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 |
|---|---|---|---|---|---|---|
10,700 | 51,353,918 | how to do finetune using pre-trained model in tf.estimator | <p>i got a model converted from caffe by using MMDNN tool, it converted the caffe model into a saved_model tensorflow style. it's a resnet18 model, and i just strip out several layers in the last, i wish i could load this architecture in the model_fn in a tf.estimator, and manually add some extra layers to do my job.
A... | <p>Here is one way of fine tuning using tf.Estimator: </p>
<ol>
<li>Define your model using the SAME variable names/scopes as in your saved model</li>
<li><p>Use tf.estimator's warm start functions to initialize your new model with the saved weights. Here is a code snippet :</p>
<pre><code>if fine_tuning:
ws = tf... | tensorflow|tensorflow-estimator | 1 |
10,701 | 51,304,669 | Pandas failing to install when deploying Dash app to Azure | <p>I'm getting the following error message when trying to deploy my Dash app to Azure:</p>
<p><a href="https://i.stack.imgur.com/AfmSz.png" rel="nofollow noreferrer">Error</a></p>
<p>It then cleans up and says that "Command pyton setup.py egg_info failed with error code 1 in D:\home\site\wwwroot\env\build\Pandas"</p>... | <p>Funnily enough I ran into the exact same issue for a web app that I am working on at the moment. After 5 days of trying endless solutions I have eventually managed to get my app to deploy to Azure. My app is a Flask web app but the process is pretty much the same (if you are using Django or Dash in your case) or any... | python|pandas|azure | 1 |
10,702 | 70,931,901 | Add list to numpy array | <p>I have an initial array that looks like that:</p>
<pre><code>[[2, 3, 0], [4, 5, 0], [4, 0], [5, 0], [3, 0]]
</code></pre>
<p>after I use np.asarray(arr, dtype = object) it becomes like that:</p>
<pre><code>[list([2, 3, 0]) list([4, 5, 0]) list([4, 0]) list([5, 0]) list([3, 0])]
</code></pre>
<p>and the problem is th... | <p><code>np.append</code> automatically flattens the list you pass it, unless you're append one array to another rectangular array. From <a href="https://numpy.org/doc/stable/reference/generated/numpy.append.html" rel="nofollow noreferrer">the docs</a> (emphasis mine):</p>
<blockquote>
<p><code>axis</code> : <code>int<... | python|arrays|list|numpy | 0 |
10,703 | 36,070,946 | why aren't pandas "rank" percentiles bounded between 0 and 1? | <p>I use pandas frequently and often execute code comparable to the following:</p>
<pre><code>df['var_rank'] = df['var'].rank(pct=True)
print( df.var_rank.max() )
</code></pre>
<p>And will often get values greater than 1. It still happens whether I keep or drop 'na' values. This is obviously easy to fix (just divide ... | <p>You have bad data.</p>
<pre><code>>>> s.rank(pct=True).max()
1.015625
s.sort(inplace=True)
>>> s.tail(7)
8 202512882
6 253661077
102 -
101 -
99 -
58 -
116 -
Name: Total Assets, dtype: object
>>> s[s != u'-'].rank(pct=Tru... | python|pandas|rank|percentile | 1 |
10,704 | 35,930,950 | Trainable weight for TensorFlow sequence_loss_by_example() | <p>I want to have a trainable weight in <code>seq2seq.sequence_loss_by_example()</code>, e.g.</p>
<pre><code>w = tf.get_variable("w", [batch_size*num_steps])
loss = seq2seq.sequence_loss_by_example([logits_1],
[tf.reshape(self._targets, [-1])],
w,vocab_size_all)
</code></pre>
<p>However, runni... | <p>In TensorFlow a <a href="https://www.tensorflow.org/versions/r0.7/api_docs/python/state_ops.html#Variable" rel="nofollow"><code>tf.Variable</code></a> can be used anywhere a <a href="https://www.tensorflow.org/versions/r0.7/api_docs/python/framework.html#Tensor" rel="nofollow"><code>tf.Tensor</code></a> (of the same... | python|tensorflow | 2 |
10,705 | 37,359,192 | Cannot figure out Numpy equivalent for cv.mat.step[0] | <p>I am currently in the process of transferring code from an old OpenCV example into OpenCV3 in Python (using PyObjC and the Quartz module). The Objective-C code takes a UIImage and creates a material that can be used by OpenCV. My python code takes a CGImage and does the same thing.</p>
<p>Here is the Objective-C co... | <p>I ended up abandoning the above approach and found an answer on this stack overflow question that worked after a little bit of editing: <a href="https://stackoverflow.com/questions/22938654/converting-cgimage-to-python-image-pil-opencv">Converting CGImage to python image (pil/opencv)</a></p>
<pre><code>image_ref = ... | python|objective-c|macos|opencv|numpy | 1 |
10,706 | 37,313,693 | When taking nlargest in pandas dataframe, is there a way to ignore column with NaN values? | <p>When taking nlargest in pandas dataframe, is there a way to ignore column with NaN values? If say I want to pick 5 column headings with the 5 largest values, and if some of the columns has NaN values, then the column is ignored. If the number of columns with finite values is smaller than 5, then pick all the column ... | <p><code>nlargest</code> takes the n top rows sorted descendingly by the <code>columns</code> passed to the method. If there are NaN values that get to the top then it will include these. If you wan to ignore rows in which NaN values exist in the columns that were sorted by then do this:</p>
<pre><code># assume a va... | python|pandas | 1 |
10,707 | 37,275,140 | join two .csv files according to reference column in pandas | <p>I have 2 files of different size (customer_id is not in same order in both files):</p>
<p><a href="https://i.stack.imgur.com/2Wm61.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/2Wm61.png" alt="enter image description here"></a></p>
<pre><code>data = pd.read_csv('data.csv')
id name count... | <p>Try to use pandas.merge</p>
<h1>Create the dataframes:</h1>
<pre><code>temp = u"""id name country town customer_id
xxxx Anna UK London sahdghkl
yyyy Maria USA Huston avrnnfgs
cccc Peter FR Paris eesfawsd"""
data = pd.read_csv(io.StringIO(... | python|csv|dictionary|pandas | 0 |
10,708 | 37,763,267 | filling forward conditional result | <p>I have a DataFrame with columns <code>A</code> and <code>B</code>. Now I want to produce column <code>C</code> like this:</p>
<pre><code> A B C
index
1 0 50 NaN
2 1 60 60
3 0 40 60
4 0 30 60
5 1 40 40
</code></pre>
<p><code>C</code> gets the value of <code>... | <p>You can select the values of B where A==1, then fill forward:</p>
<pre><code>a = pd.DataFrame({"A":[0,1,0,0,1], "B":[50,60,40,30,40]}, index=[1,2,3,4,5])
a["C"] = a.B[a.A == 1]
a = a.fillna(method="ffill")
</code></pre>
<p>The ffill method propagates forward the last valid observation to fill in the NaNs. See <a h... | python|numpy|pandas|dataframe|vectorization | 2 |
10,709 | 31,298,111 | Using OrderedDict but in graph months out of order | <p>I have a pivot_table being read into a bar plot using bokeh. The problem is that the months are plotted out of order, even though I am using OrderedDict. Here is a sample of the pivot_table</p>
<pre><code>pivot_table.head(3)
Out[45]:
Month 1 2 3 4 5 6 7 8 9 10 11 Co... | <p>You can use OrderedDict like this:</p>
<pre><code>months = OrderedDict([('Jan', Jan), ('Feb', Feb), ('Mar', Mar)])
</code></pre>
<p>Or:</p>
<pre><code>months = OrderedDict()
months['Jan'] = Jan
months['Feb'] = Feb
months['Mar'] = Mar
</code></pre>
<p>An OrderedDict is a dict that remembers the order that keys we... | python|pandas|bar-chart|ordereddictionary | 2 |
10,710 | 47,635,397 | Reordering nodes in increasing order in pandas dataframe | <pre><code>data1 = { 'node1': [2,2,3,6],
'node2': [6,7,7,28],
'weight': [1,2,1,1], }
df1 = pd.DataFrame(data1, columns = ['node1','node2','weight'])
</code></pre>
<p>I want to rename the node1 and node 2 in the data1 according in increasing order.
