters in position
233-234: surrogates not allowed</p>
</blockquote>
<p>In python3 you can simply replace such ... | <pre><code>for col in train.columns:
if train[col].dtype==object:
train[col]=train[col].apply(lambda x: np.nan if x==np.nan else str(x).encode('utf-8', 'replace').decode('utf-8'))
</code></pre>
<p>Try this. It worked for me</p> | python-3.x|pandas | 4 |
357,876 | 46,718,738 | DataFrame with 3 columns to dictionary of dictionaries | <p>I have following <code>DataFrame</code> with 3 columns </p>
<pre><code>my_label product count
175 '409' 41
175 '407' 8
175 '0.5L' 4
175 '1.5L' 4
177 ... | <p>The straightforward way is to groupby <code>my_label</code> then iterate over the resulting rows, grabbing the values you need:</p>
<pre><code>In [7]: df
Out[7]:
my_label product count
0 175 '409' 41
1 175 '407' 8
2 175 '0.5L' 4
3 175 '1.5L' ... | python|pandas|dataframe | 3 |
357,877 | 46,676,738 | Using Tensorflow on smartphones | <p>I've been learning a lot about the uses of Machine Learning and Google's Tensorflow. Mostly, developers use Python when developing with Tensorflow. I do realize that other languages can be used with Tensorflow as well, i.e. Java and C++. I see that Google s about to launch Tensorflow Lite that is supposed to be a ga... | <p>In short, yes. It would be safe to learn implementing TensorFlow using python and still comfortably develop machine learning enabled mobile apps.</p>
<p>Let me elaborate. Even with TensorFlow Lite, training the data can only happen on the server side; only the prediction, or the inference happens on the mobile devi... | java|android|python|mobile|tensorflow | 0 |
357,878 | 46,760,037 | How to calculate the mean index in array with NumPy | <p>How can I calculate the mean index T for an array nums that minimize the value of </p>
<pre><code>abs(sum(nums[:T])-sum(nums[T:]))
</code></pre> | <p>The specific problem you're trying to solve has a well known solution called Otsu's method. The code below is from <a href="https://learnopencv.com/otsu-thresholding-with-opencv/" rel="nofollow noreferrer">https://learnopencv.com/otsu-thresholding-with-opencv/</a>:</p>
<pre><code># Set total number of bins in the h... | python|numpy|average | 0 |
357,879 | 47,008,199 | How to traverse the result of tf.unqiue? | <p>After invoking <code>tf.unqiue</code>, the shape of tensor will be unknown, but I want to traverse the result of <code>tf.unqiue</code></p>
<p>Suppose <code>tensor = tf.unqiue(...)</code></p>
<p>I have tried:</p>
<ul>
<li>for i in tf.range(tf.shape(tensor)[0])</li>
<li>tf.unstack(tensor, num=tf.shape(tensor)[0])<... | <p>I just tried this:</p>
<pre><code>import tensorflow as tf
import numpy as np
a = tf.constant(np.random.randn(200), dtype='float32')
b = tf.unique(a)
print b[0] #Tensor("Unique:0", shape=(?,), dtype=float32)
c = tf.map_fn(lambda x: x*x, b[0])
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run... | tensorflow | 0 |
357,880 | 46,869,884 | How to replace values inside a list inside a pandas row | <p>I have a pandas dataframe that looks like this </p>
<pre><code>2 zero zero zero zero zero zero zero 2 zero zero ... 6 6 zero [2, 4] zero 2 zero zero zero zero
3 1 zero 6 1 zero zero zero zero zero zero ... zero zero zero zero... | <p><code>df.replace</code> will look for cells that contain that value, and replace it with the target.</p>
<p>In your case, you're dealing with a column of lists, so something a little more aggressive is needed. Let's try <code>astype(str)</code> + <code>str.replace</code> + <code>ast.literal_eval</code>.</p>
<pre><... | python|pandas|numpy|dataframe | 2 |
357,881 | 32,664,374 | How can I plot the number of rows that occurred per hour over a long period of time? | <p>I have a large CSV file that looks like this:</p>
<pre><code>ID,Time,Disposition,eventsID,Class,teamID
1,"2011-03-02 22:18:37",1,107,2,2
2,"2011-03-02 22:19:05",1,115,1,2
3,"2011-03-02 22:19:10",1,103,4,2
4,"2011-03-02 22:19:41",1,104,1,3
5,"2011-03-03 01:24:31",1,117,4,3
</code></pre>
<p>This data spans many mont... | <p>You can use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html" rel="nofollow noreferrer"><code>DataFrame.plot()</code></a> to plot the graph. Example -</p>
<pre><code>count_per_day.plot()
</code></pre>
<p>Demo with the example data you added -</p>
<p><a href="https://i.stac... | python|pandas | 1 |
357,882 | 33,030,336 | Converting a column value when filtering in pandas | <p>In a csv file which I read using pandas, there's a column of type bool but in the string format which is 'F' or 'T'. How can I convert it to the real Bool when filtering? No need to change in the source file, only when filtering:</p>
<pre><code># how it is now
if something:
df1 = df1[df1['str_bool_column'] == 'F'... | <p>You can convert that column to a column of <code>True/False</code> values after reading it from the csv. One method to do that would be to use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.map.html" rel="nofollow"><code>Series.map</code></a> , to map <code>'F'</code> to <code>False</co... | python|pandas | 0 |
357,883 | 32,968,167 | In pandas when querying, how to skip filters that are None? | <p>How can I skip (don't apply) the filters that are None?</p>
<pre><code>res = df[
(df['Column1'] >= column1_min) & (df_item['Column1'] <= column1_max) &
(df['Column2'].isin(column2) ) &
(df['Column3'] == column3) &
#.....
