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 |
|---|---|---|---|---|---|---|
16,600 | 59,097,989 | Deep Q Learning - training slows down significantly | <p>I'm trying to build a deep Q network to play snake. I designed the game so that the window is 600 by 600 and the snake's head moves 30 pixels each tick. I implemented the DQN algorithm with memory replay and a target network, but as soon as the policy network starts updating its weights the training slows down signi... | <pre><code>def decay_epsilon(self, episode):
self.current_eps = self.eps_end + (self.eps_start - self.eps_end) * np.exp(-self.eps_decay * episode)
// part of code from train()
epsilon = self.current_eps
if epsilon > random.random():
action = np.random.choice(env.action_space) #expl... | python|tensorflow|keras|deep-learning|reinforcement-learning | 2 |
16,601 | 59,442,361 | EnvironmentNotWritableError in Conda | <p>I am trying to run this command <code>conda install pytorch torchvision cuda80 -c soumith
</code> from this link <code>https://github.com/kenshohara/video-classification-3d-cnn-pytorch</code> and I am getting error as below. I don't have root privileges. Can anyone tell me what I should do to fix this issue?</p>
<p... | <p>If you have a shared Anaconda then you should create your own local environments for your projects. E.g.,</p>
<pre><code>conda create --name my_env -c pytorch torchvision
</code></pre>
<p>Also note that <code>pytorch</code> is the official channel from which to get PyTorch and that <code>torchvision</code> has a P... | python-3.x|pytorch|conda | 0 |
16,602 | 59,343,462 | How to fit a pandas timeseries to a 24h graph? | <p>I have a pandas timeseries of multiple months and want to count occurences of a feature for different times of day. </p>
<p>I.e. I want to create a graph (using seaborn or matplotlib) with the time of day on the x axis (0 to 24 hours) and the relative number of occurences of a column on the y axis <a href="https://... | <p>You need to prepare the plot data first:</p>
<pre><code>hour = df['Created Date'].dt.hour.rename('Hour')
df_plot = df.groupby(hour).apply(lambda x: x['Open Data Channel Type'].value_counts() / x.shape[0]) \
.rename_axis(index=['Hour', 'Channel Type']) \
.to_frame('Frequency') \
.... | python|pandas|matplotlib|seaborn | 1 |
16,603 | 44,927,945 | How to compare 2 fields of same dataframe values and update result in another column | <p>How to compare 2 columns of same dataframe and update result in another column, if its matches update as <code>True</code> else <code>False</code>.</p>
<p><code>df</code>:</p>
<pre><code>Col1 Col2 Result
1234569 1234569 TRUE
256132 453543 FALSE
DSDFDSF DSDFDSF TRUE
TRYTR FGFH FALSE
</code></pre> | <p>This returns a boolean series: <code>df.col1==df.col2</code></p> | python|pandas|dataframe | 0 |
16,604 | 44,899,890 | How do I perform calculations on a string in pandas? | <p>I have a dataframe like this:</p>
<pre><code>Country Sales Assets
China 4B 320B
China 3B 125B
India 112M 100B
USA 39M 200B...
</code></pre>
<p>The <strong>Sales</strong> and <strong>assests</strong> columns have some values in billions and some in millio... | <p>Something like this should work</p>
<pre><code>data=df['Sales']
for value in data:
char=value[-1]
if char=='M'
toadd=float(value[:-1]/1000.0)
elif char=='B':
toadd=float(value[:-1])
totalsales=totalsales+toadd
</code></pre> | python|pandas|dataframe | 0 |
16,605 | 45,168,334 | Pandas apply - Re-using apply result to save time | <p>I am trying to make a new column on a subset of my data-frame that is relatively small (~600 rows) using the apply function and it works but it is slow because the apply function is computationally intensive and I cannot make this black-box function faster / less complex. </p>
<p>However, a lot of the results retur... | <p>MVCE example:</p>
<pre><code>df = pd.DataFrame({'key':np.random.randint(1,10,60000),'result':np.nan})
def factorial(x): #Black box
accum = 1
for i in range(1,x+1):
accum *= i
return accum
%timeit df['result'] = df.key.apply(lambda x: factorial(x))
</code></pre>
<p><em>10 loops, best of 3: 120... | python|pandas | 1 |
16,606 | 57,074,580 | How to use np.where between dataframes of different sizes? 'operands could not be broadcast together' | <p>I have two dataframes of different sizes.</p>
<p><code>df1</code> has addresses and no zipcodes.
<code>df2</code> has address and zipcodes.</p>
<p>I am trying to match addresses from <code>df1</code> to <code>df2</code> using <code>np.where</code>, and if there's a match, bring the corresponding zipcode over to <c... | <p>You can use a merge:</p>
<pre><code>df_new = df1.merge(df2[['address1', 'zipcode']], on='address1', how='left')
df_new = df_new.fillna('no_match')
</code></pre> | python|pandas|numpy|dataframe | 1 |
16,607 | 57,073,490 | Fastest way to compute a column of a dataframe | <p>I'm getting a pandas issue that I need help with.</p>
<p>On the one hand, I have a DataFrame that looks like the following:</p>
<pre><code> contributor_id timestamp edits upper_month lower_month
0 8 2018-01-01 1 2018-04-01 2018-02-01
1 26424341 2018-01-01 ... | <p>Use list comprehension with flattening for test membership between zipped columns converted to tuples and values in range, create <code>DataFrame</code> and <code>sum</code> in generator:</p>
<pre><code>rng = pd.date_range('2018-01-01', freq='MS', periods=12)
vals = list(zip(df['lower_month'], df['upper_month']))
... | python|pandas|dataframe | 3 |
16,608 | 56,875,250 | Plot line graph Seaborn while iterating across columns | <p>Given: </p>
<pre><code> Month = ["Jan","Feb","Mar","Apr","May","Jun"]
Apple= [500,180,1141, 1209, 600,1200]
Orange= [900,350,198,789,650,500]
Cherry = [852,415,874,404, 692,444]
list = {'Month': Month,
'Apple': Apple,
'Orange': Orange,
'Cherry': Cherry}
</code></pre>
<p>I'm trying to plot ... | <p>IIUC, you want <code>hue</code> in <code>seaborn</code>:</p>
<pre><code>df = pd.DataFrame(lst)
new_df = df.melt(id_vars='Month',
value_name='val',
var_name='type')
sns.lineplot(x='Month', y='val', hue='type', data=new_df)
</code></pre>
<p>Output:</p>
<p><a href="https://i.stac... | python|pandas|dataframe | 1 |
16,609 | 57,029,867 | Making multiple copies of a smaller matrix into a bigger matrix | <p>So suppose I have a 2by2 numpy array. I want to create another 2 by 2 numpy array so that the elements will each be the previous 2by2 array, without using an explicit for loop. How can I achieve this? The shape of the new numpy matrix should be (2,2,2,2)</p> | <p><strong>This helps you copy the numpy matrix.</strong>
But I really did not understand your point</p>
<pre><code>import numpy as np
a = np.matrix('1,2; 3,2; 3,2')
b = a.copy()
</code></pre> | python|numpy | 0 |
16,610 | 57,027,615 | pandas groupby followed by resample work differently with datetime in index and datetime in different column | <p>// The comments have made me realize that this is actually a far broader question about how the <code>on</code> keyword works in <code>.reshape</code>. I left the old question below for reference, but I think the question is much broader.</p>
<p>Here's a reproducible example; I would expect the first two statements... | <p>You need to use "apply" on a custum function and let pandas adapt itself to the output.</p>
<pre><code>def my_func(grouped):
my_sum = grouped.resample('D', on = 'DATETIME').X.sum()
return my_sum
</code></pre>
<p>Now call this function on your groupby object:</p>
<pre><code>df[df.GROUP == 'B'].groupby("GROUP")... | pandas|pandas-groupby | 0 |
16,611 | 45,915,198 | Why is order of data items reversed while creating a pandas series? | <p>I am new to python and pandas so please bear with me. I tried searching the answer everywhere but couldn't find it. Here's my question:</p>
<p>This is my input code:</p>
<pre><code>list = [1, 2, 3, 1, 2, 3]
s = pd.Series([1, 2, 3, 10, 20, 30], list)
</code></pre>
<p>The output is:</p>
<pre><code>1 1
2 2
... | <p>I think you omit <code>index</code> which specify first column called <code>index</code> - so <code>Series</code> construction now is:</p>
<pre><code>#dont use list as variable, because reversed word in python
L = [1, 2, 3, 1, 2, 3]
s = pd.Series(data=[1, 2, 3, 10, 20, 30], index=L)
print (s)
1 1
2 2
3 ... | python-3.x|pandas | 1 |
16,612 | 45,871,191 | groupby same partial string of pandas dataframe | <p>I've been using pandas to export JSON data to a csv file. Now, I've been asked to group this data and get the sum for each date grouped by <code>system</code>. Below is an example of my DataFrame.</p>
<p><strong>DataFrame:</strong></p>
<pre><code>system,totalCapacity,totalLocatedCapacity,availableCapacity,date
aad... | <p>Are you looking for</p>
<pre><code>df.groupby([df.system.str[:2], 'date']).sum().reset_index()
system date totalCapacity totalLocatedCapacity availableCapacity
0 aa 20170728 281001546 272202901 91479738
1 bb 20170728 78074304 46757718 56387268
</co... | python|pandas | 4 |
16,613 | 28,506,194 | Identify time point in DataFrame based on condition per time series | <p>I have a DataFrame with time series data, as such below:</p>
<p>(TP = time point)</p>
<pre><code>gene number TP1 TP2 TP3 TP4 TP5 TP6
gene1 0.4 0.2 0.1 0.5 0.8 1.9
gene2 0.3 0.05 0.5 0.8 1.0 1.7
....