Nodes are 2 3 6 7 28 so they become 1 2 3 4 5 respectively.</... | <p>Factorizing by sorting and assigning by reshaping i.e </p>
<pre><code>df1[['node1','node2']] = (pd.factorize(np.sort(df1[['node1','node2']].values.reshape(-1)))[0]+1).reshape(-1,len(df1)).T
node1 node2 weight
0 1 3 1
1 1 4 2
2 2 4 1
3 3 5 1
</co... | python|pandas|dataframe | 7 |
10,711 | 48,954,694 | How to interpret the loss returned by model.evaluate()? | <p>I searched a lot for an answer but wasn't able to find a satisfying one.</p>
<p>If I understood correctly, during <code>model.fit()</code>, Keras prints the loss for the last batch to terminal. </p>
<p>If I call <code>model.evaluate()</code> on the training set I get the loss value for the whole set. </p>
<p>So, ... | <blockquote>
<p>If I understood correctly, during model.fit(), Keras prints the loss for the last batch to terminal. </p>
</blockquote>
<p>Generally speaking yes, but this also depends on your <code>verbose</code> parameter; if it is set to <code>2</code> you are going to get <em>one line per epoch</em>, but if you ... | python|tensorflow|machine-learning|keras | 2 |
10,712 | 49,157,647 | Converting numpy array to single integer | <p>I am a new Python user, and am struggling with what is seemingly a very easy problem — yet I cannot seem to solve it. The problem is that I created an array from np that contains a single value.</p>
<pre><code>In: distance_index
Out: (array([14], dtype=int64),)
In: type(distance_index)
Out: tuple
</code></p... | <p>To get any index of your array just use "array[index]". Keep in mind arrays start at 0</p>
<pre><code>print(distance_index[0])
</code></pre>
<p>However, as @Christian Dean points out, this question deals with an array in a tuple. So you need:</p>
<pre><code>print(distance_index[0][0])
</code></pre> | python|arrays|numpy | 0 |
10,713 | 58,923,432 | Handling duplicate data with pandas | <p><strong>Hello everyone, I'm having some issues with using pandas python library. Basically I'm reading csv
file with pandas and want to remove duplicates. I've tried everything and problem is still there.</strong></p>
<pre><code>import sqlite3
import pandas as pd
import numpy
connection = sqlite3.connect("test.db... | <p>It seems like a simple group-by could solve your problem.</p>
<pre><code>import pandas as pd
na = 'North America'
a = 'Asia'
e = 'Europe'
df = pd.DataFrame({'Retailer': [0, 1, 2, 3, 4, 5, 6],
'country': ['Unitied States', 'Canada', 'Japan', 'Italy', 'Canada', 'Unitied States', 'France'],
... | python-3.x|pandas|dataframe|unique | 2 |
10,714 | 70,250,156 | Transpose pandas Styler | <p>I would like to style my data for some data analysis. This works well with <code>df.style</code>, which I have only recently discovered. I seem to be missing one crucial aspect, however: how can you transpose a Styler <em>after</em> you have applied methods to it? This is useful when you want to use the column names... | <p>Not entirely correct, but <code>pd.DataFrame.style</code> can be thought of as an HTML-formatted string, so it doesn't have a transpose operator.</p>
<p>You can transpose the data and use <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.IndexSlice.html" rel="nofollow noreferrer"><code>pd.In... | python|pandas|data-visualization | 0 |
10,715 | 70,326,263 | Calculating hamming distance in a given year | <p>I have a following dataframe:</p>
<pre><code>Bacteria Year Feature_Vector
XYRT23 1968 [0 1 0 0 1 1 0 0 0 0 1 1]
XXQY12 1968 [0 1 0 0 0 1 1 0 0 0 1 1]
RTy11R 1968 [1 0 0 0 0 1 1 0 1 1 1 1]
XYRT23 1969 [0 1 0 0 1 1 0 0 0 0 1 1]
XXQY12 1969 [0 0 1 0 0 1 1 0 0 0 1 1]
R... | <p>The function <code>pairwise_distances</code> can take in a matrix, so it might be easier to just provide the features in a year as a matrix, get back a pairwise matrix of distances and just subset on the comparisons we need. For example, a dataset like yours:</p>
<pre><code>df = pd.DataFrame({'Bacteria':['XYRT23','X... | python|pandas|scikit-learn | 1 |
10,716 | 70,359,075 | How to upload data from pandas into Google sheet? | <p>I am trying to upload data from scraping into a google sheet using pandas, but I get the following error:</p>
<pre><code>worksheet.update([df.columns.values.tolist()] + df.values.tolist())
AttributeError: 'list' object has no attribute 'columns'
</code></pre>
<p>Here is my code</p>
<pre class="lang-py prettyprint-ov... | <p>According to pandas documentation:
<a href="https://pandas.pydata.org/docs/reference/api/pandas.read_html.html" rel="nofollow noreferrer">https://pandas.pydata.org/docs/reference/api/pandas.read_html.html</a></p>
<p>The method <code>read_html</code> returns a list of dataframes, that is why you are getting that erro... | python|pandas|google-sheets|google-api | 0 |
10,717 | 70,246,141 | Average of multiple rows based on column condition in Python | <p>How do I get the average from multiple rows where column stage = 2.</p>
<p>At the moment I am using</p>
<pre><code>average = df.loc[df.Stage == 2,'Vout'].mean()
</code></pre>
<p>However, this returns an average based off the entire column.</p>
<p>I want to have multiple average values based off certain rows, as ther... | <p>If possible distinguish group by missing values use:</p>
<pre><code>df['g'] = df['Stage'].isna().cumsum()
average = df.loc[df.Stage == 2].groupby('g')['Vout'].mean()
</code></pre> | python|pandas|dataframe | 1 |
10,718 | 70,260,554 | Problems appending columns from one excel file to another | <p>I seem to be unable to figure out how to append values from one excel file to another one.
I have tried append mode numerous times but it fails each time due to encoding errors.
I have sought solutions but none has turned out to solve my problem.</p>
<p>I have two excel files which have a certain amount of rows. The... | <p>File <code>.xlsx</code> has complex structure. It is <code>zip</code> file with many files. One of them is <code>.xml</code> with all data, others can be images embeded in file, macros in VBA, etc.</p>
<p>You can even change extension from <code>.xlsx</code> to <code>.zip</code> and see content.</p>
<p>So you can't ... | python|excel|pandas|csv|openpyxl | 0 |
10,719 | 56,337,049 | How to convert hex form dataframe to human readable form in python | <p>I am trying to fetch varbinary form of data from mssql server and convert it into readable form.</p>
<pre><code>def fetchdata(self):
query = 'select * from xlstorage'
df = pd.read_sql(query, self.conn)
print(df.to_string())
print(type(df))
print(df.all)
</code></pre>
<p>id ... | <p>check the <strong>struct.unpack()</strong> module.
As we don't know what kind of data your string should contain, its hard to tell you, what <strong>format characters</strong> to use <a href="https://docs.python.org/3.7/library/struct.html" rel="nofollow noreferrer">see here</a></p>
<p>the struct module is used li... | python-3.x|pandas|hex|pyodbc | 1 |
10,720 | 56,098,186 | Extract date from strings that contains names+dates | <p>I need to extract the dates from a series of strings like this:</p>
<pre><code>'MIHAI MĂD2Ă3.07.1958'
</code></pre>
<p>or</p>
<pre><code>'CLAUDIU-MIHAI17.12.1999'
</code></pre>
<p>How to do this?</p>
<p>Tried this:</p>
<pre><code>for index,row in DF.iterrows():
try:
if math.isnan(row['Data_Nasterii... | <p>The <code>.</code> (dot) in regex doesn't mean the character dot, it means "anything" and needs to be escaped (<code>\</code>) to be an actual dot. <br>other than that your first group is <code>\d{2}</code> but some of your dates have a single digit day. <br>I would use the following:</p>
<pre><code>re.search(r'(\d... | python|python-3.x|pandas|date|extract | 2 |
10,721 | 55,831,153 | How I can vectorize this operation in pandas? | <p>SO community, I'm working with a pandas data frame that has the following structure.</p>
<p>Structure:</p>
<pre><code>index event_name info
8469 OPTIONS 20404,400,113,117
8470 OPTIONS_SELECTION 117
8473 OPTIONS 437,436,114,117
8475 OPTIONS_SELECTION 437
8479 OPTIONS 121,451,444,407
8481 OP... | <p>Using <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.where.html" rel="nofollow noreferrer"><code>where</code></a>:</p>
<pre><code>log_df['Selection'] = log_df['info'].where(
log_df['event_name'] == 'OPTIONS_SELECTION', '').shift(-1)
</code></pre>
<p>Keep ... | python|pandas | 0 |
10,722 | 64,694,623 | Load a tensorflow model inside another one, and concatenate two models | <p>The idea is to create a deconvolution model following my convolution model to see the importance of the learned pixels.</p>
<p><a href="https://i.stack.imgur.com/Hmii0.png" rel="nofollow noreferrer">Overview of the model</a></p>
<p>I am having problems that I cannot explain.
The first is that when I have created my ... | <p>Thanks Amin, You answered my question, the problem came from the type of my input variable. (uint8 instead of float32) The predict managed to work even if the type was wrong but by fixing this problem I can now decompose my model and everything works!</p>
<p>It only stays the problem to load the data.
