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 |
|---|---|---|---|---|---|---|
6,400 | 66,241,524 | How can I scrape a table's header that contains an image? | <p>I'm trying to scrape a table from a wiki website, by using pandas library, the header consists of 5 parts: name, stars, image, health, notes.<br />
I successfully scraped name, stars and notes, but the "Health" header has an image instead of a string name.<br />
(I would like to display "Health" ... | <pre><code>df = url[1]
df.rename(columns={'Unnamed: 3':'Health'}, inplace=True)
</code></pre>
<p>result:</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th style="text-align: right;"></th>
<th style="text-align: left;">Name</th>
<th style="text-align: right;">Image</th>
<th style="text-align: ... | html|python-3.x|pandas|dataframe|web-scraping | 0 |
6,401 | 66,006,228 | 3D CNN using keras-tensorflow on pycharm ( Process finished with exit code 137 (interrupted by signal 9: SIGKILL) ) | <p>I'm doing a 3D CNN to classify LUNA16 data set (CT scan data set), I'm using keras-tensorflow on pycharm.</p>
<p>I'm following this code
<a href="https://github.com/keras-team/keras-io/blob/master/examples/vision/3D_image_classification.py" rel="nofollow noreferrer">https://github.com/keras-team/keras-io/blob/master... | <p>Right now the code is running flawlessly on google colab.</p>
<p>I think the limitation was with the GPU (my gpu is RTX 2080ti) vs google colab gpu Nvidia T4.</p>
<p>I just preprocessed the data and saved it as a numpy array, then uploaded the arrays to google colab, and run the code after preprocessing.
Now everyth... | python|tensorflow|keras|deep-learning | 0 |
6,402 | 66,115,893 | Multiple instances of tensorflow lite with NNAPI delegate | <p>NNAPI Delegate in Tensorflow lite uses shared memory for input and output tensors of the graph. However the name of the shared memory pool is hardcoded (<code>"input_pool"</code> and <code>"otput_pool"</code>):</p>
<pre><code> // Create shared memory pool for inputs and outputs.
nn_input_memor... | <p>The name given to the shared name is just used as a label. Using the same name when creating two different shared memory regions won't cause the same memory to be used. See for example <a href="https://cs.android.com/android/platform/superproject/+/master:system/core/libcutils/ashmem-dev.cpp;l=370" rel="nofollow nor... | tensorflow|tensorflow-lite|nnapi | 1 |
6,403 | 46,251,557 | Tensorflow : Is there way to feed the crop value (central fraction) to tf.image.central_crop? | <p>I want to central crop images, with different crop fraction for each image.</p> | <p>Lets say that you have the images saved in a list called <code>images</code> where <code>images[0]</code> is the first one and so on. Lets assume that the central crop fractions lie on the list called <code>central</code> where the <code>central[0]</code> is the fraction that you want for the first image and so on f... | python|tensorflow | 0 |
6,404 | 46,257,905 | pandas_datareader not working in jupyter-notebook (Anaconda) | <p>ModuleNotFoundError Traceback (most recent call last)
in ()
3 from matplotlib import style
4 import pandas as pd
----> 5 import pandas_datareader.data as web
6
7 style.use('ggplot')</p>
<p>ModuleNotFoundError: No module named 'pandas_datareader'</p> | <p>data reader gotta be installed separetly.</p>
<p>if using anacoda, try:</p>
<pre><code>conda install -c https://conda.anaconda.org/anaconda pandas-datareader
</code></pre> | python-3.x|pandas|anaconda|jupyter-notebook | 4 |