</code></pre>
<p>For each row (gene), I want to identify the TP at w... | <p>You could first create a mask <code>ma</code> and set all the row values before the minimum to <code>False</code>. Next, use this mask find the values in each row <em>after</em> the minimum to hit 4 times the minimum (indicated by <code>True</code>):</p>
<pre><code>>>> ma = df.values.argmin(axis=1)[:,None]... | python|pandas|numpy|dataframe | 1 |
16,614 | 50,692,239 | Pandas Getting each business day of a year by using date range function | <p>I am trying to get all business day of a year by using pandas date_range function.But i am missing some necessary parameters to get my desired result.</p>
<pre><code>pd.date_range('2015-01-01', '2015-12-31', freq='D')
DatetimeIndex(['2015-01-01', '2015-01-02', '2015-01-03', '2015-01-04',
'2015-01-05... | <p>Use the <code>freq</code> <code>B</code> for business days</p>
<pre><code>pd.date_range('2015-01-01', '2015-12-31', freq='B')
DatetimeIndex(['2015-01-01', '2015-01-02', '2015-01-05', '2015-01-06',
'2015-01-07', '2015-01-08', '2015-01-09', '2015-01-12',
'2015-01-13', '2015-01-14',
... | pandas|data-science|pandas-groupby|date-range|data-science-experience | 2 |
16,615 | 50,698,208 | Python - CSV - Calculate average of column values by a column id | <p>I have a very large CSV file that I managed to order by a column id, but I cannot calculate the average column values that have that column id.</p>
<pre><code>88741,42.84286022,16.41829224,1
88797,42.78081536,16.40743455,1
88797,42.78081536,16.21153455,1
88823,42.51512511,16.43304948,2
88885,42.88204193,16.12412548... | <p>I believe need convert values to numeric first if necessary:</p>
<pre><code>df[['Lat','Long']] = df[['Lat','Long']].apply(pd.to_numeric, errors='coerce')
</code></pre>
<p>And then aggregate <code>mean</code> per groups:</p>
<pre><code>df.groupby('Cluster')['Lat','Long'].mean()
</code></pre> | python-3.x|pandas|csv | 0 |
16,616 | 50,797,803 | Changing format of date in pandas dataframe | <p>I have a pandas dataframe, in which a column is a string formatted as</p>
<pre><code>yyyymmdd
</code></pre>
<p>which should be a date. Is there an easy way to convert it to a recognizable form of date?</p>
<p>And then what python libraries should I use to handle them?
Let's say, for example, that I would like to ... | <p>Ok so you want to select Mon-Friday. Do that by converting your column to datetime and check if the <code>dt.dayofweek</code> is lower than 6 (Mon-Friday --> 0-4) </p>
<pre><code>m = pd.to_datetime(df['date']).dt.dayofweek < 5
df2 = df[m]
</code></pre>
<p>Full example:</p>
<pre><code>import pandas as pd
df ... | python|python-3.x|pandas|date | 2 |
16,617 | 50,702,033 | What is DataFrame.columns.name? | <p>Could you explain to me, what the purpose of the 'DataFrame.columns.name' attribute is? </p>
<p>I unintentionally got it after creating a pivot table and resetting the index. </p>
<pre><code>import pandas as pd
df = pd.DataFrame(['a', 'b'])
print(df.head())
# OUTPUT:
# 0
# 0 a
1 b
df.columns.name = 'temp... | <p>giving name to column levels could be useful in many ways when you manipulate your data.</p>
<p>a simple example would be when you use `stack()'</p>
<pre><code>df = pd.DataFrame([['a', 'b'], ['d', 'e']], columns=['hello', 'world'])
print(df.stack())
0 hello a
world b
1 hello d
world e
df.column... | python|pandas|dataframe | 2 |
16,618 | 50,992,946 | Cast KERAS Tensor to K.tf.int32 | <p>This is from a Custom Keras Callback
casted=K.cast((yPred), K.tf.int32)</p>
<p>I absolutely need to cast yPred, which is a Tensor, to the type int32 (The cast is applied to the Tensor content, I know that)</p>
<p>Still, K.cast allow only a conversion to float. </p>
<p>How can I solve the problem?</p> | <p>This is how you do it:</p>
<pre><code>casted = K.cast(yPred,"int32")
</code></pre> | python|tensorflow|casting|keras|tensor | 6 |
16,619 | 33,378,318 | For-loop using dictionary key reference not working | <p>I have never used Python before but have decided to start learning it by manipulating some market data. I am having trouble using the dictionary structures. In the code for <em>read_arr_price</em> below the command <strong>dict_price_recalc[price_id][year_to_index(year), Q] = float(line2)/7.5</strong> assigns <stron... | <p>this line <code>price_id[key] = np_array</code> is setting the same array to each key so every key points to the same array. you probably meant <code>price_id[key] = np_array.copy()</code></p> | python|numpy|dictionary | 2 |
16,620 | 9,074,996 | matplotlib: how to annotate point on a scatter automatically placed arrow? | <p>if I make a scatter plot with matplotlib:</p>
<pre><code>plt.scatter(randn(100),randn(100))
# set x, y lims
plt.xlim([...])
plt.ylim([...])
</code></pre>
<p>I'd like to annotate a given point <code>(x, y)</code> with an arrow pointing to it and a label. I know this can be done with <code>annotate</code>, but I'd l... | <p>Basically, no, there isn't. </p>
<p>Layout engines that handle placing map labels similar to this are surprisingly complex and beyond the scope of matplotlib. (Bounding box intersections are actually a rather poor way of deciding where to place labels. What's the point in writing a ton of code for something that ... | python|numpy|matplotlib|scipy | 44 |
16,621 | 9,280,488 | How to store numerical lookup table in Python (with labels) | <p>I have a scientific model which I am running in Python which produces a lookup table as output. That is, it produces a many-dimensional 'table' where each dimension is a parameter in the model and the value in each cell is the output of the model.</p>
<p>My question is how best to store this lookup table in Python.... | <p>Why don't you use a database? I have found <a href="http://www.mongodb.org/" rel="nofollow">MongoDB</a> (and the official Python driver, <a href="http://www.mongodb.org/display/DOCS/Python+Language+Center" rel="nofollow">Pymongo</a>) to be a wonderful tool for scientific computing. Here are some advantages:</p>
<ul... | python|numpy | 4 |
16,622 | 66,748,416 | How can I subtract a value from all values in the imported CSV file? | <p>I have read in a CSV file with one column. The column contains almost 300 rows of different values. From these values I want to subtract a certain value <code>b=0.157</code>. These new approx. 300 values should be saved in a new CSV file (array). How can I do this?</p>
<p>This is the csv - file: <strong>wearable.csv... | <p>You can use the broacasting property of <code>numpy</code> (<code>pandas</code> is based on <code>numpy</code> arrays) and simply subtract a constant value from the dataframe.</p>
<p><strong>Edit</strong>: adding the part to save to file</p>
<pre><code>import pandas as pd
import numpy as np
b = 0.157
df = pd.DataFr... | python|pandas|csv|for-loop|subtraction | 1 |
16,623 | 66,591,446 | How to suppress scientific notation in values in a pandas dataframe? | <p>I have <code>pandas.DataFrame</code> that contains some values with scientific notation and I want to change those values to a normal value without the <code>e+..</code>.</p>
<pre><code>import pandas as pd
df = pd.DataFrame(
[7.70000e+05, 4.5000000e+09, 3.219500e+05, 25000, 476577],
columns = ['Price'])
</c... | <p>You can try something like this:</p>
<pre><code>pd.set_option("float_format", lambda x: f"{x:.2f}")
df
Price
0 770000.00
1 4500000000.00
2 321950.00
3 25000.00
4 476577.00
</code></pre> | python|pandas | 1 |
16,624 | 66,414,456 | Update visibility of Traces with fig.update_layout Plotly | <p>Following on from this quesiton: <a href="https://stackoverflow.com/questions/66226542/set-sqrt-as-yaxis-scale-from-dropdown-or-button-python-plotly">Set sqrt as yaxis scale from dropdown or button-Python/Plotly</a></p>
<p>I want to :</p>
<ol>
<li>Define a plot with all traces: visible = False</li>
</ol>
<pre class=... | <p>In this case you can conditionally update the trace as shown <a href="https://plotly.com/python/creating-and-updating-figures/#conditionally-updating-traces" rel="nofollow noreferrer">here</a>.</p>
<p>First when you add each trace give it a <code>name</code> (using 'linear' and 'sqrt' in this case):</p>
<pre><code>f... | python|pandas|plotly | 2 |
16,625 | 57,522,176 | Perform calculations based on signals in array | <p>I have two columns - a 'close' column and a 'signals' column in an array. I would like to perform calculations on data in the 'close' column based on classified data that is in the 'signals' column. If the same signal appears consecutively (ignoring NANs) then do nothing, only perform a calculation when the 'signals... | <p>One way might be:</p>
<ol>
<li>Extract rows where "signals" are not null using <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.dropna.html" rel="nofollow noreferrer"><code>dropna</code></a></li>
<li>Remove consecutive duplicates using <a href="https://pandas.pydata.org/pandas-do... | python|pandas|loops|numpy|signals | 1 |
16,626 | 24,151,207 | Concatenating data in different columns into a single column (pandas, python) | <p>I am looking for the logic to concatenate the values in many columns with related data from an .xlsx file into a single column using pandas in python. The logic to combine each different column would be different depending on what information the column contains. For example:</p>
<pre><code>input:
ID,when_carpool... | <p>Join all the columns into a new one:</p>
<pre><code> df["carpool_info"] = df.apply(lambda x: "+".join([str(x[i]) for i in range(len(x))]),axis=1)
</code></pre>
<p>and then drop the other columns you don't need (see also here: <a href="https://stackoverflow.com/questions/13411544/delete-column-from-pandas-datafr... | python|excel|pandas | 2 |
16,627 | 43,654,244 | Pandas Python Data Frame: Adding a column depending on the rest | <p>I have two data frames and I want to join them using a "key" that I am going to create.