If you tell me... | python|tensorflow|deep-learning|deconvolution | 0 |
10,723 | 39,542,642 | Reshaping pandas dataframe: New row for every 76 entries | <p>I'm new to Python and Pandas and am playing around with a heart disease data set via UCI. <a href="https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/hungarian.data" rel="nofollow">https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/hungarian.data</a></p>
<p>There are 76 att... | <p>As <a href="https://stackoverflow.com/questions/39542642/reshaping-pandas-dataframe-new-row-for-every-76-entries#comment66400235_39542642">@Boud has already said</a> it's much easier to pre-process your data instead of massaging "incorrectly built" DF:</p>
<pre><code>import io
import requests
import pandas as pd
u... | python|pandas | 1 |
10,724 | 39,796,519 | How would I count the number of days based on months with zero data? | <p>I'm writing a script in which I read in a csv with several columns and rows. I need the script to total the values in each column for a single row and return which columns have a value of zero for the row. Here's an example of what the data looks like, there are several other columns but these are the columns of int... | <p>well, I would get rid of the "fout" line. You don't seem to write to that file, and it doesn't need to be open to use the "read_csv" feature of pandas. then you can go through each row and find what's zero, and what isn't</p>
<pre><code>returnArray = []
i=0
while i < len(df.values):
j=14 #since user only c... | python|pandas | 0 |
10,725 | 44,286,093 | PANDAs: create 'filled' column with incremental datetime values, in between 2 datetimes (range) columns | <p>I have a dataframe with 2 columns ['startdt'] and ['enddt']. They are datetime objects in a PANDAs dataframe. I'd like to create a new column which is grouped according to each combination of 'startdt' and 'enddt', and is filled with values down rows of the column, with 10 minute increment values from the 'startdt' ... | <h1>Define a custom (generic) transform function</h1>
<pre><code>def transform_func(row, freq, include_last):
start = row['startdt'].min()
end = row['endt'].max()
idx = pd.DatetimeIndex(start=start, end=end, freq=freq)
if include_last and idx[-1] != end:
idx = idx.append(pd.DatetimeIndex([end])... | python|pandas | 2 |
10,726 | 43,950,463 | Python Pandas Series Tuples dataframe | <p>I have somehow got a Series with index as a Tuple where as data as a number. I want to convert it into a series with index as a single string that is by removing the tuple[0] value.<a href="https://i.stack.imgur.com/kAoa4.png" rel="nofollow noreferrer">This is my current output</a> <a href="https://i.stack.imgur.com... | <p>You need select second value of tuples by <code>str[1]</code>:</p>
<pre><code>s.index = s.index.str[1]
</code></pre>
<p>Sample:</p>
<pre><code>s = pd.Series([80,79,70],
index=[('total','Mumbai'),('total','Chennai'),('total','Royal')])
print (s)
(total, Mumbai) 80
(total, Chennai) 79
(total, ... | python|pandas|dataframe|tuples | 1 |
10,727 | 69,380,454 | Convert list of dict into DataFrame with Koalas | <p>I've tried to convert a list of dicts into a Databricks' Koalas DataFrame but I keep getting the error message:</p>
<blockquote>
<p>ArrowInvalid: cannot mix list and non-list, non-null values</p>
</blockquote>
<p>Pandas works perfectly (with pd.DataFrame(list)) but because of company restrictions I must use PySpark/... | <p>You can create a Spark DataFrame using your data without data-manipulation using <code>spark.createDataFrame()</code>.</p>
<pre><code>sdf = spark.createDataFrame(
data_list,
T.StructType([
T.StructField('A', T.ArrayType(T.IntegerType()), True),
T.StructField('B', T.ArrayType(T.IntegerType()),... | python|pandas|dataframe|pyspark|spark-koalas | 0 |
10,728 | 69,525,476 | Federated Averaging (fedavg) with resnet 18 that has batch_normalization makes the same prediction after first round, but in no other rounds | <p>I was trying to implement <a href="https://github.com/tensorflow/federated/tree/main/tensorflow_federated/python/examples/simple_fedavg" rel="nofollow noreferrer">tensorflow-federated simple fedavg</a> with <a href="https://github.com/ozgurcelik/stackoverflowQuestion" rel="nofollow noreferrer">cifar10 dataset and re... | <p>I guess loading model weights from previously trained model would resolve the issue? See <a href="https://stackoverflow.com/questions/65273151/how-to-initialize-the-model-with-certain-weights">How to initialize the model with certain weights?</a> for how to initialize the first round model weights.</p> | tensorflow|pytorch|tensorflow-federated|federated-learning | 0 |
10,729 | 69,638,778 | Return substring if present in a string and match with case insensitive python | <p>I am currently trying to return a substring if is present in a string, with case insensitive.</p>
<p>So an example would be, I want to return the string "apple" even when the sentence is "Apple is cool" or "I like APPLE" or "I like apples"</p>
<p>What I have so far is this:</p... | <pre><code>df.sentence.str.lower().str.split().apply(lambda l: ", ".join([x for x in l if x in df_word_list["word"].values]))
</code></pre>
<p>result is <code>pandas.Series</code> of strings</p>
<pre><code>0 apple, cool
1 apple
2
Name: sentence, dtype: object
</code></pre> | python|pandas | 0 |
10,730 | 69,301,179 | why does ~True not work in pandas dataframe conditional | <p>I am trying to use switches to turn on and off conditionals in a pandas dataframe. The switches are just boolean variables that will be True or False. The problem is that ~True does not evaluate the same as False as I expected it to. Why does this not work?</p>
<pre><code>>>> dataframe = pd.DataFrame({'c... | <p>This is a pandas operator behavior (implemented from Numpy).</p>
<p><code>True</code> is not a pandas object. Instead it's a boolean. So obviously, the <code>~</code> operator isn't meant to reverse booleans, only in Pandas.</p>
<p>As you can see:</p>
<pre><code>>>> ~True
-2
>>>
</code></pre>
<p>I... | python|pandas | 1 |
10,731 | 53,923,914 | Weird / Wrong outpout of np.argsort() | <p>I was working with numpy and argsort, while encountering a <em>strange (?)</em> behavior of argsort:</p>
<pre><code>>>> array = [[0, 1, 2, 3, 4, 5],
[444, 4, 8, 3, 1, 10],
[2, 5, 8, 999, 1, 4]]
>>> np.argsort(array, axis=0)
array([[0, 0, 0, 0, 1, 2],
[2, 1, 1, 1... | <p>I think the issue is with what you think <code>argsort</code> is outputting. Let's focus on a simpler 1D example:</p>
<pre><code> arr = np.array([5, 10, 4])
</code></pre>
<p>The result of <code>np.argsort</code> will be the indices from the original array to make the elements sorted:</p>
<pre><code>[2, 0, 1]
</co... | python|numpy | 6 |
10,732 | 54,036,596 | Replacing/Selecting values in columns using loc. Pandas | <p>I am using label based indexing function <code>loc</code> to search all the labels where value of the object is <code>"UN"</code> in a list of column i.e is list <code>"columns"</code>, but in this piece of code as soon as <code>loc</code> doesn't find <code>"UN"</code> at the first index, it stops after that, print... | <p>I think you need to search for 'UN' in column for the first 172 records, if so:</p>
<pre><code># returns a dataframe
df.head(172).filter(df[column] == 'UN')
</code></pre>
<p>Docs: <a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.filter.html#pandas.DataFrame.filter" rel="nofollow nor... | python|database|python-3.x|pandas | 1 |
10,733 | 54,244,233 | Faster solution for date formatting | <p>I am trying to change the format of the date in a pandas dataframe.
If I check the date in the beginning, I have:</p>
<pre><code>df['Date'][0]
Out[158]: '01/02/2008'
</code></pre>
<p>Then, I use:</p>
<pre><code>df['Date'] = pd.to_datetime(df['Date']).dt.date
</code></pre>
<p>To change the format to </p>
<pre... | <p>You should first collapse by <code>Date</code> using the <code>groupby</code> method to reduce the dimensionality of the problem.</p>
<p>Then you parse the dates into the new format and merge the results back into the original DataFrame.</p>
<p>This requires some time because of the merging, but it takes advantage... | python|pandas|date|datetime|datetime-format | 1 |
10,734 | 54,011,487 | TypeError: unsupported operand type(s) for /: 'Image' and 'int' | <p>I wanted to convert the PIL Image object into a numpy array. I tried using the following codes it showing an error</p>
<pre><code>TypeError Traceback (most recent call last) <ipython-input-133-0898103f22f0> in <module>()
1 image_path = 'test/28/image_05230.jpg'
----> 2 image = process_image(ima... | <p>In the function <code>convert_pil_to_numpy_array()</code>, the <code>image</code> variable used initially is different from the <code>image</code> variable that stores the <code>crop</code>ped <code>Image</code> object.</p>
<pre><code>from PIL import Image
image_path = "C:\\temp\\Capture.JPG"
image = Image.open(ima... | python|python-3.x|numpy|python-imaging-library | 3 |
10,735 | 38,318,041 | Numpy Arrays: Extracting preferentially ordered values from array with Nans without padding? | <p>Suppose I have an array (M,N) where the values in each "column", N, represent data recordings of N different machines. Let's also imagine each "row", M, represents a unique "timestamp" where data was recorded for all of the N machines.</p>
<p>The array (M,N) is structured in a way so that at M = 0, this would corre... | <p>This is the kind of question that can generate many interesting answers. Someone will probably come up with a better way than this, but to get things started, here's one possibility:</p>
<pre><code>In [99]: AX
Out[99]:
array([[ 0.53826804, -0.9450442 , nan, 0.47251871, nan],
[ nan, ... | python|arrays|numpy|nan | 2 |
10,736 | 66,087,127 | Pandas Dataframe displays results inside brackets with commas | <p>I am reading data into dataframe using <code>pd.read_sql()</code>. The
database is Oracle and the Oracle connectivity module is <a href="https://pypi.org/project/JayDeBeApi/" rel="nofollow noreferrer">JayDeBeApi</a>.</p>
<p>Issue:
The connection is successful and I am able to retrieve data as well.