</code></pre>
<p>that is, if <code>column1_min</code> or <code>c... | <p>If I understood well, you can try this:</p>
<pre><code>res = df[( (df['Column1'] >= column1_min) if column1_min != None else True) &
( (df['Column1'] <= column1_max) if column1_max != None else True) &
( (df['Column2'].isin(column2) ) if column2 != None else True ) &
( (... | python|pandas | 1 |
357,884 | 33,003,752 | pandas DataFrame has only one row | <p>I have problem listing DataFrame rows. The below function returns only one row (if indented returns first row, if not indented returns the last one). Does anyone knows where's the problem?</p>
<pre><code>def ols_regression(formula, framedict):
for yp in framedict.keys():
ols_model = ols(formula, framedi... | <p>I solve the problem with appending the dictionary to array.</p>
<pre><code>def ols_regression(formula, framedict):
arr = []
for yp in framedict.keys():
ols_model = ols(formula, framedict[str(yp)]).fit()
year = int(yp[:-5])
params = ols_model.params
arr.append(dict(yp =... | python|pandas|dataframe | 1 |
357,885 | 33,059,857 | Add time entries to Pandas data series based on interpolation of existing values | <p>I have an annual pandas data series that looks like:</p>
<pre><code>Year Price
1940-12-31 33.85
1941-12-31 33.85
1942-12-31 33.85
1943-12-31 33.85
1944-12-31 33.85
1945-12-31 34.71
1946-12-31 34.71
1947-12-31 34.71
1948-12-31 34.71
1949-12-31 31.69
1950-12-31 34.72
</code><... | <p>The following code will do the job:</p>
<pre><code>df['Price'].resample('M').interpolate()
</code></pre>
<p>replace df with the name of your DataFrame.
resample('M') change the frequency of the series to monthly. (<a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.resample.html" rel="n... | python|pandas|time|interpolation|series | 2 |
357,886 | 32,633,265 | return a list from class object | <p>I am using multiprocessing module to generate 35 dataframes. I guess this will save my time. But the problem is that the class does not return anything. I expect the list of dataframes to be returned from self.dflist</p>
<p>Here is how to create dfnames list.</p>
<pre><code>urls=[]
fnames=[]
dfnames=[]
for x in xr... | <p>In your case I prefer to write as less code as possible and use <code>Pool</code>:</p>
<pre><code>import pandas as pd
import logging
import multiprocessing
def dframe_create(filename):
try:
return pd.read_excel(filename)
except Exception as e:
logging.error("Something went wrong: %s", e, ... | pandas|python-multiprocessing | 3 |
357,887 | 32,638,519 | Query same time value every day in Pandas timeseries | <p>I would like to get the 07h00 value every day, from a multiday DataFrame that has 24 hours of minute data in it each day. </p>
<pre><code>import numpy as np
import pandas as pd
aframe = pd.DataFrame([np.arange(10000), np.arange(10000) * 2]).T
aframe.index = pd.date_range("2015-09-01", periods = 10000, freq = "1min... | <p>You can use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DatetimeIndex.indexer_at_time.html" rel="noreferrer"><code>indexer_at_time</code></a>:</p>
<pre><code>>>> locs = aframe.index.indexer_at_time('7:00:00')
>>> aframe.iloc[locs]
0 1
2015-09-... | python|pandas|datetime|dataframe | 8 |
357,888 | 32,853,124 | Why do these two arrays have the same shape? | <p>So I am trying to create an array and then access the columns by name. So I came up with something like this:</p>
<pre><code>import numpy as np
data = np.ndarray(shape=(3,1000),
dtype=[('x',np.float64),
('y',np.float64),
('z',np.float64)])
</code></pre>
<p>I am... | <p>The reason why it comes out the same is because 'data' has a bit more structure than the one revealed by shape.</p>
<p>Example:</p>
<p><code>data[0][0] returns</code>:
(6.9182540632428e-310, 6.9182540633353e-310, 6.9182540633851e-310)</p>
<p>while <code>data['x'][0][0]:</code>
returns 6.9182540632427993e-310</p>... | python|numpy|scipy | 1 |