My data frames are of the form:</p>
<pre><code>Column1 Column2 Column3
1 240 31-02-16
2 350 25-03-16
3 100 31-03-16
4 500 13-02-16
</code></pre>
<p>and I want ... | <p>Simpliest is cast to <code>string</code> by <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.astype.html" rel="nofollow noreferrer"><code>astype</code></a> each column:</p>
<pre><code>df['new'] = df.Column1.astype(str) + "_" +
df.Column2.astype(str) + "_" +
df.C... | python|pandas|dataframe | 0 |
16,628 | 43,560,486 | How to change the dtype of a numpy array to 'object'? | <p>My goal is to do this:</p>
<pre><code>weights[1][0][0] = some_object(1)
</code></pre>
<p>But it throws this error:</p>
<pre><code>TypeError: float() argument must be a string or a number, not 'some_object'
</code></pre>
<p>Because of this I wawnt to change the dtype to 'object'
In my code I have weights. They lo... | <p>Let's make sure we understand what you are starting with:</p>
<pre><code>In [7]: weights
Out[7]:
[array([[-2.66665269, 0. ],
[-0.36358187, 0. ],
[ 1.55058871, 0. ],
[ 3.91364328, 0. ]]), array([[ 0.],
[ 0.]])]
In [8]: len(weights)
Out[8]: 2
In [9]: we... | python|numpy|object | 4 |
16,629 | 43,727,520 | Speed up JSON to dataframe w/ a lot of data manipulation | <p>I have a massive blob of JSON data formatted as follows:</p>
<pre><code>[
[{
"created_at": "2017-04-28T16:52:36Z",
"as_of": "2017-04-28T17:00:05Z",
"trends": [{
"url": "http://twitter.com/search?q=%23ChavezSigueCandanga",
"query": "%23ChavezSigueCandanga",
... | <p>You could speed things up by async flattening the data with <a href="https://docs.python.org/3/library/concurrent.futures.html" rel="nofollow noreferrer">concurrent.futures</a>, then loading it all into a DataFrame with <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.from_records.html... | python|json|pandas|dataframe | 3 |
16,630 | 43,747,102 | Indexing a numpy 2D matrix | <p>Suppose, I have this:</p>
<pre><code>import numpy as np
N = 5
ids = [ 1., 2., 3., 4., 5., ]
scores = [ 3.75320381, 4.32400937, 2.43537978, 3.73691774, 2.5163266, ]
ids_col = ids.copy()
scores_col = scores.copy()
students_mat = np.column_stack([ids_col, scores_col])... | <pre><code>#you need to convert Boolean list to an array to be used when selecting elements.
print(students_mat[np.asarray([False, True, False, False, False])])
[[ 2. 4.32400937]]
</code></pre> | python|numpy | 2 |
16,631 | 1,987,694 | How do I print the full NumPy array, without truncation? | <p>When I print a numpy array, I get a truncated representation, but I want the full array.</p>
<pre><code>>>> numpy.arange(10000)
array([ 0, 1, 2, ..., 9997, 9998, 9999])
>>> numpy.arange(10000).reshape(250,40)
array([[ 0, 1, 2, ..., 37, 38, 39],
[ 40, 41, 42, ..., ... | <p>Use <a href="https://numpy.org/doc/stable/reference/generated/numpy.set_printoptions.html" rel="noreferrer"><code>numpy.set_printoptions</code></a>:</p>
<pre><code>import sys
import numpy
numpy.set_printoptions(threshold=sys.maxsize)
</code></pre> | python|arrays|numpy|output-formatting | 883 |
16,632 | 72,925,872 | split arrays as string in one column of pandas to multiple columns | <p>I have data frame in the below format. The description is in string format.</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th>file</th>
<th>description</th>
</tr>
</thead>
<tbody>
<tr>
<td>x</td>
<td>[[array(['MIT', 'MIT', 'MIT', 'MIT', 'MIT'], dtype=object), array([0.71641791, 0.71641791,... | <p><strong>Update,</strong> If elements in the column as string format, you can find array with <code>regex</code> formula. <em>(Note don't use eval, <a href="https://stackoverflow.com/questions/1933451/why-should-exec-and-eval-be-avoided">Why should exec() and eval() be avoided?</a>)</em></p>
<pre><code>import ast
new... | python|pandas|string|dataframe | 1 |
16,633 | 73,123,821 | How to find the last line and the diff of each line | <p>I am trying to handle the following dataframe</p>
<pre><code>df = pd.DataFrame({'ID':[1,1,2,2,3,3,3,4,4,4,4],
'sum':[1,2,1,2,1,2,3,1,2,3,4,]})
</code></pre>
<p>Now I want to find the difference from the last row by each ID.</p>
<p>Specifically, I tried this code.</p>
<pre><code>df['diff'] = df.gro... | <p>You can use <a href="https://pandas.pydata.org/docs/reference/api/pandas.core.groupby.DataFrameGroupBy.transform.html" rel="nofollow noreferrer"><code>transform('last')</code></a> to get the last value per group:</p>
<pre><code>df['diff'] = df['sum'].sub(df.groupby('ID')['sum'].transform('last'))
</code></pre>
<p>or... | python|pandas|group-by | 1 |
16,634 | 73,002,191 | Pandas DataFrame (long) to Series ("wide") | <p>I have the following DataFrame:</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th style="text-align: left;"></th>
<th style="text-align: right;">completeness</th>
<th style="text-align: right;">homogeneity</th>
<th style="text-align: right;">label_f1_score</th>
<th style="text-align: right... | <p>IIUC, <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.unstack.html" rel="nofollow noreferrer"><code>unstack</code></a> and flatten the index:</p>
<pre><code>df2 = df.unstack()
df2.index = df2.index.map('_'.join)
</code></pre>
<p>output:</p>
<pre><code>completeness_mean 0.100000
completen... | python|pandas | 3 |
16,635 | 73,103,019 | Creating a new column sequence based on another column within a dataframe | <p>I have a dataframe <code>Outfall</code> which has a sequence in the column <code>['Head']</code>. The goal is to then make another column relative to <code>['Head']</code> called <code>['OHead']</code> which starts a new sequence once a certain value is matched. I dont have any issues applying a lamda function to cr... | <p>IIUC,</p>
<pre><code>data = Outfall[Outfall <= To].dropna().tail(1).reset_index(drop=True).at[0,'Head']
Outfall['Head'].apply(lambda x: 0 if x <= data else x - data)
</code></pre>
<p>or</p>
<pre><code>data = Outfall[Outfall <= To].dropna().tail(1).reset_index(drop=True).at[0,'Head']
Outfall['OHead'] = np.wh... | python|pandas|dataframe|lambda|list-comprehension | 0 |
16,636 | 72,899,320 | Subtract time only from two datetime columns in Pandas | <p>I am looking to do something like in <a href="https://stackoverflow.com/questions/54166156/adding-subtracting-datetime-time-columns-pandas">this thread</a>. However, I only want to subtract the <code>time</code> component of the two <code>datetime</code> columns.</p>
<p>For eg., given this dataframe:</p>
<pre><code>... | <p>Since you only want the <em>time difference</em> and you're not working with timezone-aware datetime, the date does not matter. Therefore you don't have to change any dates or set some arbitrary reference date. Just work with what you have.</p>
<p>Subtract ts1's time component from ts2 as a timedelta, then convert t... | python|pandas|datetime|timedelta | 1 |
16,637 | 70,724,327 | Finding the average and the standard deviation of three data sets | <p>I have a laser that is sent through a signal splitter. 90% of the light goes into a diffuser and that is detected by a Photomultiplier tube (PMT). The other 10% of the signal goes to a separate silicon photodiode that is used to monitor the power of the laser during diffuser testing. The diffuser is rotated through ... | <p>You can organize your data in a <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html" rel="nofollow noreferrer"><strong><code>pandas.DataFrame</code></strong></a>, where you have a column for angle values, then a column for each diode:</p>
<pre><code>columns = ['diode1', 'diode2', 'diode3']
di... | python|pandas|dataframe|matplotlib|data-visualization | 1 |
16,638 | 70,514,408 | Pandas: convert two times to datetime, then take the difference and display back as only times | <p>I have the following toy Pandas dataframe named <code>df</code>:</p>
<pre><code>df = pd.DataFrame({'begin' : ['08:00', '10:00', '14:00'],
'end' : ['14:00', '17:00', '22:00']})
begin end
08:00 14:00
10:00 17:00
14:00 22:00
</code></pre>
<p>I would like to calculate... | <p>Coerce the time to datetime, substract and convert outcome to hours</p>
<pre><code> df['diff_hours']=(pd.to_datetime(df['end'], format="%H:%M")-pd.to_datetime(df['begin'], format="%H:%M")).astype('timedelta64[m]')/60
begin end diff_hours
0 08:00 14:00 6.0
1 10:00 17:00 ... | python|pandas | 3 |
16,639 | 70,626,231 | How to calculate mean/variance/standard deviation per index of array? | <p>I have some data like [[0, 1, 2], [0.5, 1.5, 2.5], [0.3, 1.3, 2.3]].</p>