But when I print ... | <p>I assume that you are using <code>jpype</code> with <code>jaydebeapi</code>, since I have seen this exact behavior in such a setting before (see also <a href="https://stackoverflow.com/a/66955290/14015737">this SO answer and the comments</a>).</p>
<p>You can either deal with the symptoms (see other answer) or preven... | python|sql|pandas|dataframe|jaydebeapi | 1 |
10,737 | 65,941,981 | Why does my pandas filters work in separate steps but not in one command? | <p>I'm having problems filtering my pandas dataframe in one command. For instance, the following multi-step filter works perfectly:</p>
<pre><code>check2020 = check[check['effyear'] == '2020']
check2020_can = check2020[check2020['canceled'] == 'Y']
check2020_can_6 = check2020_can[check2020_can['decile'] == 6]
check202... | <p>I believe you need extra pair of parenthesis:</p>
<pre><code>check[(check['effyear'] == '2020') &
(check['canceled'] == 'Y') &
(check['decile'] == 6) &
((check['usage'] == 'P') | (check['usage'] == 'S')) # () here
]
</code></pre>
<p>Also, use <code>isin</code> for better syntax:... | python|pandas|filter|slice | 1 |
10,738 | 65,994,405 | Cannot install tensorflow using pip | <p>I am trying to call a simple <code>keras.Sequential()</code> model in python 3.9 (64bit), however when trying to install <code>tensorflow</code> using <code>pip</code> I get the following error:</p>
<pre><code>ERROR: Could not find a version that satisfies the requirement tensorflow
ERROR: No matching distribution f... | <p>It appears that <a href="https://www.tensorflow.org/install" rel="nofollow noreferrer">TensorFlow only supports Python 3.5-3.8</a>, so you are unable to install it because your version of Python is 3.9.</p>
<p>You will need to use a different version of Python if you want to install TensorFlow.</p> | python|tensorflow | 1 |
10,739 | 65,960,833 | Using pandas groupby and pd.concat together to add rows to a column | <p>I have a large data frame with ~20 years of data. I would like to group this data frame by YEAR, and then add the same set of new X values to each group. I'm having trouble figuring how to use pd.concat with groupby. How can I use pd.concat and df.groupby together?</p>
<p>Below is a subset of my data frame (I delete... | <p>Not a solution, I'm just not allowed to comment yet.</p>
<p>It should be <code>pd.concat</code> I think. Also, the lambda function in your groupby uses x as parameter, and so hides the x DataFrame. Name them differently, for example:</p>
<pre><code>concat_df = df.groupby(['YEAR']).apply(lambda y: pd.concat([y, x]))
... | python|pandas|dataframe|group-by|concatenation | 1 |
10,740 | 52,681,731 | Python For loop to update a data frame | <p>I am trying to re-create a VBA macro that I have using Python. Could someone please tell me the FOR statement I should use, in order to get below result? Thank you very much.</p>
<p>file 1:</p>
<pre><code>Product Colour Price
Book NaN 5
Table NaN 10
Chair NaN 7
</code></pre>
<p>file 2:</p>
<pre><code>Col... | <p>First duplicate values of <code>df1</code> by length of <code>df2</code> and then use <code>list comprehesion</code> and <a href="https://docs.python.org/2/library/itertools.html#itertools.chain.from_iterable" rel="nofollow noreferrer"><code>chain</code></a> for <code>Colour</code> as:</p>
<pre><code>from itertools... | python|pandas|loops|dataframe | 1 |
10,741 | 46,206,039 | Efficient way to find the paths with highest average edge value between two points in a pandas dataframe? | <p>Pardon the seemingly confusing phrasing of the question. Here is what I'd like to do.</p>
<p>Given a dataframe <strong>df</strong></p>
<pre><code>Fruit1 Fruit2 Weight
orange apple 0.2
orange grape 0.4
orange pineapple 0.6
orange banana 0.8
apple grape 0.9
appl... | <p>Thanks for the clarification, so you are asking for the path with the highest cost/(number of edges) between each pair of nodes in your graph where the paths are restricted to an upper limit of connecting edges. The longest path problem is np-hard so an efficient solution is only possible with restrictions
(see <a ... | python|pandas|numpy|graph|networkx | 3 |
10,742 | 46,572,361 | Configuring Keras to use Tensorflow instead of Theano | <p>I'm trying to configure Keras install under an Anaconda virtualenv with all of that running under Ubuntu 17.04. I've installed <code>keras-gpu</code> via <code>conda</code>, and have generated a bootstrap <code>~/.keras</code> directory by running <code>python -c 'import keras'</code>; finally, I've updated my <code... | <p>The problem is probably in the file <code>activate.sh</code> of the <a href="https://github.com/conda-forge/keras-feedstock/tree/master/recipe" rel="nofollow noreferrer">keras package</a> <strong>on conda-forge</strong>. The <code>export</code> statements in this file are unnecessary and should be removed IMO. There... | tensorflow|keras|conda | 1 |
10,743 | 46,498,973 | Python loading a text file line by line to a numpy array | <p>I have a text file in the following format where each line is separated by a line break</p>
<pre><code>a 0.418 0.24968 -0.41242 0.1217 0.34527 -0.044457 -0.49688 -0.17862 -0.00066023 -0.6566
b 0.013441 0.23682 -0.16899 0.40951 0.63812 0.47709 -0.42852 -0.55641 -0.364 -0.23938
c 0.15164 0.30177 -0.16763 0.17684 0.31... | <p>Use Pandas' <code>read_table</code> instead, then convert the DataFrame to the numpy array you need. It will be faster and cleaner than trying to split strings and assemble an array row-by-row. </p> | python|arrays|numpy | 0 |
10,744 | 46,364,407 | Add a new column of timedelta values in a particular format in Pandas | <p>I have a dataframe and I want to add a column containing time difference between two another column: </p>
<pre><code> df[Diff] = df['End Time'] - df['Open Time']
df[Diff]
0 0 days 01:25:40
1 0 days 00:41:57
2 0 days 00:21:47
3 0 days 16:41:57
4 0 days 04:32:00
5 0 days 03:01:57
6 ... | <p>You could extract the relevant columns, convert to <code>str</code> using <code>astype</code>, and just concat the cols as needed.</p>
<pre><code>c = (df['End Time'] - df['Open Time'])\
.dt.components[['days', 'hours', 'minutes']]
df['diff'] = (c.days * 24 + c.hours).astype(str) + 'h ' + c.minutes.ast... | python|pandas|datetime|dataframe|date-formatting | 2 |
10,745 | 58,470,644 | Including column name as argument in function that uses dot notation | <p>I'd like to include the column name as an acceptable argument for this function to make it more flexible. </p>
<pre><code>def func(df, column):
return df.column.str.split('', n = 5, expand=True)
</code></pre>
<p>As expected, <code>func(my_df, columnX)</code> is returning a <code>NameError: name 'columnX' is no... | <p>You can access your column as if it where a dictionary field. Another recommendation is not to name an object as it class name (replace <code>DataFrame</code> by <code>df</code>). Finally you need to return the obtained object. </p>
<pre class="lang-py prettyprint-override"><code>def func(df, column):
return df... | python|pandas | 1 |
10,746 | 58,599,431 | create loop to extract urls to json and csv | <p>I set up a loop to <strong>scrape</strong> with 37900 records. Due to the way the url/ server is being set up, there's a limit of 200 records displayed in each url. Each url ends with 'skip=200', or mulitiple of 200 to loop to the next url page where the next 200 records are displayed. Eventually I want to loop thro... | <p><em>I have nothing to test against</em></p>
<p>I think you massively over-complicated this. You've since edited the question but there's a couple of points to make:</p>
<ol>
<li>You define <code>jsnlist = []</code> but never use it. Why?</li>
<li>You called your own object <code>json</code> (now gone but I'm not s... | python|json|pandas|loops|url | 0 |
10,747 | 69,097,798 | Equally distribute duplicate Timestamps in column | <p>Given this data frame with the date <code>2020-01-02</code> repeated three times</p>
<pre><code>df_original
time
0 2020-01-02 00:00:00
1 2020-01-02 00:00:00
2 2020-01-02 00:00:00
3 2020-01-03 00:00:00
</code></pre>
<p>I would like to transform it into the following, where the three <code>2020-01-02<... | <p>Use custom function only for duplicted values of column <code>time</code> in <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.transform.html" rel="nofollow noreferrer"><code>GroupBy.transform</code></a>:</p>
<pre><code>df['time'] = pd.to_datetime(df['time'])
m... | python|pandas | 2 |
10,748 | 69,113,768 | maximum sum of consecutive n-days using pandas | <p>I've seen solutions in different languages (i.e. SQL, fortran, or C++) which mainly do for loops.</p>
<p>I am hoping that someone can help me solve this task using pandas instead.</p>
<hr />
<p>If I have a data frame that looks like this.</p>
<pre><code> date pcp sum_count sumcum
7/13/2013 0.1 3.0 ... | <p>First, convert <code>date</code> as a real <code>datetime</code> dtype and create a binary mask which keep rows where <code>pcp</code> is not null. Then you can create groups and compute your variables:</p>
<p>Input data:</p>
<pre><code>>>> df
date pcp
0 7/13/2013 0.1
1 7/14/2013 48.5
2... | python|pandas | 1 |
10,749 | 68,903,153 | Pandas column not displaying when using groupby | <p>I have a data set that I've imported as a Pandas DataFrame. I've converted one of the columns 'talkTime' ,which is represented in seconds (an integer), to a duration representation on the same value. This seems to work fine and I can see that the new column, Duration(h:m:s), has been added to the original DF.</p>
<... | <p>Since <code>Duration(h:m:s)</code> column is not a numeric type, <code>sum</code> can not be applied on it, and even if you use <code>first</code> as aggregate for duration column <code>{</code>Duration(h:m:s)<code>:'first'}</code>, you will again get error for marginal sum, because its string type.</p>
<p>What you ... | python|pandas|dataframe | 1 |
10,750 | 61,084,675 | Translate a big amount of data, pandas dataframe python | <p>I would like to translate the text of a column of my dataframe, the goal is to harmonize the data. I have text in Chinese, English, French, German, Spanish etc... I want to have all the text in English.