6,405 | 58,253,826 | Eigen values and vectors calculation error in python | <p>I'm trying to obtain the eigenvectors and values of any matrix 'X' in a specific format. I used the <code>linalg</code> function to get the eigen pairs but the expected output format is different from my result. For example, <code>v</code> and <code>e</code> denote the eigenvalues and eigenvectors. <code>v1 = 1</cod... | <p><strong>np.linalg.eigh</strong></p>
<p>First, one should note that <code>np.linalg.eigh</code> calculates the eigenvalues of a Hermitian matrix -- this will not apply for all matrices. If you want to calculate the eigenvalues of any matrix <code>X</code> you should probably switch to something like <code>np.linalg.... | python|numpy|matrix|eigenvalue|eigenvector | 0 |
6,406 | 58,550,023 | Pandas apply not working inside Spark parallelized code | <p>I am trying to use Pandas "apply" inside the parallelized code but the "apply" is not working at all. Can we use "apply" inside the code which gets distributed to the executors while using Spark (parallelize on RDD)?</p>
<p>Code:</p>
<pre><code>def testApply(k):
return pd.DataFrame({'col1':k,'col2':[k*2]*5})
... | <p>I am also getting the same error (TypeError: an integer is required (got type bytes)</p>
<pre><code>from pyspark.context import SparkContext
TypeError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_11288/3937779276.py in <module>
----> 1 from pyspark.contex... | python|apache-spark|pyspark|apply|pandas-apply | 0 |
6,407 | 69,286,904 | How to get evenly-spaced data quickly with a MultiIndex in pandas | <p>I have a dataframe indexed by stock ticker and date that's pretty sparse, something like:</p>
<pre><code>df = pd.DataFrame({
'ticker': ['SPY', 'GOOGL', 'GOOGL', 'TSLA', 'TSLA'],
'date': ['2021-01-01', '2021-09-01', '2021-09-21', '2021-09-21', '2021-09-22'],
'price': [430.0, 2500.0, 2600.0, 700.0, 710.0],... | <p>You'll probably see a huge improvement in performance using an <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.merge_asof.html" rel="nofollow noreferrer"><code>asof</code> merge</a>. First create all the rows you need from the cartesian product of unique ticker labels and the last three da... | python|pandas | 3 |
6,408 | 44,740,150 | Measure correlation without counting some values | <p>I have an array:</p>
<pre><code>a = np.array([[1,2,3], [0,0,3], [1,2,0],[0,2,3]])
</code></pre>
<p>which looks like:</p>
<pre><code>array([[1, 2, 3],
[0, 0, 3],
[1, 2, 0],
[0, 2, 3]])
</code></pre>
<p>I need to calculate paired correlations, <strong>but without</strong> taking <code>0</code>... | <p>Actually, i decieded to change all zeros in data to <code>np.nan</code></p>
<pre><code>for i,e_i in enumerate(array_data):
for j, e_j in enumerate(e_i):
if e_j == 0:
array_data[i,j] = np.NaN
</code></pre>
<p>and then, <code>pandas.corr()</code> worked fine...</p> | python|numpy|correlation | 0 |
6,409 | 44,770,573 | Python pandas large database using excel | <p>I am comfortable using python / excel / pandas for my dataFrames . I do not know sql or database languages . </p>
<p>I am about to start on a new project that will include around 4,000 different excel files I have. I will call to have the file opened saved as a dataframe for all 4000 files and then do my math on th... | <p>You definitely want to use a database. At nearly 2GB of raw data, you won't be able to do too much to it without choking your computer, even reading it in would take a while. </p>
<p>If you feel comfortable with python and pandas, I guarantee you can learn SQL very quickly. The basic syntax can be learned in an hou... | python|pandas | 2 |