<p>I am using numpy and python and I wish to calculate the mean and standard deviation for my data, per index. So I wish to calculate the mean/std for (0, 0.5, 0.3) (e.g. index 0 of each subarray), (1, 1.5, 1.3) (e.g. index 1 of each subarray),... | <p>The various statistics functions all take an <code>axis</code> argument that will allow you to calculate the statistic over a column:</p>
<pre><code>import numpy as np
a = np.array([[0, 1, 2], [0.5, 1.5, 2.5], [0.3, 1.3, 2.3]])
np.mean(a, axis=0)
# array([0.26666667, 1.26666667, 2.26666667])
np.std(a, axis=0)
# a... | python|arrays|numpy|statistics|mean | 1 |
16,640 | 70,476,585 | Why optimize in the einsum can accelerate binary contraction? | <p>In <a href="https://numpy.org/doc/stable/reference/generated/numpy.einsum.html" rel="nofollow noreferrer">https://numpy.org/doc/stable/reference/generated/numpy.einsum.html</a></p>
<blockquote>
<p>optimize{False, True, ‘greedy’, ‘optimal’}, optional
Controls if intermediate optimization should occur. No optimization... | <p>My timings:</p>
<pre><code>In [26]: A = np.random.random((90,80))
In [27]: B = np.random.random((80,81,82))
In [28]: timeit np.einsum('ab,bcd->acd',A,B,optimize=False)
39.2 ms ± 1.51 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
In [29]: timeit np.einsum('ab,bcd->acd',A,B,optimize=True)
9.06 ms ± 70.... | python|numpy|numpy-einsum | 3 |
16,641 | 42,691,991 | Python - Pandas: How to add a series to each row in dataframe | <p>I have this dataframe.</p>
<pre><code>>>> print(df)
a b c d e
0 z z z z z
1 z z z z y
2 z z z x y
3 z z w x y
4 z v w x y
</code></pre>
<p>I also have a series.</p>
<pre><code>>>> print(map_class)
class
0 -1
1 0
2 1
3 2
4 3
5 ... | <p>Here's an approach making use of NumPy for creating the output data and specifically in it, using <a href="https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#advanced-indexing" rel="nofollow noreferrer"><code>NumPy's advanced-indexing</code></a> and finally constructing a dataframe from that output data... | python-3.x|pandas|numpy | 2 |
16,642 | 27,134,330 | python/numpy - How to use einsum in the following example? | <p>I have the following:</p>
<p>sum_XY C_x I_xk Cy I_yl P_xy</p>
<p>currently my code looks like this:</p>
<pre><code># initialise dummy values
Nk = Nl = 100
NX = Ny = 10
Ix = np.random.rand(Nx, Nk)
Iy = np.random.rand(Ny, Nl)
C = np.random.rand(Nk)
Pin = np.ones(Nx*Ny)
# point 1
Fx = (Ix * C[np.newaxis, :Nk]).T # ... | <p>Starting with <code>point2</code>, this matches your calculation.</p>
<pre><code>H2 = np.einsum('kx,ly->klxy', Fx, Fy)
out2 = np.einsum('klxy,xy->kl', H2, np.ones((Nx,Ny)))
print np.allclose(out, out2)
</code></pre>
<p>I tried to choose einsum indexes to match your shape parameters. I tested it with</p>
<p... | python|numpy | 0 |
16,643 | 27,030,424 | How can I make a one-dimensional array into a two-dimensional array using numpy? | <p>I have data that looks like this: </p>
<pre><code>>>>npfilled[:5]
array([('!', 0, 0, 3, 10, 0, 2, 4, 4), ('!"', 0, 0, 0, 5, 0, 0, 0, 0),
('"', 23, 13, 20, 32, 0, 0, 22, 9),
("'", 21, 8, 23, 12, 5, 10, 0, 7), ('(', 3, 2, 2, 3, 0, 0, 0, 0)],
dtype=[('token', '<U64'), ('mel_freq1', '&... | <p>In numpy terms, you're asking how to convert a structured array into a "normal" 2D array, where each item in the structure is along a new axis.</p>
<p>On a quick side note, for heterogeneous data such as this, <code>pandas</code> is probably more what you're looking for. </p>
<p>That having been said, here's a qu... | python|arrays|numpy | 2 |
16,644 | 27,384,489 | AttributeError: 'NoneType' object has no attribute 'ravel' | <p>Can someone please tell me what is wrong with this code? I keep on getting a <code>NoneType</code> error. I am trying to create a histogram. </p>
<pre><code>import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('C:\Pictures\naturalScene.bmp',0)
plt.hist(img.ravel(),256,[0,256]);
plt.s... | <p>From the <a href="http://docs.opencv.org/modules/highgui/doc/reading_and_writing_images_and_video.html" rel="nofollow">docs</a>:</p>
<blockquote>
<p>The function imread loads an image from the specified file and returns it. If the image cannot be read (because of missing file, improper permissions, unsupported or... | python|opencv|numpy|matplotlib|image-compression | 4 |
16,645 | 14,751,165 | Using MultiIndex on DataFrame | <p>This is follow-up question to the answer for this question:</p>
<p><a href="https://stackoverflow.com/questions/14737566/pandas-performance-issue-need-help-to-optimize/14750813#14750813">pandas performance issue - need help to optimize</a></p>
<p>The following suggestion works:</p>
<pre><code>df = DataFrame(np.a... | <p>To show it works for me (pandas 0.10.1):</p>
<pre><code>In [1]: from StringIO import StringIO
In [2]: import numpy as np
In [3]: import pandas as pd
In [4]: s = StringIO("""col1,col2,year,amount
...: 111111,3.5,2012,700
...: 111112,3.5,2011,600
...: 222221,4.0,2012,222""")
In [5]: sd=pd.read_csv(s)
... | python|pandas | 1 |
16,646 | 38,970,423 | netCDF convert to NaN for conditional | <p>I want to convert values from a netCDF file into NaN called <code>LandMask_NaN</code> when they are greater than zero. However, there seems to be a type mismatch between <code>LandMask</code> and what numpy will convert to NaNs. Any help much appreciated, code and info below:</p>
<pre><code> import netCDF4 as nc
i... | <p>It'll be helpful if you share the netcdf file, but here are a few ideas of what's going on:</p>
<p>Variables are not currently being read-in as numpy arrays. You need to add indexing parameters to cast them to arrays. Without the file, I'm not sure what they are, but surely some are multi-dimensional. For example:... | python|numpy|nan|netcdf4 | 1 |
16,647 | 39,298,783 | Using only part of comma delimited files | <p>I know that in Python, if you have a comma-delimited file that reads something like</p>
<pre><code>1,5
2,4
3,3
4,2
5,1
</code></pre>
<p>you can do something similar to the following:</p>
<pre><code>import numpy as np
x, y = np.loadtxt('example.txt', delimiter=',', unpack=True)
plt.plot(x,y, label='myLine')
</cod... | <p>You can use <a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.loadtxt.html" rel="nofollow"><code>np.loadtxt</code></a> with <code>usecols=(1, 2)</code>:</p>
<blockquote>
<p>usecols : sequence, optional - Which columns to read, with 0 being the first. For example, usecols = (1,4,5) will extract th... | python|csv|numpy | 0 |
16,648 | 23,753,476 | Fill non-consecutive missings with consecutive numbers | <p>For a given data frame...</p>
<pre><code>data = pd.DataFrame([[1., 6.5], [1., np.nan],[5, 3], [6.5, 3.], [2, np.nan]])
</code></pre>
<p>that looks like this...</p>
<pre><code> 0 1
0 1.0 6.5
1 1.0 NaN
2 5.0 3.0
3 6.5 3.0
4 2.0 NaN
</code></pre>
<p>...I want to create a third... | <p>You can do it this way, I took the liberty of renaming the columns to avoid the confusion of what I am selecting, you can do the same with your dataframe using:</p>
<pre><code>data = data.rename(columns={0:'a',1:'b'})
In [41]:
data.merge(pd.DataFrame({'c':range(1,len(data[data.b.isnull()]) + 1)}, index=data[data.... | python|pandas | 1 |
16,649 | 23,917,144 | Python Pandas DateOffset using value from another column | <p>I was thinking this would be very easy but the below is not working for what I want. Just trying to compute a new date column by adding days to a pre-existing datetime column using values from another column. My 'offset' column below just has 1 digit numbers.</p>
<pre><code>df['new_date'] = df['orig_date'].apply(la... | <pre><code>In [12]: df['C'] = df['A'] + df['B'].apply(pd.offsets.Day)
In [13]: df
Out[13]:
A B C
0 2013-01-01 0 2013-01-01
1 2013-01-02 1 2013-01-03
2 2013-01-03 2 2013-01-05
3 2013-01-04 3 2013-01-07
4 2013-01-05 4 2013-01-09
</code></pre> | python|pandas|time-series | 10 |
16,650 | 22,833,404 | How do I plot hatched bars using pandas? | <p>I am trying to achieve differentiation by hatch pattern instead of by (just) colour. How do I do it using pandas?</p>
<p>It's possible in matplotlib, by passing the <code>hatch</code> optional argument as discussed <a href="https://stackoverflow.com/questions/14279344/how-can-i-add-textures-to-my-bars-and-wedges">h... | <p>This is kind of hacky but it works:</p>
<pre><code>df = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])
ax = plt.figure(figsize=(10, 6)).add_subplot(111)
df.plot(ax=ax, kind='bar', legend=False)
bars = ax.patches
hatches = ''.join(h*len(df) for h in 'x/O.')