I have tried several things: with the googletrans API<br>
1)naively try to do it </p>
<pre><code>from googletran... | <p>I was working XML file for translate and I was getting this error "JSONDecodeError: Expecting value: line 1 column 1 (char 0)". When I searched this error, I encountered that some special characters fails translation. In this case <code>&nbsp; &amp; etc.</code> were problem for me. If there are special chara... | python|pandas|dataframe|google-translate|google-translation-api | 1 |
10,751 | 61,084,092 | How to assign rows a number based on a level in pandas dataframe? | <p>I have the following code:</p>
<pre><code>from pandas import DataFrame
import pandas as pd
data = {'City': ['NY', 'NY', 'Arizona'], 'Doctor': ['Dr. Prof. Vera', 'Dr. Prof. Vera', 'Dr. Martin'], 'Type': ['Checked', 'Checked', 'Ordered'], 'Covid-Patient': ['yes', 'no', 'no']}
df = DataFrame(data).set_index(['City', '... | <p>You can <code>rank()</code> index level <code>Doctor</code>:</p>
<pre class="lang-py prettyprint-override"><code>df['Dr-Nr.'] =df.assign(d_=df.index.get_level_values('Doctor'))['d_'].rank(method='dense').astype(int)
</code></pre>
<p>The order of indexing will be alphabetical here, so:</p>
<pre class="lang-py pret... | python|python-3.x|pandas|pandas-groupby | 0 |
10,752 | 61,143,998 | numpy best fit line with outliers | <p>I have a scatter plot of data that mostly fits a line, but with some outliers. I've been using numpy polyfit to fit a line to the data, but it will pick up the outliers and give me the wrong line output:</p>
<p><a href="https://i.stack.imgur.com/Xow0I.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.c... | <p>You can fit a linear model with the <a href="https://en.wikipedia.org/wiki/Huber_loss" rel="noreferrer">Huber loss</a>, which is robust towards outliers.</p>
<p>Full example using scikit learn:</p>
<pre><code>from sklearn.linear_model import HuberRegressor
from sklearn.preprocessing import StandardScaler
y = np.a... | python|numpy | 12 |
10,753 | 61,179,687 | return different values from different columns according to conditions pandas | <p>a snapshot of DF looks like this:</p>
<p><a href="https://i.stack.imgur.com/NofsU.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/NofsU.png" alt="dataframe"></a></p>
<pre><code>idf=pd.DataFrame({'p1': {549: 'Staffordshire_bullterrier', 1374: 'kelpie', 641: 'Samoyed'},
'p1_conf': {549: 0.6892590... | <p>It looks to me a perfect case for <code>np.select</code></p>
<h1><code>np.select</code></h1>
<pre class="lang-py prettyprint-override"><code>import pandas as pd
import numpy as np
condlist = [df["p1_dog"]==1,
((df["p1_dog"]==0) & (df["p2_dog"]==1)),
((df["p1_dog"]==0) & (df["p2_dog... | python|pandas|dataframe | 1 |
10,754 | 60,863,819 | How to split a sentence from dataframes with nltk library? | <p>I want to create bag of words models but with calculate <em>relative frequencies</em> with nltk package. My data is built with pandas dataframe.</p>
<p>Here is my data:</p>
<pre><code>text title authors label
0 On Saturday, September 17 at 8:30 pm EST, an e... Another Terrorist Attack in NYC…Why Are we ST... | <p><a href="https://www.nltk.org/api/nltk.tokenize.html" rel="nofollow noreferrer"><code>nltk.tokenize()</code></a> requires the input to be a string, you are getting the error because you are directly passing a pandas.Series object:</p>
<p>Try this to tokenize by words:</p>
<pre><code>data['Corpus'] = df.text.apply(... | python|pandas|nlp | 0 |
10,755 | 71,740,424 | Converting every other csv file column from python list to value | <p>I have several large csv filess each 100 columns and 800k rows. Starting from the first column, every other column has cells that are like python list, for example: in cell A2, I have [1000], in cell A3: I have [2300], and so forth. Column 2 is fine and are numbers, but columns 1, 3, 5, 7, etc, ...99 are similar to ... | <p>When reading the cells, for example <code>column_1[3]</code>, which in this case is <code>[4554.8433]</code>, python will read them as arrays. To read the numerical value inside the array, simply read the values like so:</p>
<pre><code>value = column_1[3]
print(value[0]) #prints 4554.8433 instead of [4554.8433]
</co... | python|pandas|numpy|csv | 0 |
10,756 | 71,609,761 | I would like to calculate the difference between two floats datatypes and hours within respective columns | <p>I would like to calculate the difference between enrollment fees that were paid within 24 hours and is less than 10000 USD each but when combined is greater than 10000 USD that was paid by specific/unqiue students</p>
<p>See below for columns/data dictionary in my dataset for clarity</p>
<ul>
<li><strong>Student ID ... | <p>I copied your 'student_information' table and added some rows for the sake of this answer.</p>
<pre><code>import pandas as pd
#Converted your table from the image you provide to excel.
student_information = pd.read_excel('/content/student_data_notebook picture.xlsx', 'Sheet1')
student_information.head(10)
</code></p... | python|pandas|dataframe | 0 |
10,757 | 71,573,440 | Create Sliding Window DataFrames with Timestamps | <p>I need to create dataframes using a sliding window for multiple 24-hour dataframes covering the span of <code>2021-01-01 00:00:00</code> to <code>2021-12-31 23:56:00</code>. Each interval between dataframes is 6 hours (hence start/end hours are 00,06,12,18). Doing this manually wouldnt be scalable, any input would b... | <p>One way to go about is to repeat your data 4 times, assign each with a label being the starting timestamp, then group:</p>
<pre><code>freq = 6
periods = 24 // freq
shifted = pd.to_timedelta(np.arange(0,24,freq), unit='H')
group = df.Timestamp.dt.floor(f'{freq}H')
groups = pd.concat([
df.assign(start=group-shi... | python|pandas|loops|datetime | 1 |
10,758 | 71,583,525 | Geopandas plots no points | <p>I want to plot points using Longitude and Latitude with Geopandas, but nothing gets plotted. How to fix this?</p>
<p><img src="https://i.stack.imgur.com/0OB17.png" alt="geopandas no plots" /></p> | <ul>
<li>it's never easy to answer a question when one has to use OCR to extract the data and code. Here's what I've managed to extract with OCR, there are some errors in sample points</li>
<li>this sample works as can be seen by output of <code>plot()</code></li>
<li>what is very clear from your output is the axes ma... | python|pandas|matplotlib|gis|geopandas | 0 |
10,759 | 71,582,835 | Convert different units of a column in pandas | <p>I'm working on a Kaggle project. Below is my CSV file column:</p>
<pre><code>total_sqft
1056
1112
34.46Sq. Meter
4125Perch
1015 - 1540
34.46
10Sq. Yards
10Acres
10Guntha
10Grounds
</code></pre>
<p>The column is of type object. First I want to convert all the values to float then update the string <code>1015 - 1540</... | <p>First extract numeric values by <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.extractall.html" rel="nofollow noreferrer"><code>Series.str.extractall</code></a>, convert to floats and get averages:</p>
<pre><code>df['avg'] = (df['total_sqft'].str.extractall(r'(\d+\.*\d*)')
... | python-3.x|pandas | 3 |
10,760 | 71,617,690 | pandas aggregate column doesnt exist? | <p>Currently i have a dataframe that i am preforming a group by on with aggregate functions. these are the functions</p>
<pre><code> aggregation_functions = {
'12_months': 'sum',
'24_months': 'sum',
'36_months': 'sum',
'number_36_months': 'sum'
}
</... | <p>Are you sure that id is a column and not an index?</p>
<p>You could try resetting the index of your DataFrame before you groupby:</p>
<pre><code>df = df.reset_index()
final_df = df.groupby(['buy_country', 'buy_activity', 'vd_country', 'vd_activity'], as_index=False).aggregate(aggregation_functions)
</code></pre> | python|python-3.x|pandas|dataframe | 1 |
10,761 | 69,891,068 | Python- delete rows where 2 columns are empty/null | <p>I need to drop all rows where 2 column values are null (both need to be empty). Code below deletes rows where either one is empty.</p>
<pre><code>df.dropna(subset=['name', 'toy'])
</code></pre>
<p>Code below doesnt delete anything</p>
<pre><code>df.dropna(axis=0, how='all', subset=['name', 'toy'])
</code></pre>
<p>A... | <p>Try this:</p>
<pre class="lang-py prettyprint-override"><code>df.drop(df[pd.isna(df['name']) & pd.isna(df['toy'])].index)
</code></pre> | python|pandas|drop | 0 |
10,762 | 69,691,945 | How can I make a for loop that creates an array from 2 columns from different excel files? | <p>I've been trying to learn python coming from MATLAB and I've been struggling with creating for loops. I have around 10 excel files (.csv) with 2 columns of CURR and VOLT. I'd like to be able to quickly make a vector (or are they called arrays?) of CURR and VOLT for all the excel files.</p>
<p>Here is what I'm curren... | <p>In your example the "data" variable has to be created before insert any value to it. You can create an empty list:</p>
<pre><code>data = []
</code></pre>
<p>And after in every loop you can append the pandas DataFrame to that list:</p>
<pre><code>files = ['BLUE' , 'BLUE.REV', 'GREEN', 'GREEN.REV', 'NITRO', ... | python|excel|pandas|numpy|matplotlib | 0 |
10,763 | 69,715,935 | Map Values from one dataframe to another based two criteria | <p>I have one dataframe (df) that contains home field advantage values for various conferences. The first columns represents the home teams and the remaining column headers represent the away teams. I would like to create a new column, <code>df1['hfa']</code> in the second dataframe (df1) that shows the home field adva... | <p>Based on what I undesrstand, this is what I have. See if this is what you looking for. Given Data:</p>
<p><a href="https://i.stack.imgur.com/Jhyos.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/Jhyos.png" alt="enter image description here" /></a></p>
<pre><code>(
df.melt(id_vars=['Home Team... | python|pandas|dataframe | 0 |
10,764 | 69,995,141 | How do I compute the cumlative sum of a column while skipping the first two rows? | <p>I have a pandas dataframe that looks like:</p>
<pre><code> capacity_gw marginal_cost chained_capacity
Case Category
CES - No Storage Hydro 4.277016 0.000000 NaN
Solar 9.774715 0.000000 ... | <p>Using tricks from <a href="https://stackoverflow.com/questions/54993050/pandas-groupby-shift-and-cumulative-sum">this answer</a>, either</p>
<pre class="lang-py prettyprint-override"><code># this transform may be slow for large dataframes
stack['chained_capacity'] = \
stack.groupby('Case')['capacity_gw'].transfo... | python|pandas|cumulative-sum | 1 |
10,765 | 69,887,692 | Python dictionary comprehension Scoping | <p>I am running the code below and getting a NameError.