357,889 | 32,733,203 | "Unsparsing" numpy Arrays with Given Masks | <p>Suppose there are two arrays, <code>vals</code> contains values, and <code>masks</code> contains booleans indicating whether to use the values in <code>vals</code>, or <code>nan</code>s. The goal is to build an array <code>ret</code> of the same length as <code>masks</code>, containing the values from <code>vals</co... | <p>You could do something like this -</p>
<pre><code>out = np.empty(masks.shape,dtype=object)
out[masks] = vals[:masks.sum()]
</code></pre>
<p>Please note that <code>:masks.sum()</code> selects first <code>N</code> elements from <code>vals</code>, where <code>N</code> is the number of <code>TRUE</code> elements in m... | python|numpy|vectorization|sparse-matrix | 2 |
357,890 | 32,996,894 | How to get the all the columns which the datatype is not int or float with python pandas? | <p>I want to change the dataframe to numpy.ndarray with datatype float32, so I want to drop those column which dtype is object or other type which is not number. </p> | <pre><code>df.select_dtypes(include=['int', 'float'])
</code></pre>
<p>will do it for you. There's also an <code>exclude</code> option.</p> | pandas | 1 |
357,891 | 33,003,547 | How to filter through pandas pivot table | <p>I have a pivot table created from pandas (DataFrame object). </p>
<p>Currently, I have multiple indexes and I want to be able to filter through some of them. To clarify this is how the pivot tables looks like.</p>
<p><img src="https://i.stack.imgur.com/8nADP.png" alt="enter image description here"></p>
<p>There a... | <p>First, create a multi-indexed dataframe:</p>
<pre><code>df = pd.DataFrame({'i1': [1, 1, 1, 1], 'i2': [2, 2, 3, 3], 'i3': [4, 5, 4, 5], 'v1': [10] * 4, 'v2': [20] * 4}).set_index(['i1', 'i2', 'i3'])
>>> df
v1 v2
i1 i2 i3
1 2 4 10 20
5 10 20
3 4 10 20
5 10 20
... | python|pandas | 1 |
357,892 | 32,853,043 | pandas filtering consecutive rows | <p>I got a Dataframe with a Matrix colum like this</p>
<pre><code>11034-A
11034-B
1120-A
1121-A
112570-A
113-A
113.558
113.787-A
113.787-B
114-A
11691-A
11691-B
117-A RRS
12 X R
12-476-AT-A
12-476-AT-B
</code></pre>
<p>I'd like to filter only matrix that ends with A or B only when they are consecutive, so in the exam... | <p>Here is how I would do it.</p>
<pre><code>df['ShiftUp'] = df['matrix'].shift(-1)
df['ShiftDown'] = df['matrix'].shift()
def check_matrix(x):
if pd.isnull(x.ShiftUp) == False and x.matrix[:-1] == x.ShiftUp[:-1]:
return True
elif pd.isnull(x.ShiftDown) == False and x.matrix[:-1] == x.ShiftDown[:-1]:
... | python|pandas | 2 |
357,893 | 32,805,916 | Compute Jaccard distances on sparse matrix | <p>I have a large sparse matrix - using sparse.csr_matrix from scipy. The values are binary. For each row, I need to compute the Jaccard distance to every row in the same matrix. What's the most efficient way to do this? Even for a 10.000 x 10.000 matrix, my runtime takes minutes to finish. </p>
<p>Current solution:</... | <p>Vectorization is relatively easy if you use matrix multiplication to calculate the set intersections and then the rule <code>|union(a, b)| == |a| + |b| - |intersection(a, b)|</code> to determine the unions:</p>
<pre><code># Not actually necessary for sparse matrices, but it is for
# dense matrices and ndarrays, if... | python|numpy|scipy|sparse-matrix | 17 |
357,894 | 38,577,444 | How does one keep track of the training error of a Neural Network when using Batch Normalization in TensorFlow? | <p>I wanted to keep track of my training error as the Neural Network is trained. During testing, it is customary to remove the batch normalization layer. For example:</p>
<pre><code># when test
# is_training determines if Batch-norm is off or on
error = sess.run([opt, loss], feed_dict={x: bx, y: by, is_training=False}... | <p>For reporting you would use the same metric on both your training and test set.</p>