for bar, hatch in zip(bars, hatches):
... | python|matplotlib|plot|pandas | 26 |
16,651 | 22,840,900 | Hard time finding Python-Numpy deg2rad function | <p>Title says it all, I somehow can not find that function. Obviously it's inside the Numpy package (numpy.core.umath.deg2rad) and I've tried importing it but to no avail. Anyone care to chime in?</p>
<ul>
<li>import numpy as np - np.deg2rad doesn't even show up</li>
<li>from numpy import* - umath.deg2rad shows up, bu... | <pre><code>from numpy.core.umath import deg2rad
# then
deg2rad(...)
</code></pre>
<p>Or</p>
<pre><code>import numpy as np
np.core.umath.deg2rad(...)
</code></pre> | python|numpy|vector|scipy|degrees | 2 |
16,652 | 15,125,407 | dealing with zero log values in numpy/pandas | <p>I have a dataframe in pandas that stores a column containing ratios. The ratios need to be transformed into a <code>log2</code> scale for plotting but the ratio values are often 0, leading in <code>log2(0)</code> which is recorded as <code>inf</code> or a missing value in pandas. I want to visualize these since in m... | <p>I guess you can use <code>numpy.inf</code> to identify those that are <code>infinity</code> and treat them separately.</p>
<p>Ref: <a href="https://github.com/pydata/pandas/issues/2858" rel="nofollow">github.com/pydata/pandas</a></p> | python|numpy|pandas | 3 |
16,653 | 14,954,354 | numpy correlation coefficient | <p>1) How can I find correlation for the following data set using python code?</p>
<pre><code>T = [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
P = [ 3480. 7080. 10440. 13200. 16800. 20400. 23880. 27480. 30840. 38040. 41520. 44880. 48480. 52080. 55680. 59280. 62520. 66120. 67580. 69620. 69621.]
</code></pre>
<p>2) **i... | <p>To calculate correlations between two series of data, i use <code>scipy.stats</code>. I would recommend you to investigate this package.</p>
<p>From the <a href="http://docs.scipy.org/doc/scipy/reference/stats.html" rel="nofollow">docs</a>:</p>
<pre><code>pearsonr(x, y) #Pearson correlation coefficient and the p-v... | python|numpy | 3 |
16,654 | 29,356,412 | Efficiently re-indexing one level with "forward-fill" in a multi-index dataframe | <p>Consider the following DataFrame:</p>
<pre><code> value
item_uid created_at
0S0099v8iI 2015-03-25 10652.79
0F01ddgkRa 2015-03-25 1414.71
0F02BZeTr6 2015-03-20 51505.22
2015-03-23 51837.97
2015-03-24 51578.63
2015-03-25 NaN
... | <p>You have a couple of options, the easiest IMO is to simply unstack the first level and then ffill. I think this make it much clearer about what's going on than a groupby/resample solution (I suspect it will also be faster, depending on the data):</p>
<pre><code>In [11]: df1['value'].unstack(0)
Out[11]:
item_uid ... | python|pandas | 6 |
16,655 | 29,777,702 | Aggregate all dataframe row pair combinations using pandas | <p>I use python pandas to perform grouping and aggregation across data frames, but I would like to now perform specific pairwise aggregation of rows (n choose 2, statistical combination). Here is the example data, where I would like to look at all pairs of genes in [mygenes]:</p>
<pre><code>import pandas
import itert... | <p>I can't think of a clever vectorized way to do this, but unless performance is a real bottleneck I tend to use the simplest thing which makes sense. In this case, I might <code>set_index("Gene")</code> and then use <code>loc</code> to pick out the rows:</p>
<pre><code>>>> df = df.set_index("Gene")
>>... | python|pandas|aggregate|combinations|itertools | 11 |
16,656 | 29,628,410 | How do I multiply each element in an array with a large number without getting OverflowError: Python int too large to convert to C long? | <p>I'm writing a program where I want to multiply each number in an array (from numpy) with a big number (<code>1.692287392646066e+41</code>) and I do it like this:</p>
<pre><code>x = array([ 16, 18, 19, 15, 27, 26, 13, 34, 38, 36, 43, 42, 48,
50, 55, 57, 70, 67, 65, 85, 99, 94, 90, 112, 126, ... | <p>It makes no sense to make the multiplication in <code>python</code> and not in <code>numpy</code>. Not only is <code>numpy</code> faster, but it also works better.</p>
<p>What you are trying to do creates a <code>python list</code>, which it then tries to copy into the <code>numpy array</code> element wise.</p>
<p... | python|arrays|for-loop|numpy | 3 |
16,657 | 29,797,012 | pandas: How to Series.sort_index() in-place? | <p>Is it possible to sort_index a pandas' Series in-place?</p>
<p>Well, of course, you can do this:</p>
<pre><code>ser = ser.sort_index()
</code></pre>
<p>What I'm actually trying to do is to sort_index a Series-derived class, like this:</p>
<pre><code>class SeriesAutoSorted(Series):
def append(self, index, va... | <p>There's an undocumented <code>Series._update_inplace()</code> method that you could use. Pandas uses this <a href="https://github.com/pydata/pandas/blob/69087192747ea670e1e781b25371f40ffec31073/pandas/core/series.py#L1784" rel="nofollow">internally</a></p>
<p>Here's an example:</p>
<pre><code>In [50]: ser = pd.Ser... | python|pandas | 2 |
16,658 | 62,107,071 | How do I show pie graph in 2 row and 2 column in Python | <p>I have a problem about showing pie graphs in 2 row and 2 column. They are listed in one column.</p>
<p>How can I fix the issue?</p>
<p>Here is my code snippets shown below.</p>
<pre><code>plt.figure(figsize = (8,8))
ax1 = plt.subplot(2,2,1)
coursera_df_beginner["course_Certificate_type"].value_counts().plot(kind=... | <p>Here is my answer</p>
<pre><code>f,a = plt.subplots(2,2,figsize=(8,8))
f.subplots_adjust(wspace = .8)
coursera_df_beginner["course_Certificate_type"].value_counts().plot(kind='pie',
shadow=True,
... | python|pandas|pie-chart | 0 |
16,659 | 62,425,222 | Return data indices for all bins with counts greater than threshold | <p>I am trying to find the indices all within a certain bin of the data binned liked this: </p>
<pre><code>import numpy as np
x=np.random.random(1000)
y=np.random.random(1000)
#The bins are not evenly spaced and not the same number in x and y.
xedges=np.array(0.1,0.2, 0.4, 0.5, 0.55, 0.6, 0.8, 0.9)
yedges=np.arange(... | <p>If I understand correctly, you want to build a mask on the original points indicating that the point belongs to a bin with more than 5 points.</p>
<p>To construct such a mask, <code>np.histogram2d</code> returns the counts for each bin, but does not indicate which point goes into which bin.</p>
<p>You can construc... | python|numpy|scipy|histogram|binning | 1 |
16,660 | 62,063,777 | Creating pandas DataFrame through class returns empty DataFrame | <p>The purpose of this script is to produce a data frame that is generated through code written in object oriented style.</p>
<p>The problem is the outcome of this script is an empty data frame.</p>
<p>There is no error.</p>
<p>Here is the code:</p>
<pre><code>import pandas as pd
class Dataframe:
def __init__... | <p>You need to call the methods you have created to populate the DataFrame.</p>
<pre><code>import pandas as pd
class Dataframe:
def __init__(self):
self.df = pd.DataFrame()
self.name()
self.age()
self.sex()
self.address()
def name(self):
self.df['name'] = ["Ha... | python|pandas|oop | 3 |
16,661 | 51,523,380 | Python 3.6.5 returns '<' not supported between instances of 'tuple' and 'str' error message | <p>I'm trying to split a data set into a training and testing part. I am struggling at a structural problem as it seems as the hierarchy of the data seems to be wrong to proceed with below code.</p>
<p>I tried the following:</p>
<pre><code>import pandas as pd
data = pd.DataFrame(web.DataReader('SPY', data_source='mor... | <p>As <code>data.head()</code> will reveal, <code>data</code> is not actually a <code>pd.DataFrame</code> but a <code>pd.Series</code> whose index is a <code>pd.MultiIndex</code> (as suggested also by the error which hints that each element is a tuple) rather than a <code>pd.DatetimeIndex</code>.</p>
<p>What you could... | python|pandas|time-series|tuples|pandas-datareader | 2 |
16,662 | 51,537,166 | How to plot column from a dataframe | <p>I am trying to plot integer columns of dataframe. I am trying by following way</p>
<pre><code>for i in df:
if df[i].dtypes == 'int64':
df[i].plot.kde()
</code></pre>
<p>But it is plotting all in the same graph. I am new to it and would like to know how can I do it?</p> | <p>Just try to add plot option in your loop:</p>
<pre><code>for i in df:
if df[i].dtypes == 'int64':
df[i].plot.kde()
plt.show()
</code></pre> | pandas|dataframe|matplotlib|data-visualization | 1 |
16,663 | 48,510,555 | Pandas: Flag column if value in list exists anywhere in row | <p>Ultimately, I want to flag a new column, 'ExclusionFlag', with 0 or 1 depending on whether a value that is found in a list exists anywhere in the row. </p>
<pre><code>df = pd.DataFrame([['cat','b','c','c'],['a','b','c','c'],['a','b','c','dog'],['dog','b','c','c']],columns=['Code1','Code2','Code3','Code4'])
excluded... | <p>Something like</p>
<pre><code>df['ExclusionFlag'] = df.isin(excluded_codes).any(1).astype(int)
Code1 Code2 Code3 Code4 ExclusionFlag
0 cat b c c 1
1 a b c c 0
2 a b c dog 1
3 dog b c c 1
</code></pre> | python|pandas|conditional | 10 |
16,664 | 48,547,617 | Sum for a large dataframe? | <p>I have a large SparseDataFrame, approximately 12000 rows x 16000 columns. I want to calculate sum of rows grouped by a column:</p>
<p>Input:</p>
<pre><code>+-------+------+------+------+
| | Col1 | Col2 | Col3 |
+-------+------+------+------+
| row 1 | Foo | 1 | 0 |
| row 2 | Foo | 3 | 1 |
| ro... | <p>This should be fast than <code>groupby</code></p>
<pre><code>df.set_index('Col1').sum(level=0)
Out[294]:
Col2 Col3
Col1
Foo 4 1
Bar 5 3
</code></pre> | python|pandas | 2 |
16,665 | 48,517,405 | Chaining multiple combine_first() | <p>What would be a better way to chain multiple combine_first() statements.