When I run it one line at a time, it works, but when I wrap the lines inside a function, I get <em>NameError: name 'primes_cols' is not defined</em>. Why is the code below producing a NameError when the variables are defined?</p>
<pre><code>import pandas as pd
pr... | <p>The best approach (@juanpa.arrivillaga), as seen in the comments, Is to avoid eval</p>
<pre><code>import pandas as pd
primes = pd.DataFrame(columns = ['A','B','C','D'], data=[[3,5,7,11]])
tens = pd.DataFrame(columns = ['E','F','G','H'], data=[[10,20,30,40]])
evens = pd.DataFrame(columns = ['I','J','K','L'], data=[[... | python|pandas|dataframe|nameerror|dictionary-comprehension | 0 |
10,766 | 69,913,126 | how to covert ndarray object of numpy module to string in python | <p>I have ndarray object of modules and i want to covert it to a string.
for example i want to go from:</p>
<pre><code>[[array(['img_10.jpg'], dtype='<U22')]
[array(['img_11.jpg'], dtype='<U22')]
[array(['img_12.jpg'], dtype='<U22')]
[array(['img_13.jpg'], dtype='<U22')]]
</code></pre>
<p>to only a list ... | <p>You could just use a simple comprehension, because numpy arrays are iterables. Assuming that your list is <code>arr</code>:</p>
<pre><code>python_list = [k for a in arr for k in a]
</code></pre>
<p>Above assumed a simple array. Per your edit, you have a top 2D array of shaped (2338, 1). In that case, you will have t... | python|numpy | 1 |
10,767 | 43,259,276 | Python: Fast Hankel Transform for 1d array | <p>I'm trying to find any existing implementation for Hankel Transform in Python (actually i'm more into symmetric fourier transform of two 2d radially symmetric functions but it can be easily reduced to hankel transform).</p>
<p>I do know about <code>hankel</code> python module, but it requires lambda function for in... | <p>I'm the author of <a href="http://github.com/steven-murray/hankel" rel="noreferrer" title="hankel">hankel</a>. While I would not recommend to use my code in this case (since as you mention, it requires a callable input function, and its purpose is to accurately compute integrals, not to do a DHT), I will say that it... | python|numpy|transform|fft | 7 |
10,768 | 72,413,615 | On Device Training Tensorflow | <p>Is there a way to trained dataset taken from user image while running the apps? Dataset are collected based on images from user(take photo or select from gallery) on apps.</p>
<p>p/s: I found on-device training Tensorflow but based on documentation we have to trained the model in Python first and export to tensorflo... | <p>There are many ways, to predict with trainable weight, feedback, update weights or load model is possible. You may transfer learning from the current to another model.</p>
<p>There are many libraries including C++, C# wrapper, TFLite, TF and etc.</p>
<p><strong>[ Sample ]:</strong></p>
<pre><code>import pyaudio as p... | python|java|android|tensorflow|dataset | 0 |
10,769 | 50,254,058 | trying to aggregate multiple columns in groupy by using python similiar to SQL | <p>I am currently looking to aggregate a dataframe by many categorical columns and sum up several metric columns as well. I am trying to do this similiar to how I would in SQL but I cant seem to find a simple method. I also am not sure if I am at the limits of pandas group by as the code below returns a keyerror on the... | <p>Use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.core.groupby.DataFrameGroupBy.agg.html" rel="nofollow noreferrer"><code>agg</code></a> by <code>dictionary</code> of columns with aggregate functions, <code>DataFrame</code> contructor is not necessary:</p>
<pre><code>d = {'points':'mean', 'c... | python-3.x|pandas|group-by|aggregate|metrics | 2 |
10,770 | 50,261,144 | Merge subgroup into adjacent subgroup after groupby | <p>If we run the following code</p>
<pre><code>np.random.seed(0)
features = ['f1','f2','f3']
df = pd.DataFrame(np.random.rand(5000,4), columns=features+['target'])
for f in features:
df[f] = np.digitize(df[f], bins=[0.13,0.66])
df['target'] = np.digitize(df['target'], bins=[0.5]).astype(float)
df.groupby(fe... | <p>A simple way to do it is with a loop <code>for</code> on indexes meeting your condition:</p>
<pre><code>df_group = df.groupby(features)['target'].agg(['mean','count'])
# Fist reset_index to get an easier manipulation
df_group = df_group.reset_index()
list_indexes = df_group[df_group['count'] <=58].index.values #... | python|pandas|pandas-groupby | 1 |
10,771 | 50,666,614 | Pandas - Join two JSON in different structure the right way | <p>I know there are many similar problems, but still I have the feeling I'm doing so many things wrong here, I'm sure some of you can help me and give me some advise for the future, I tried many different ways but still doesn't work properly.</p>
<p>I have the two following data sources that I'd like to join with pand... | <p>Consider pandas' <a href="http://pandas.pydata.org/pandas-docs/version/0.17.0/generated/pandas.io.json.json_normalize.html" rel="nofollow noreferrer"><code>json_normalize</code></a> that can build dataframes from nested json data and then merge the two sets like any dataframe:</p>
<pre><code>import json as json
im... | python|json|pandas|dictionary|join | 2 |
10,772 | 50,323,160 | Python - How to sort except one index | <pre><code>columns=['NAME', 'AB', 'H']
import pandas as pd
df = pd.DataFrame([['Harper', '10', '5'], ['Trout', '10', '5'], ['Ohtani', '10', '5'], ['TOTAL', '30', '15']], columns=columns)
df1 = df.sort_values(by='NAME')
print(df1)
</code></pre>
<p>the result is</p>
<pre><code> NAME AB H
0 Harper 10 5
2 O... | <p>Try following code to sort the df by 'NAME' by excluding 'Total':</p>
<pre><code>df1 = df[df.NAME!='TOTAL'].sort_values(by='NAME')
</code></pre>
<p>Output:</p>
<pre><code> NAME AB H
0 Harper 10 5
2 Ohtani 10 5
1 Trout 10 5
</code></pre>
<p>You can append back the 'Total' after sorting by:</p>
<p... | python|pandas|sorting | 5 |
10,773 | 50,317,825 | Python For Loop DataFrame | <p>see below the following code I have at the moment and the error that is coming with it.</p>
<pre><code> companynames = []
for x in urls:
website_nametext = requests.get(x)
all_text = website_nametext.text
all_soup = BeautifulSoup(all_text, 'html.parser')
companynames.append(all_soup.h1.get_text())
o... | <p>Try taking out the square brackets around companynames in the pd.DataFrame call. With the brackets there it is a list of lists, albeit only one, but it effectively turns it through 90 degrees.</p> | python|python-3.x|pandas|for-loop|beautifulsoup | 0 |
10,774 | 45,282,357 | Pandas: replace values in dataframe from pivot_table | <p>I have <code>dataframe</code> and Pivot Table and I need to replace some values in <code>dataframe</code> from pivot_table's columns.</p>
<p>Dataframe:</p>
<pre><code> access_code ID cat1 cat2 cat3
g1gw8bzwelo83mhb 0433a3d29339a4b295b486e85874ec66 1 2
g0... | <p>As long as your dataframe is not exceedingly large, you can make it happen in some really ugly ways. I am sure someone else will provide you with a more elegant solution, but in the meantime this duct tape might point you in the right direction.</p>
<p>Keep in mind that in this case I did this with 2 dataframes ins... | python|pandas|dataframe|pivot-table | 0 |
10,775 | 62,697,633 | Percent change from one column to the next | <p>Let's say I had a dataframe:</p>
<pre><code>df = pd.DataFrame([[3, 2, 1], [5, 4, 2]])
3 2 1
5 4 2
</code></pre>
<p>I want to return a dataframe that has the percent change from one column to the next. So the above dataset would return:</p>
<pre><code>.666 .5
.8 .5
</code></pre>
<p>How would I accomplish this in p... | <p>Pandas has a function for this. Use <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pct_change.html" rel="nofollow noreferrer">pct_change</a>:</p>
<pre><code>df.pct_change(axis='columns')
</code></pre>
<p>This will output the 'percentage change' (per your question) from one colum... | python|pandas | 1 |
10,776 | 62,501,519 | Is there a way in Pandas to subtract two values that are in the same column that have the same name? | <p>Here is a snippet of a dataframe I'm trying to analyze. What I want to do is simply subtract FP_FLOW FORMATTED_ENTRY values from D8_FLOW FORMATTED_ENTRY values only if the X_LOT_NAME is the same. For example, in the X_LOT_NAME column you can see MPACZX2. The D8_FLOW FORMATTED_ENTRY is 12.3%. The FP_FLOW FORMATTED... | <p>it is advisable to first convert your data into a format where the values to be added / subtracted are in the same row, and after that subtract / add the corresponding oclumns. You can do this using <code>pd.pivot-table</code>. The below example will demonstrate this using a sample dataframe similar to what you've s... | python|pandas|dataframe | 0 |
10,777 | 62,647,950 | Faster way to check if a date is within a 5000+ element long list / numpy array? | <p>I have the following function that is called multiple times:</p>
<pre><code>def next_valid_date(self, date_object):
"""Returns next valid date based on valid_dates.