<p>For example, while training you might use <a href="https://en.wikipedia.org/wiki/Dropout_(neural_networks)" rel="nofollow">dropout</a> to increase the robustness of the net. To figure out how well your net performs out-of-sample,... | machine-learning|neural-network|tensorflow|conv-neural-network | 0 |
357,895 | 38,700,694 | Pandas: Set multiple MultiColumns as MultiIndex | <p>I generate an empty data frame as follows:</p>
<pre><code>topFields = ['desc', 'desc', 'price', 'price', 'units', 'units']
bottomFields = ['foo', 'bar', 'mean', 'mom_2', 'mean', 'mom_2']
resultsDf = pd.DataFrame(columns=pd.MultiIndex.from_arrays([topFields, bottomFields]))
</code></pre>
<p>Now I would like to set ... | <p>It looks like need <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.set_index.html" rel="nofollow"><code>set_index</code></a> by tuple:</p>
<pre><code>test = resultsDf.set_index(('desc', 'foo'))
print (test)
Empty DataFrame
Columns: [(desc, bar), (price, mean), (price, mom_2), (units,... | python|pandas | 1 |
357,896 | 38,575,672 | pandas: create pivot table by two different dimensions? | <p>I am a pandas newbie. I have a dataframe of examinations taken by sponsor and company:</p>
<pre><code>import pandas pd
df = pd.DataFrame({
'sponsor': ['A71991', 'A71991', 'A71991', 'A81001', 'A81001'],
'sponsor_class': ['Industry', 'Industry', 'Industry', 'NIH', 'NIH'],
'year': [2012, 2013, 2013, 2012, 2013]... | <p>I can see how to do it in quite a few steps:</p>
<pre><code>import numpy as np, pandas as pd
df['total'] = df['passed'].astype(int)
ldf = pd.pivot_table(df,index=['sponsor','sponsor_class'],columns='year',
values=['total'],aggfunc=len) # total counts
rdf = pd.pivot_table(df,index=['sponsor','sp... | python|pandas | 2 |
357,897 | 38,613,514 | Prepare Data Frames to be compared. Index manipulation, datetime and beyond | <p>Ok, this is a question in two steps. </p>
<p><strong>Step one:</strong> I have a pandas DataFrame like this:</p>
<pre><code> date time value
0 20100201 0 12
1 20100201 6 22
2 20100201 12 45
3 20100201 18 13
4 20100202 0 54... | <p><strong>Step 1 Answer</strong></p>
<pre><code>df['DateTime'] = (df['date'].astype(str) + ' ' + df['time'].astype(str) +':'+'00'+':'+'00').apply(lambda x: pd.to_datetime(str(x)))
df.set_index('DateTime', drop=True, append=False, inplace=True, verify_integrity=False)
df.drop(['date', 'time'], axis=1, level=None, in... | python|datetime|pandas | 1 |
357,898 | 38,548,871 | Tensorflow: How to Propagate Gradient Through tf.gather? | <p>I'm having some issues trying to propagate the gradient of my loss function with respect to a variable that represents the gather index, similar to what is done in spatial transformer networks (<a href="https://github.com/tensorflow/models/blob/master/transformer/spatial_transformer.py" rel="noreferrer">https://gith... | <p>The problem is that you can't differentiate since pt is an integer. It is selecting one index in the x placeholder so it does not have a derivative. Normally when you do this you would input an integer and use this to select a floating point value. You are doing it the other way around.</p> | tensorflow | 0 |
357,899 | 38,709,570 | Divide numpy matrix elements in 2-D matrix by sum of all elements in that position of 1-D | <p>If we have a matrix such as:</p>
<p>[ [ 2, 3 ] , [ 4, 9 ], [ 3, 1 ] ]</p>
<p>I want to know how to be able to divide matrix elements as follows:</p>
<p>Sum the elements in the same position of their respective 1-D vectors</p>
<p>2 + 4 + 3 = 9</p>
<p>3 + 9 + 1 = 13</p>
<p>Then divide each of the elements by the... | <h3>One solution:</h3>
<pre><code>import numpy as np
data = [[2, 3] , [4, 9], [3, 1]]
result = data / np.sum(data, axis=0)
print(result)
</code></pre>
<h3>Output:</h3>
<blockquote>
<p>[[ 0.22222222 0.23076923] <br>
[ 0.44444444 0.69230769] <br>
[ 0.33333333 0.07692308]]</p>
</blockquote> | python|numpy|matrix | 2 |
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