i.e.</p>
<p>I have parsed some data, and have 3 different columns for cc-email. This works, but is there a cleaner way of doing it?</p>
<pre><code>df['cc-email2'] = df['cc-email'].combine_first(
df['cc-email_cc-email'].combine_first(
df['cc-... | <p>I think you can use <code>reduce</code>:</p>
<pre><code>from functools import reduce
dfs = [df['cc-email'], df['cc-email_cc-email'], df['cc-emails_cc-email']]
df['cc-email2'] = reduce(lambda l,r: l.combine_first(r), dfs)
</code></pre>
<p>But it seems <code>ffill</code> with select last column should working too:... | python|pandas | 3 |
16,666 | 70,756,617 | Keras GradientType: Calculating gradients with respect to the output node | <p>For startes: this question does not ask for help regarding reinforcement learning (RL), RL is only used as an example.</p>
<p>The Keras documentation contains an example <a href="https://keras.io/examples/rl/actor_critic_cartpole/" rel="nofollow noreferrer">actor-critic reinforcement learning implementation</a> usin... | <p>Well, after some research I found the answer myself: It is possible to extract the trainable variables of a given layer based on the layer name. Then we can apply <code>tape.gradient</code> and <code>optimizer.apply_gradients</code> to the extracted set of trainable variables. My current solution is pretty slow, but... | python|tensorflow|keras|reinforcement-learning|gradienttape | 0 |
16,667 | 70,980,642 | Python Tensorflow Dataset Filter Set .issubset() | <p>I have a tensorflow dataset:</p>
<pre><code>def fake_sequence():
seq = [np.random.choice(["A", "B", "C", "D"]) for _ in range(100)]
mutate = [np.random.choice(["E", "F", "G", "H"]) for _ in range(100)]
mask = np.random.choice... | <p>You could use a <code>tf.lookup.StaticHashTable</code> and <code>tf.cond</code> to solve what you want:</p>
<pre class="lang-py prettyprint-override"><code>import tensorflow as tf
import numpy as np
def fake_sequence():
seq = [np.random.choice(["A", "B", "C", "D"]) for _ ... | python|tensorflow|set|tensorflow-datasets | 1 |
16,668 | 70,929,624 | Pandas - get first occurrence of a given column value | <p>I have a <code>df</code> with repeated <code>names</code> for different rounds of a tournament, like so:</p>
<pre><code>name round_id price_open
John 1 5.0
Paul 1 4.0
John 2 5.4
Paul 2 3.4
John 3 5.0
Paul 3 4.0
</code></pre>
<p>But at round 3, a new playe... | <p>Use <code>drop_duplicates</code> to keep the first instance of each name:</p>
<pre><code>>>> df.drop_duplicates('name')
name round_id price_open
0 John 1 5.0
1 Paul 1 4.0
6 George 3 6.0
</code></pre> | python|pandas|dataframe | 3 |
16,669 | 70,785,103 | Pandas MultiIndex dataframe to nested json | <p>I have the following pandas multi-index dataframe and I would like it to become a nested json object.</p>
<pre><code>import pandas as pd
data = {'store_id' : ['1', '1','1','2','2'],
'item_name' : ['apples', 'oranges', 'pears', 'persimmons', 'bananas'],
'2022-01-01': [2.33, 1.99, 2.33, 2.33, 4.21],
... | <p>Use nested list comprehension for add custom format of inner dicts:</p>
<pre><code>import json
d = {level: {k1: [{'date': k, 'price': v}
for k, v in v1.items()]
for k1, v1 in df.xs(level).T.items()}
for level in df.index.levels[0]}
j = json.dumps(d)
print (j)
</code></pre>... | python|pandas|dataframe|multi-index|nested-json | 1 |
16,670 | 51,970,251 | Tensorflow: get predictions | <p>I try to get predictions and learning network.</p>
<p>This is parameters of my network</p>
<pre><code>X = tf.placeholder(tf.float32, shape=(None, X_train.shape[1]), name="input")
y = tf.placeholder(tf.float32, shape=(None, y_train.shape[1]), name="y")
y_cls = tf.argmax(y, axis=1)
weights = tf.Variable(tf.truncate... | <p>There are multiple issues with your code and I think the error you posted here is one of the least significant.</p>
<p>Lets go through your code and let me comment on some things. I hope this will be more helpful than just fixing the single <code>ValueError</code>.</p>
<p>You start with the definition of two place... | python|tensorflow | 1 |
16,671 | 51,842,534 | How do I sum unique values per column in Python? | <p>I am working with weblogs and have data containing account_id and session_id. Multiple sessions can be associated with one account. I want to create a new dataframe containing account_id and count the number of unique sessions associated with that account. My df looks like this:</p>
<pre><code>account_id session_id... | <pre><code>df = pd.DataFrame({'session': ['de322', 'de322', 'de322', 'de323', 'de323', 'ge012', 'ge012', 'ge013', 'ge333'],
'user_id': [1111, 1111, 1111, 1111, 1111, 210, 210, 210, 211],
})
print(df)
df = df.drop_duplicates().groupby('user_id').count()
print(df)
</code></pre>
<p... | python|pandas|pandas-groupby | 2 |
16,672 | 51,966,498 | Pandas: Replace values of multiple columns using boolean masks | <p><strong>Question</strong>: Given DataFrame <code>b</code>, how can I replace the values of multiple columns, with one value, through boolean mask column identification?</p>
<p><strong>What works, but I don't want:</strong></p>
<pre><code>b.iloc[:, 2:6] = "someConstantValue"
</code></pre>
<p><strong>What doesn't w... | <p>You can use <code>iloc</code> with Boolean indexing, but be careful. It works with Boolean <strong>arrays</strong>, not Boolean series. For example:</p>
<pre><code>b.iloc[(b['A'] == 'a').values, 2:6] = 'someConstantValue'
</code></pre>
<p>As an aside, chained indexing is <a href="https://pandas.pydata.org/pandas-d... | python|pandas|dataframe|indexing | 3 |
16,673 | 42,126,754 | pandas. How to update a new pandas column row by row | <p>I am trying to add a new column in a pandas data frame, then update the value of the column row by row:</p>
<pre><code> my_df['col_A'] = 0
for index, row in my_df.iterrows():
my_df.loc[index]['col_A'] = 100 # value here changes in real case
print(my_df.loc[index]['col_A'])
my_df
</code>... | <p>you are assigning to a slice in this line <code>my_df.loc[index]['col_A'] = 100</code></p>
<p>Instead do</p>
<pre><code>my_df['col_A'] = 0
for index, row in my_df.iterrows():
my_df.loc[index, 'col_A'] = 100 # value here changes in real case
print(my_df.loc[index]['col_A'])
</code></pre> | python-3.x|pandas | 5 |
16,674 | 42,070,410 | Match value in pandas cell where value is array using np.where (ValueError: Arrays were different lengths) | <p>many thanks for your reading.</p>
<p>(Prior consideration: I cannot change the format of the data inside the dataframes; I'm stuck with what I have. The following is a simplified and reduced version of my data and problem)</p>
<p>I have a dataframe with the following form:</p>
<pre><code>df = pd.DataFrame(
{'Mach... | <p>You need <code>apply</code> with <code>==</code> for check values in <code>list</code>:</p>
<pre><code>df['TF'] = np.where(df['Machine'].apply(lambda x: ['No Match'] == x),True, False)
print (df)
Machine TF
0 [red, blue] False
1 [red] False
2 [blue] False
3 [No Match] True
</code></p... | python|arrays|pandas|numpy|dataframe | 2 |
16,675 | 41,766,648 | Efficiently sorting and grouping really big arrays | <p>I am sorting an array of data based on the angle each point within the data forms with the other points. For my given <code>data</code> (x,y,z), i calculate the pairwise distance (<code>pwdist</code>), the pairwise value (<code>pwresi</code>) and the angle between pair data point (<code>pwang</code>). Once i get thi... | <p>To be honest, your code messy, and your question is not fully understandable.</p>