If argument date_object is valid, original date_object will be returned."""
while date_object not in se... | <p>I just created an alternate next_valid_date() method that calls .loc() on a pandas dataframe (the dataframe's index is a list of the valid dates, which is where the list of valid_dates comes from in the first place):</p>
<pre><code>def next_valid_date_alt(self, date_object):
while True:
try:
... | python|python-3.x|numpy|numpy-ndarray | 0 |
10,778 | 62,869,164 | Generate Numpy array of even integers that sum to a value | <p>Is there a numpy solution that would allow you to initialize an array based on the following conditions?</p>
<ol>
<li>Number of elements in axis 1. (In the example below you have 4 places in each element of the array)</li>
<li>Sum of values. (All elements sum to 8)</li>
<li>Step size. (Using increments of 2)</li>
</... | <p>Here is a small code that will enable you to loop over the desired combinations. It takes 3 parameter:</p>
<ul>
<li><code>itsize</code>: Number of elements.</li>
<li><code>itsum</code>: Sum of values.</li>
<li><code>itstep</code>: Step size.</li>
</ul>
<p>It may be necessary to optimize it if the computations you do... | python|numpy | 1 |
10,779 | 54,256,382 | Getting AttributeError during python library import | <p>I was using jupyter notebook using <code>pip</code> and all libraries were working fine. But from today, it suddenly started showing the following error on every library. I don't know what happened.</p>
<p>It is working fine in Conda.</p>
<pre><code>import pandas as pd
--------------------------------------------... | <p>have you update tesorflow to 1.12?
i encountered the same situation when i udate tensor flow to 1.12.
So i <code>pip install tensorflow==1.10.0</code> ,it worked.</p> | python|pandas|pip|jupyter-notebook|conda | 1 |
10,780 | 54,368,348 | OOM error even after clearing GPU Session | <p>I am applying CNN on a data set with 4684 images of size 2000*102. I am using 5 fold cross validation in keras for recording performance metrics. I am using <code>del.model()</code>, <code>del.histroy</code> and <code>K.clear_session()</code> but after 2 running two folds its giving OOM error. Please see the develop... | <p>A few suggestions considering the updates in the comments:</p>
<p>1) Create a bash script that launches the python scripts individually (after the process dies, the memory is freed) and have them write results to separate files that you can later process and join together. E.g use a bash script that iterates and fe... | tensorflow|gpu|conv-neural-network|cross-validation | 3 |
10,781 | 54,598,678 | How to create dynamically named dictionary in Python? | <p>I am looking to chunk a bunch of data from a dataframe. In order to do so, I need to define a dynamic name to a dictionary. </p>
<p>I would like to do something like:</p>
<pre><code>dict_{}.format(VARIABLE_NAME) = {}
</code></pre>
<p>The above shown is an illegal operation. How can I go about defining a new dicti... | <p>Instead of trying to generate a dynamic number of variable names on the fly, You should instead opt for a different higher level data-structure to store your objects, such as a dictionary or a list.</p>
<pre><code>import pandas as pd
REFERENCE_CODE = ["ladder_now", 0, 1, 5, 15, "country_satis", 20, 50, 100, "test3"... | python|pandas|dictionary | 1 |
10,782 | 73,605,708 | Save csv file in each iteration of loop python | <p>I want to know how can i save each CSV file in iteration on a pandas data frame.</p>
<p>Like i need to save each file as newcsv_1, newcsv_2, newcsv_3 .....</p>
<p>I have used it for loop but it is starting saving from 0 ex:</p>
<pre><code>for i in range(0, len(arr)):
df = pd.Dataframe(arr[i])
df.to_csv(&quo... | <pre class="lang-py prettyprint-override"><code>for i in range(1, len(arr) + 1):
df = pd.Dataframe(arr[i])
df.to_csv("newcsv_{}".format(i))
</code></pre> | python|pandas|dataframe | 0 |
10,783 | 73,550,332 | Is there an alternative to tf.one_hot()? | <p>I was trying to convert one hot encode tensors, but <code>tf.one_hot</code> takes too much memory and keeps crashing. I cannot use keras-> <code>to_catagorical</code> because I work with tensors. So I was wondering if there is an alternative to tf.one_hot or if there is any way to make it less resource intensive?... | <p>Since you use tensors <code>tf.keras.utils.to_categorical()</code> also return tensor you better try it, however you need to change the loss into <code>loss = "CategoricalCrossentropy"</code> and the <code>metrics = ["CategoricalAccuracy", ...]</code>.</p> | tensorflow|machine-learning|keras|deep-learning|nlp | 0 |
10,784 | 73,716,250 | What is the correct implementation for rounding a 2D tensor given the rounding values in a 1D tensor? | <p>This is what I have done so far:</p>
<pre><code>def round_values(predictions):
# Rounding values
rounded = torch.tensor([1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0])
# Find nearest rounding value
dif = predictions.unsqueeze(2) - rounded.view(1,1,-1)
indexes = torch.argmin(abs(dif), dim=2)
... | <p>A thousand times faster for a 100 x 100 array (on cpu)</p>
<pre class="lang-py prettyprint-override"><code>def round_values_bob(predictions):
return torch.clamp(torch.round(predictions * 2) / 2, 1, 5)
</code></pre> | python|pytorch|rounding|tensor | 2 |
10,785 | 73,657,115 | numpy alternative to successive indexing | <p>I'm sure this is probably a duplicate, but I couldn't find another question with exactly what I wanted so apologies in advance.</p>
<p>Given the following example array:</p>
<pre><code>array([[[ 0, 1],
[ 2, 3],
[ 4, 5]],
[[ 6, 7],
[ 8, 9],
[10, 11]],
[[12, 13],
... | <p>Is this what you're after?</p>
<pre><code>import numpy as np
a = np.array(
[[[ 0, 1],
[ 2, 3],
[ 4, 5]],
[[ 6, 7],
[ 8, 9],
[10, 11]],
[[12, 13],
[14, 15],
[16, 17]]]
)
result = a[0:2, [0, 2]]
print(result)
</code></pre>
<p>Result:</p>
<pre class="lang-none ... | python|arrays|numpy|indexing | 0 |
10,786 | 73,811,794 | Check if two words with same index position from 2 diff lists are in string - python | <p>I have 2 lists from a csv file.</p>
<p>column1 = ZIP codes<br />
column2 = City Name</p>
<p>I have a string; that contains some random text, and possibly zip codes and cities names.</p>
<p>I want to check for each i if column1[i] & colmun2[i] are in the string.</p>
<p>I used <a href="https://stackoverflow.com/qu... | <p>You can loop directly over the items instead of the indices and use the built-in zip function to loop over both the lists simultaneously -</p>
<pre class="lang-py prettyprint-override"><code>def op(): # this is your solution
collect = [] # I am collecting into a list instead of print for benchmark
for i in r... | python|python-3.x|pandas | 1 |
10,787 | 71,382,491 | PyTorch moving average computation creates inplace operation | <p>I have a loss function that depends on an "exponential moving average" <code>Z</code>. A minimal example (pay special attention to the <code>getUpdatedZ</code> function):</p>
<pre class="lang-py prettyprint-override"><code>import torch
import torch.nn as nn
class FeedForward(nn.Module):
def __init__(s... | <p>After some trials, I think that the error arises because you are computing a recursive function (<code>Z = getUpdatedZ(X, Z)</code>) but you are changing some of its parameters (the weights of the <code>Linear</code> modules) at each iteration through <code>optimizer.step()</code>.</p>
<p>You can <code>backward()</c... | pytorch | 1 |
10,788 | 71,426,725 | Using pandas how to sperate column from one column specific row from csv file | <p>my csv file like</p>
<p>A B D
EB AC 1
EF 2
MT 3</p>
<p>BM AC 1
EF 2</p>
<p>I want like
A MT
EB 3
BM NULL</p> | <pre><code>import pandas as pd
df = pd.read_csv(my_csv_path)
df = df[[my_list_of_column]]
</code></pre>
<p>it can be an easy way for your problem</p> | python|pandas|dataframe | 0 |
10,789 | 71,330,554 | Pass correct image shape to model that uses a conv1d and con2d in the same network | <p>so I am trying to implement the VGG network, everything in the paper, but i have when i am using the architecture that has a conv1-255 as part of it network. below is my code</p>