6,410 | 60,760,573 | Multiply 2 different dataframe with same dimension and repeating rows | <p>I am trying to multiply two data frame</p>
<p>Df1</p>
<pre><code>Name|Key |100|101|102|103|104
Abb AB 2 6 10 5 1
Bcc BC 1 3 7 4 2
Abb AB 5 1 11 3 1
Bcc BC 7 1 4 5 0
</code></pre>
<p>Df2</p>
<pre><code>Key_1|100|101|102|103|104
AB 10 2 1 5 1
BC 1 10 ... | <p>You can <code>rename</code> the df2 <code>Key_1</code> to <code>Key</code>(similar to df1) , then set index and <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.mul.html" rel="nofollow noreferrer"><code>mul</code></a> on <code>level=1</code></p>
<pre><code>df1.set_index(['Name','... | python-3.x|pandas|dataframe | 4 |
6,411 | 61,051,911 | How to ensure each worker use exactly one CPU? | <p>I'm implementing <a href="https://arxiv.org/abs/1910.06591" rel="nofollow noreferrer">SEED</a> using ray, and therefore, I define a <code>Worker</code> class as follows</p>
<pre class="lang-py prettyprint-override"><code>import numpy as np
import gym
class Worker:
def __init__(self, worker_id, env_name, n):
... | <p>You can pin your core at each worker. For example, you can use something like psutil.Process().cpu_affinity([i]) to pin an index i core at each worker. </p>
<p>Also, before you pin your cpu, make sure to know what cpu has been assigned to the worker by this api. <a href="https://github.com/ray-project/ray/blob/203c... | python|numpy|ray | 2 |
6,412 | 71,787,733 | Pandas filter/subset columns based on conditions | <p>I have about 300 columns that are basically encoding of categorical variables. I'd like to drop columns where <code>sum</code> of values of column is <code><</code>, say 3.</p>
<pre><code>import pandas as pd
df = pd.DataFrame({
'id': [0, 1, 2, 3, 4, 5],
'col1': [0, 0, 0, 0, ... | <p>You can use <code>loc</code> to use a boolean indexing on the columns:</p>
<pre><code>N = 3
out = df.loc[:, df.sum(axis=0) > N]
</code></pre>
<p>If <code>id</code> is not actually numeric or if <code>N</code> can be a very large number, then maybe <code>set_index</code> with <code>id</code> first, then use boolea... | python|pandas | 2 |
6,413 | 71,465,857 | How to conditionally aggregate a Pandas dataframe | <p>I have a dataframe with some data that I'm going to run simulations on. Each row is a datetime and a value. Because of the nature of the problem, I need to keep the original frequency of 1 hour when the value is above a certain threshold. When it's not, I could resample the data and run that part of the simulation o... | <p>So I found a solution.</p>
<pre><code>grouped = df.resample("1D")
def conditionalAggregation(x):
if x['v'].max() <= 3:
idx = [x.index[0].replace(hour=0, minute=0, second=0, microsecond=0)]
return pd.DataFrame(x['v'].max(), index=idx, columns=['v'])
else:
return x
conditi... | python|pandas|dataframe | 1 |
6,414 | 42,221,022 | Pandas subtract all values from one value, move to next value and repeat | <p>I have a df with two columns 'a' and 'b' </p>
<pre><code>[a] [b]
11 100
2 100
10 100
</code></pre>
<p>What I need is an extra column 'c', which represents following calculation:</p>
<p>((11-2) + (11-10)) / 100</p>
<p>((2-11) + (2-10)) / 100</p>
<p>((10-11) + (10-2)) / 100</p>
<pre><code>[a] [b] [c]
11 ... | <p>I'll give a numpy example. For</p>
<pre><code>>>> a = numpy.array([11, 2, 10])
>>> b = numpy.array([100, 100, 100])
</code></pre>
<p>you can do</p>
<pre><code>>>> c = (len(a) * a - sum(a)) / b
</code></pre>
<p>Similar for a pandas data frame.</p> | python|pandas|dataframe|sum | 2 |