<p>So my answer is theoretical, and you should apply it to your own case:</p>
<p>Given, a list:
<code>myList = [element1, element2, element3]</code></p>
<p>And known, an evaluation function: <code>def eval(a): return angle(a.x, a.y)... | python|performance|python-3.x|sorting|numpy | 0 |
16,676 | 41,916,275 | Grouping dataframe by each 6 hour and generating a new column | <p>I have this dataframe (type could be 1 or 2):</p>
<pre><code>user_id | timestamp | type
1 | 2015-5-5 12:30 | 1
1 | 2015-5-5 14:00 | 2
1 | 2015-5-5 15:00 | 1
</code></pre>
<p>I want to group my data by six hours and when doing this I want to keep <code>type</code> as:</p>
<ul>
<li><code>1</c... | <p>Try this:</p>
<pre><code>In [54]: df.groupby(['user_id', pd.Grouper(key='timestamp', freq='6H')]) \
.agg({'type':lambda x: x.unique().sum()})
Out[54]:
type
user_id timestamp
1 2015-05-05 12:00:00 3
</code></pre>
<p>PS it'll work only with given types: (<code>1</cod... | python|pandas|dataframe|group-by | 2 |
16,677 | 64,606,752 | Android: mobilenet_v1_1.0_224.tflite model doesn't return bounding box information | <p>I am using <a href="https://github.com/anupamchugh/AndroidTfLiteCameraX" rel="nofollow noreferrer">https://github.com/anupamchugh/AndroidTfLiteCameraX</a> source code to learn about integrating the TFLite model with Android. I have labels.txt with all the classes in the <code>Assets</code> folder as well.</p>
<p>Cur... | <p>The source code and model you linked is for <a href="https://medium.com/analytics-vidhya/image-classification-vs-object-detection-vs-image-segmentation-f36db85fe81" rel="nofollow noreferrer">Image Classification not Object Detection</a>. So there are no bounding boxes coordinatioon / information.</p>
<p>Here is my e... | android|kotlin|classification|bounding-box|tensorflow-lite | 1 |
16,678 | 64,596,968 | Time data does not match format '%yyyy-%mm-%dd | <p>I have the column "date of registration" in the below format.</p>
<pre><code>array(['01JAN2018:00:00:00.000000000', '01JAN2019:00:00:00.000000000',
'01JAN2020:00:00:00.000000000', ...,
'09FEB2018:00:00:00.000000000', '09FEB2019:00:00:00.000000000',
'09FEB2020:00:00:00.000000000'], dtype=object)
</... | <p>You can use <code>format='%d%b%Y:%H:%M:%S.%f'</code>:</p>
<pre><code>pd.to_datetime(['01JAN2018:00:00:00.000000000', '01JAN2019:00:00:00.000000000',
'01JAN2020:00:00:00.000000000',
'09FEB2018:00:00:00.000000000', '09FEB2019:00:00:00.000000000',
'09FEB2020:00:00:00.000000000'], format='%d%b%Y:%H:%M:%S.%f')
... | pandas|datetime | 1 |
16,679 | 64,176,047 | How can i apply onehotencoder to one column of an array? | <p>I've been following a tutorial trying to understand machine learning while trying out what he's doing at the same time.</p>
<p>My array is:</p>
<pre><code>0 44 72000
2 27 48000
1 30 54000
2 38 61000
1 40 ... | <p>Assuming that your data X has a shape (n_rows, features).
If you like to apply one-hot encoding to say, the first column. A quick approach would be</p>
<pre><code>onehotencoder = OneHotEncoder()
one_hot = onehotencoder.fit_transform(X[:,0:1]).toarray()
</code></pre>
<p>A better approach to apply one-hot encoding onl... | python|numpy|machine-learning|scikit-learn | 1 |
16,680 | 64,546,542 | Cloud SQL - Postgres - Very slow insert | <p>I may be used to Big Data technology, but when I try to insert ~300k rows for a total (as csv) of 30Mb I don't think 15min is an acceptable time to spend on INSERT with Postgres.</p>
<p>I first understand that increasing the total disk size on GCP also increase the IOPS, so I up the disk from 20Go to 400Go.</p>
<p>T... | <p>According to the Cloud SQL <a href="https://cloud.google.com/sql/docs/postgres/best-practices#import-export" rel="nofollow noreferrer">best practices</a>, to speed up imports (for small instance sizes):</p>
<blockquote>
<p>you can temporarily <a href="https://cloud.google.com/sql/docs/postgres/edit-instance" rel="no... | python|pandas|postgresql|google-cloud-sql | 0 |
16,681 | 64,292,702 | Pandas convert partial column Index to Datetime | <p>DataFrame below contains housing price dataset from 1996 to 2016.</p>
<p>Other than the first 6 columns, other columns need to be converted to <code>Datetime</code> type.</p>
<p><a href="https://i.stack.imgur.com/HlTVv.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/HlTVv.png" alt="DataFrame's col... | <p>The <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.html" rel="nofollow noreferrer">pandas index</a> is immutable, so you can't do that.</p>
<p>However, you can access and modify the column index with <code>array</code>, see doc <a href="https://pandas.pydata.org/pandas-docs/stable/r... | python|pandas|dataframe|indexing | 1 |
16,682 | 64,247,027 | create multiple dataframes from unique values of a cloumn | <p>I have dataframe with columns(issue_id,summary, source_id). The source_id has values ranging from 1 to 3.
I want to create multiple dataframes having name df_1, df_2,df_3 as per the values in source_id.</p>
<p>I tried groupby and it gave a dict. but converting dict to dataframe is giving only 1 dataframe.</p>
<p>da... | <p>The simple way is delete other columns and assign different name. @krishna</p> | python|pandas|dataframe | 0 |
16,683 | 47,753,003 | How to select ndarray data in python? | <p>I have a <code>ndarray</code> type array, for example:</p>
<pre><code>1, 2, 0.5
2, 6, 0.9
9, 2, 0.83
</code></pre>
<p>I want to keep the rows whose 3rd elements are larger than <code>0.8</code>, and discard the other rows. It means I want this result:</p>
<pre><code>2, 6, 0.9
9, 2, 0.83
</code></pre>
<p>How can ... | <p>Here is a simple implementation of your problem:</p>
<pre><code>import numpy as np
data = np.array([[1, 2, 0.5],[2, 6, 0.9],[9, 2, 0.83]])
result =data[data[:,2]>0.8]
</code></pre>
<p>Output:</p>
<pre><code>[[ 2. 6. 0.9 ]
[ 9. 2. 0.83]]
</code></pre> | python-3.x|numpy | 2 |
16,684 | 47,746,450 | Read date from tab delimited text file | <p>I only recently switched to Python so this question probably has a really simple solution but I can't seem to find it. I have a text file in the following format:</p>
<pre><code>08-05-90 0:00:00 1.78 7.1 10
08-05-90 3:00:00 2.01 7.4 11.1
08-05-90 6:00:00 1.74 7 10.5
08-05-90 9:00:... | <p>Hopefully you are OK using <a href="https://stackoverflow.com/questions/11077023/what-are-the-differences-between-pandas-and-numpyscipy-in-python">pandas</a> in addition to numpy. If so, the datetime of the combined columns is as easy as:</p>
<h3>Code:</h3>
<pre><code>df['datetime'] = pd.to_datetime(df.date + ' '... | python|arrays|numpy|datetime | 0 |
16,685 | 58,774,526 | TensorFlow custom C++ op with resource handle | <p>Python code:</p>
<pre><code>
import os
import sys
from subprocess import check_call
import tensorflow as tf
CC_NAME = "tf-resource-op.cc"
SO_NAME = "tf-resource-op.so"
def compile_so():
use_cxx11_abi = hasattr(tf, 'CXX11_ABI_FLAG') and tf.CXX11_ABI_FLAG
common_opts = ["-shared", "-O2"]
common_opts += ["-s... | <p>Adding <code>-DNDEBUG</code> to the build flags fixes the issue.