<pre><code>def _make_convo_layers(architecture) -> torch.nn.Sequential:
"""
Create convolutional layers ... | <p>just as jhso commented use are doing this wrong, looking that this VGG explanation on this <a href="https://neurohive.io/en/popular-networks/vgg16/" rel="nofollow noreferrer">VGG</a> page, what you need to do isn't to use a 1D convolution but to perform a convolution operation of kernel size 1 instead of using the o... | python|pytorch | 1 |
10,790 | 71,339,145 | how to extract digits from column names in a pandas dataframe? | <p>I have</p>
<pre><code>df.columns
Index(['location', 'date',
'deaths_2020_all_ages', 'average_deaths_2015_2019_all_ages',
'deaths_2015_all_ages', 'deaths_2016_all_ages', 'deaths_2017_all_ages',
'deaths_2018_all_ages', 'deaths_2019_all_ages', 'deaths_2010_all_ages',
'deaths_2011_all_ages',... | <p>Assuming you only want to extract the dates from the columns starting in "death", you could use a regex (here: first number or full string if not starting with "death"):</p>
<pre><code>df.columns = df.columns.str.extract('(\d+|^(?!death).*$)', expand=False)
</code></pre>
<p>columns before:</p>
<p... | python-3.x|regex|pandas|string|dataframe | 1 |
10,791 | 71,366,708 | Trouble with 'for' loop: Length of values (0) does not match length of index (52) | <p>I am newer to python, and am trying to write a 'for' loop to perform a calculation on one column in a dataframe and put the results in another column. However, I'm getting the following error:</p>
<pre><code>ValueError: Length of values (0) does not match length of index (52)
</code></pre>
<p>What is the problem in ... | <p>I'm not sure if I understand your calcuations but in <code>DataFrame</code> you can do it without <code>for</code>-loop</p>
<pre><code>df['Nitrogen recommendation (ppm)'] = ((1.6 * 150) - (df['OM'] * 20) - (df['n_ppm']) - float(0))
</code></pre>
<hr />
<p>And if you would need to use <code>for</code>-loop then you s... | python|pandas|for-loop | 1 |
10,792 | 71,125,857 | Extracting Info From A Column that contains irregular structure of ";" and "|" separators | <p>I have a pandas data frame in which one of the columns looks like this.</p>
<pre><code>INFO
SVTYPE=CNV;END=401233
SVTYPE=CNV;END=401233;CSQT=1|BHAT12|ESNT12345|
SVTYPE=CNV;END=401233;CSQT=1|JHV87|ESNT12345|,1|HJJUB2|ESNT12345|
SVTYPE=CNV;END=401233;CSQT=1|GFTREF|ESNT12345|,1|321lkj|ESNT12345|,1|16-YHGT|ESNT12345|...... | <p>IIUC, you could use a regex and <a href="https://pandas.pydata.org/docs/reference/api/pandas.Series.str.extractall.html" rel="nofollow noreferrer"><code>str.extractall</code></a>.</p>
<h4>joining to the original data:</h4>
<pre><code>new_df = df.join(
df['INFO']
.str.extractall(r'(\w+\|ESNT\d+)')[0]
.unstack(leve... | python|pandas | 0 |
10,793 | 52,192,148 | Divide image to single objects (coins) for machine learning | <p>I want to <strong>automatically divide an image</strong> of multiple coins to the single coins, so that afterwards the single coins can be put into a model that classifies the coins (with Tensorflow/Keras).</p>
<p><a href="https://i.stack.imgur.com/mbI0f.jpg" rel="nofollow noreferrer">The input images look something... | <p>I would try color segmentation similar to this <a href="https://pythonprogramming.net/color-filter-python-opencv-tutorial/" rel="nofollow noreferrer">tutorial</a> as a first step to separate the coins from the background. Here is my quick try in Python using OpenCV:</p>
<pre><code>import cv2
import numpy as np
impo... | python|tensorflow|machine-learning|keras|computer-vision | 1 |
10,794 | 60,686,581 | reg problems:TypeError: expected string or bytes-like object | <p>I am trying the code:</p>
<pre><code>`s='{"mail":vip@a.com,"type":"a","r_id":"1312","level":307},{"mail":vipx@a.com,"type":"b","r_id":"1111"}'`
data_raw=re.split(r'[\{\}]',s)
data_raw=data_raw[1::2]
data=pd.DataFrame(data_raw)
data[0]=str(data[0])
data['r_id']=data[0].apply(lambda x:re.search(r'(r_id)',data[0]))
da... | <p>My two cents...</p>
<pre><code>import re
pattern = re.compile(r'^.*?id\":\"(\d+)\",\"level\":(\d+).*id\":\"(\d+).*$')
string = r'{"mail":vip@a.com,"type":"a","r_id":"1312","level":307},{"mail":vipx@a.com,"type":"b","r_id":"1111"}'
data = pattern.findall(string)
data
</code></pre>
<p>Which returns an array:</p>... | python|regex|pandas | 0 |
10,795 | 60,607,394 | How to set dynamically "range" for google sheet when add data from panda | <p>I would like to append data from pandas to google sheets using google sheets api. checking the document, it require parameter <em>range</em>. so far I set the max possible value in google sheets, see similar post <a href="https://stackoverflow.com/questions/58467792/how-to-set-cells-range-dynamically-in-google-sheet... | <p>As written in the <a href="https://developers.google.com/sheets/api/reference/rest/v4/spreadsheets.values#ValueRange" rel="nofollow noreferrer">documentation</a>,</p>
<blockquote>
<p>When appending values, this field represents the range to search for a table, after which values will be appended.</p>
</blockquote... | python-3.x|pandas|google-sheets|google-sheets-api | 1 |
10,796 | 60,509,186 | How to find the number of value in range of integers in a column of a data frame in python pandas | <p>So i have this big dataframe with alot of columns like age, name, sex, etc. </p>
<p>I want to make a new column with age group between 1-10, 11-20, 21-30,...,71-80</p>
<p>I tried to do </p>
<pre><code>ranges = [1, 10, 20, 30, 40, 50, 60, 70, 80]
df.age.groupby(pd.cut(df.age, ranges)).count()
</code></pre>
<p>and... | <p>I think first is necessary explain by comment of @samthegolden:</p>
<blockquote>
<p>(10, 20] means "between 10 and 20, excluding 10 and including 20" due to the parenthesis format. </p>
</blockquote>
<p>But you can do it by <code>labels</code> parameter created by <code>ranges</code> with <code>zip</code> in lis... | python|pandas|numpy | 1 |
10,797 | 60,575,527 | Resample data monthly in python | <p>I have a large csv file example below,</p>
<pre><code>data = pd.read_csv('C:/Users/Ene_E/Desktop/Data/data.csv')
data.head()
</code></pre>
<hr>
<pre><code> name year value
0 Afghanistan 1800 603
1 Albania 1800 667
2 Algeria 1800 715
3 Andorra 1800 1200
4 Angola 180... | <p>Conditionally assign name for where date values are missing using <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.where.html" rel="nofollow noreferrer">np.where</a></p>
<pre><code>data['value']=np.where(data['date'].isna(), 'This column is interpolated smoothly', '')#.data.ffill... | python|pandas | 0 |
10,798 | 60,595,614 | Keras predict accuracy does not match to fit's result | <p>I'm trying to build a binary classification model by TensorFlow 2.0 + Keras. Each target have <code>5</code> features and I want this model can predict whether the input data is belong to <code>a</code>.</p>
<p>However, the accuracy is totally different between <code>fit()</code> and <code>predict()</code>. The mos... | <p>After inspecting your source code, there are a few implementation issue:</p>
<blockquote>
<ol>
<li>Training data and validation data are left randomized by Keras</li>
</ol>
</blockquote>
<p>During your training, 20% of the data is sampled to be the validation data, but you wouldn't know if the data sampled i... | python|tensorflow|machine-learning|keras | 5 |
10,799 | 60,514,720 | I'm getting the following error message: ValueError: cannot reindex from a duplicate axis | <p>Why am I getting this error message? My code:</p>
<pre><code>my_df.loc[my_df['col1'] < my_df['col2'],'col3'] = my_df['col1'].
</code></pre>
<p>Basically what I'm trying to do is set col3 equal to col1 whenever col3 is less than col2. Thanks!</p> | <p>I solved this by resetting the index on the dataframes with reset_index(). The index got "messed up" due to a prior concat function. </p> | python|pandas|valueerror | 0 |
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