6,415 | 69,782,130 | Set alignment of columns in pandastable | <p>I am trying to set different alignment types for different columns in <a href="https://pandastable.readthedocs.io/en/latest/" rel="nofollow noreferrer">pandastable</a>.</p>
<p>Meanwhile I have found a way to set the alignment for the complete table using the global configuration as shown here (in this example, defau... | <p>You can use <code>pt.columnformats['alignment'][colname]</code> to set the alignment of an individual column with name <code>colname</code>.</p>
<p>For example, to change the alignment for the <code>label</code> column (one of the columns in the data model returned by <code>.getSampleData()</code>:</p>
<pre class="l... | python|tkinter|pandastable | 2 |
6,416 | 69,673,717 | Python: numpy.sum returns wrong ouput (numpy version 1.21.3) | <p>Here I have a 1D array:</p>
<pre><code>>>> import numpy as np
>>> a = np.array([75491328, 75491328, 75491328, 75491328, 75491328, 75491328, 75491328, 75491328,
75491328, 75491328, 75491328, 75491328, 75491328, 75491328, 75491328, 75491328,
75491328, 75491328, 754... | <p>The problem is caused by integer overflow, you should change the datatype of <code>np.array</code> to <code>int64</code>.</p>
<pre><code>import numpy as np
np.array([Your values here], dtype=np.int64)
</code></pre> | python|arrays|numpy | 1 |
6,417 | 72,437,909 | Playing with my Apple Health Data but the csv is harder to wrangle than I have encountered | <p>My pandas dataframe comes out as:</p>
<pre><code> BURNED CALORIES
Date 00 - 01 01 - 02 02 - 03 .... 23 - 24
1/13/19 17.6 11.53 3.24 28.6
1/14/19 1.5 1.46 2.41 27.44
</code></pre>
<p>The top row is the hourly breakdown but the only column is... | <p>IIUC, is that what you're looking for?</p>
<pre><code>df2=df.melt(id_vars='Date', var_name='variable')
df2.rename(columns={'variable':'hours', 'value':'calories burned'}, inplace=True)
df2
</code></pre>
<pre><code> Date hours calories burned
0 1/13/19 00-01 17.6
1 1/14/19 00-01 1.5
2 1/13/19 01-02 ... | pandas|time|time-series | 1 |
6,418 | 50,317,538 | calculate end time from start time and duration (minutes) using pandas. Error on standard approach | <p>i have a pandas dataframe:</p>
<p>Start_time | Duration(minutes) </p>
<p>2018-03-01 16:37:09 | 155 </p>
<p>2018-03-01 07:02:10 | 5 </p>
<p>2018-03-01 13:07:09 | 250 </p>
<p>2018-03-01 20:46:34 | 180 </p>
<p>2018-03-01 07:45:49 | 5</p>
<p>I want output as</p>
<p>Start_time | End time
2018-03-01 16:37:0... | <p>You need change <code>DatetimeIndex</code> to <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html" rel="nofollow noreferrer"><code>to_datetime</code></a> for remove first error:</p>
<blockquote>
<p>Cannot compare type 'Timestamp' with type 'int' ** </p>
</blockquote>
<pre><code... | python|pandas | 3 |
6,419 | 45,584,907 | Flatten layer of PyTorch build by sequential container | <p>I am trying to build a cnn by sequential container of PyTorch, my problem is I cannot figure out how to flatten the layer.</p>
<pre><code>main = nn.Sequential()
self._conv_block(main, 'conv_0', 3, 6, 5)
main.add_module('max_pool_0_2_2', nn.MaxPool2d(2,2))
self._conv_block(main, 'conv_1', 6, 16, 3)
main.add_module('... | <p>This might not be exactly what you are looking for, but you can simply create your own <code>nn.Module</code> that flattens any input, which you can then add to the <code>nn.Sequential()</code> object:</p>
<pre><code>class Flatten(nn.Module):
def forward(self, x):
return x.view(x.size()[0], -1)
</code><... | python|conv-neural-network|pytorch | 18 |
6,420 | 45,713,159 | print mismatch items in two array | <p>I want to compare two array(4 floating point)and print mismatched items.