This workaround is explained <a href="https://github.com/tensorflow/tensorflow/issues/17316" rel="nofollow noreferrer">in TF issue 17316</a>.</p> | python|c++|tensorflow | 1 |
16,686 | 58,925,771 | pandas aggregate sum of two columns and make it as one column | <p>I am looking for solution to group by and then find the sum of two columns in pandas dataframe and display as one column.</p>
<p>Sum of Net and Gross column for each row and add a new column 'Total' as teh sum of both.</p>
<p>Sample dataset as below</p>
<pre><code> Name state Net Gross
A1 TN ... | <p>In one step we can do <code>melt</code> first </p>
<pre><code>df.melt(['Name','state']).groupby(['Name','state']).value.sum().reset_index()
Out[56]:
Name state value
0 A1 TN 230
1 A2 AP 300
2 A3 KAR 290
</code></pre> | python-3.x|pandas|sum|aggregate|multiple-columns | 2 |
16,687 | 58,942,697 | Feed data into a deployed model in Tensorflow 2.0 | <p>I'm checking the new features of Tensorflow 2.0, and I saw that the <code>placeholder</code>s got deprecated. Now is possible to use directly a python object.</p>
<pre class="lang-py prettyprint-override"><code># Define the SummatorModule that sum the submitted value with the previously
# submitted one
class Summat... | <p>The new saved_model contains the signature of the input/output. Actually the Tensorflow 2.0 C API is not yet released but probably will be a way to parse the Metadata and get these information.</p> | tensorflow|tensorflow-serving|tensorflow2.0 | 0 |
16,688 | 59,011,891 | How to make column operations between two different data frames based on a condition in pandas python | <p>I have been trying to perform mathematical operation between two data frames Data1 and Data 2 based on a condition.</p> | <p>Note that for some accounts you have <strong>more than one</strong> operation,
<strong>both</strong> incoming and outgoing.</p>
<p>So to reflect the transfers between accounts in their balances,
in the way compliant with the rules of accounting,
you should process your data as follows:</p>
<ol>
<li>Set the index i... | python|pandas|dataframe | 0 |
16,689 | 58,876,144 | python dataframe gropuby using pd.Series.mode throws error when `by` column contains values with same starting value | <p>I have a dataframe as follows.</p>
<pre class="lang-py prettyprint-override"><code>df2 = pd.DataFrame({
"Name" : ['Thomas', 'Thomas', 'Thomas John'],
"Credit" : [1200, 1300, 900],
"Mood" : ['sad', 'happy', 'happy']
})
</code></pre>
<p>I am trying to group it as follows.</p>
<pre class="lang-py prettyp... | <p>We could use:</p>
<pre><code>aggrFDColumnDetails = {
'Mood':lambda x: x.value_counts().idxmax(),
'Credit':'sum'
}
df=df2.groupby(['Name']).agg(aggrFDColumnDetails)
print(df)
Mood Credit
Name
Thomas happy 2500
Thomas John happy 900
</code></pre>
<hr>
<p>as ... | pandas|dataframe|python-3.6|pandas-groupby|mode | 2 |
16,690 | 58,793,491 | convert 6 digit int into to yyyymm in pandas | <p>I made a file that had three date columns:</p>
<pre><code>pd.DataFrame({'yyyymm':[199501],'yyyy':[1995],'mm':[1],'Address':['AL1'],'Number':[12]})
yyyymm yyyy mm Address Number
0 199501 1995 1 AL1 12
</code></pre>
<p>and saved it as a file:</p>
<pre><code>df.to_csv('complete.csv')
</code></pr... | <p>The columns <code>yyyymm</code> and <code>yyyy</code> and <code>mm</code> are <em>integers</em>. By using <code>.astype(str)</code>, you convert these to strings. But a string has no <code>.dt</code>.</p>
<p>You can use <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_datetime.html" rel... | pandas|datetime | 1 |
16,691 | 70,300,750 | Is there a function in Python to show all rows from a particular year from a data frame with a column of Date type? | <p><code>df['Date']</code></p>
<p>gives me the data like this</p>
<pre><code>2014-08-22
2016-08-27
2014-04-12
2015-05-05
</code></pre>
<p>I need a way to filter out so that it returns only the entire rows from the year 2014.</p>
<p>the type of df['Date'] is pandas.core.series.Series</p> | <p>Use:</p>
<pre><code>df = df[df['Date'].dt.year == 2014]
</code></pre>
<p>For multiple years:</p>
<pre><code>df = df[df['Date'].dt.year.isin([2014, 2015])]
</code></pre> | python|pandas | 1 |
16,692 | 70,230,009 | Pandas df.apply() | <p>I have a df:</p>
<pre><code>test2= pd.DataFrame({
'A':[1, 2, 3],
'B':[4, 5, 6],
'C':[7, 8, 9] })
</code></pre>
<p>and I have written a simple function as:</p>
<pre><code>def add(a,b,c):
return a+b+c
</code></pre>
<p>Now I am using this function in my df using pandas df.apply m... | <p>It is because <code>apply</code> expects a function to apply to a <code>DataFrame</code> or a <code>Series</code>. If you call <code>add</code> on the three <code>Series</code> objects you are passing to it you don't need to call <code>apply</code> anymore at all. This is because addition is already applicable to <c... | python|pandas | 0 |
16,693 | 70,027,928 | Create a set from two datasets with only values from df1 that isnt in df2 | <p>I have 2 dataframes.</p>
<p>I want to create a series with locations from df1 that arent duplicated in df2.</p>
<p>i am confused how to do this, any answers appreciated</p> | <p>From <code>df_1</code> :</p>
<pre class="lang-py prettyprint-override"><code>>>> df_1
values
0 a
1 b
2 c
3 d
4 e
</code></pre>
<p>And <code>df_2</code> :</p>
<pre class="lang-py prettyprint-override"><code>>>> df_2
values
0 b
1 e
2 f
3 g
</code></pre>
<p>We can get the val... | pandas|dataframe|series | 0 |
16,694 | 56,409,500 | Image in image.show isn't showing anything (Python 3 notebook) | <p>My output doesn't show anything and I honestly can't find out why</p>
<p>This is the full code, but I think the problem is when I'm passing the argument to <code>aRed, aGreen, aBlue, originalImage = openImage(response.content)</code>
When I run that code in collab python notebook, my image isn't showing up for some... | <p>You don't need to specify <code>.show()</code> in interactive modes. Just remove that part, and it will work fine.</p>
<pre><code>import numpy
from PIL import Image
import requests
from io import BytesIO
# FUNCTION DEFINTIONS:
# open the image and return 3 matrices, each corresponding to one channel (R, G and B c... | python|image|numpy|url|python-imaging-library | 0 |
16,695 | 55,913,667 | How to extract segments with the same value? | <p>I have the following dataframe, </p>
<pre><code>df = pd.DataFrame({'col1':range(20), 'col2': list(range(3)) + [5] *3 +list(range(3)) + [3]*3 + list(range(4)) + [2]*3 + [4] },
index = pd.date_range('1/1/2000', periods=20, freq='1S'))
df
Out[115]:
col1 col2
2000-01-01 00:00:00 0 ... | <p>Here is one way using <code>diff</code> and <code>cumsum</code> create the different group , then we using <code>transform</code> and <code>count</code> , get the group count , and select count equal to 3 ,finally we just need <code>groupby</code> and split the dataframe by <code>col2</code></p>
<pre><code>s=df.col... | python|pandas|dataframe | 2 |
16,696 | 64,731,339 | Merging two pandas dataframes with common data | <p>Consider this data:</p>
<pre><code> INF CTR Time
A 1 8 3
B 5 1 3
C 3 2 3
</code></pre>
<p>And I have another set of data with the same elements, but different column names:</p>
<pre><code> INF2 CTR2 Time
A 3 1 3
B 6 4 3
C 1 7 3
</code></pre>
<p>I need to merge ... | <p>When you want to join on indexes use <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.join.html" rel="nofollow noreferrer">.join()</a>, otherwise <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html" rel="nofollow noreferrer">pd.merge()</... | python|pandas|dataframe | 1 |
16,697 | 64,905,089 | How to find row number from content of cell in python? | <p>How do I from a specific ticker in a cell parse out where that specific ticker occurs? For example if I'm looking for 'AAAB' I would get a read back of every row number that has 'AAAB' Is it better to use pandas or CSV to do this?</p>
<pre><code> ,ticker
472,AA
473,AA
474,AAAB
475,AAAB
476,AAA... | <p>If you just want the number of the rows that match the desired pattern, you can use <code>pandas</code>. If your data is inside a <code>.csv</code> file, you can just do:</p>
<pre><code>import pandas as pd
filename = 'my_file.csv'
pattern = 'AAAB'
df = pd.read_csv(filename, index_col=0)
rows = df[df['ticker'] == ... | python|pandas|database|csv | 2 |
16,698 | 40,281,849 | Concatenate the results of two calls to tf.contrib.learn.datasets.base.load_csv_with_header | <p>I'm trying to concatenate the results of two calls to <code>tf.contrib.learn.datasets.base.load_csv_with_header</code>. I have to do this because I can't have files larger than 25MB on the Juypter Notebook server, so I have to split them in two.</p>
<p>Currently I'm attempting to do this, but it just gives a <code>... | <p>So I found a different way to do it. Since later on in the script I split up the <code>data</code> and <code>target</code> parts it was as easy as this:</p>
<pre><code>my_x = training_set1.data + training_set2.data
my_y = training_set1.target + training_set2.target
</code></pre> | python|tensorflow | 0 |
16,699 | 40,029,618 | How to update `xarray.DataArray` using `.sel()` indexer? | <p>I found the easiest way to visualize creating a <code>N-dimensional</code> <code>DataArray</code> was to make a <code>np.ndarray</code> and then fill in the values by the coordinates I've created. When I tried to actually do it, I couldn't figure out how to update the <code>xr.DataArray</code>.</p>
<p><strong>How ... | <p>This is really awkward, due to the unfortunate limitation of Python syntax that keyword arguments are not supported in inside square bracket.</p>
<p>So instead, you need to put the arguments to <code>.sel</code> as a dictionary in <code>.loc</code> instead:</p>
<pre><code>DA.loc[dict(axis_A="A_1", axis_B="B_1", ax... | python|database|numpy|dataframe|python-xarray | 18 |
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