I used this code:</p>
<pre><code>>>> from numpy.testing import assert_allclose as np_assert_allclose
>>> x=np.array([1,2,3])
>>> y=np.array([1,0,3])
>>> np_assert_allclose(x,y, rtol=1e-4)
AssertionError:
N... | <p>Just use</p>
<pre><code>~np.isclose(x, y, rtol=1e-4) # array([False, True, False], dtype=bool)
</code></pre>
<p>e.g.</p>
<pre><code>d = ~np.isclose(x, y, rtol=1e-4)
print(x[d]) # [2]
print(y[d]) # [0]
</code></pre>
<p>or, to get the indices</p>
<pre><code>np.where(d) # (array([1]),)
</code></pre> | python-3.x|numpy | 7 |
6,421 | 62,877,986 | AttributeError: module 'pandas' has no attribute 'df' | <p>For a current project, I am planning to clean a Pandas DataFrame off its Null values. For this purpose, I want to use <code>Pandas.DataFrame.fillna</code>, which is apparently a solid soliton for data cleanups.</p>
<p>When running the below code, I am however receiving the following error <code>AttributeError: modul... | <p>When you load the file to the pandas - in your code the <code>data</code> variable is a <code>DataFrame</code> instance. However, you made a typo.</p>
<pre><code>df = pd.json_normalize(data)
df = df.fillna()
</code></pre> | python|pandas|dataframe | 2 |
6,422 | 71,231,466 | Muliply only numeric values from object type column pandas | <pre><code>df = pd.DataFrame({'col1': [1, 2, 4, 5, 'object']})
df['col1'] * 5
</code></pre>
<p>this code multiplies 'object' string to 5 and writes the string 5 times but I want to multiply only numeric values strings should be leave as it is.
I have also tried to convert column to numeric using to_numeric with errors=... | <p>Use <a href="https://pandas.pydata.org/docs/reference/api/pandas.Series.str.isnumeric.html#pandas.Series.str.isnumeric" rel="nofollow noreferrer"><code>isnumeric</code></a> to identify rows with numerics values.</p>
<pre><code>import pandas as pd
df = pd.DataFrame({'col1': [1, 2, 4, 5, 'object']})
mask_ = df['col1... | python|pandas | 0 |
6,423 | 52,273,275 | regex replacing multiple codes with values in a column | <p>I have a dataframe(df) with different columns. One of the column (col1) is as follows:</p>
<pre><code> col1
----
0 1
1 2
2 1-2
3 1,2
4 1-3
5 3
</code></pre>
<p>I am using .replace method in python/pandas to replace the codes in col1 using the code:</p>
<pre><code> df.col1.replace(to_replace=({'... | <p>Repeating your work I don't seem to get the same error as you do with input </p>
<p><code>df = pd.DataFrame({'col1' : ['1', '2', '1-2', '1,2', '1-3', '3']})</code></p>
<p>and applying the same .replace method:</p>
<p><code>df.col1.replace(to_replace=({'1':'Normal','2':'1-2 more than normal','3':'3-4 more than nor... | python|string|pandas|dictionary | 0 |
6,424 | 52,046,971 | sigmoid_cross_entropy loss function from tensorflow for image segmentation | <p>I'm trying to understand what the <code>sigmoid_cross_entropy</code> loss function does with regards to image segmentation neural networks:</p>
<p>Here is the relevant Tensorflow source <a href="https://github.com/tensorflow/tensorflow/blob/600caf99897e82cd0db8665acca5e7630ec1a292/tensorflow/python/ops/nn_impl.py#L... | <p><code>sigmoid_cross_entropy_with_logits</code> is used in multilabel classification.</p>
<p>The whole problem can be divided into binary cross-entropy loss for the class predictions that are independent(e.g. 1 is both even and prime). Finaly collect all prediction loss and average them.</p>
<p>Below is an example:... | python|tensorflow|machine-learning|neural-network|deep-learning | 7 |
6,425 | 52,422,265 | Python MultiIndex Column Rename | <p>I am a <strong>NEWBIE</strong> to panda framework. I tried various things shown below and I Want to rename a column name in pandas dataframe , can some one please guide me with this. The column is multilevel pivot column. </p>
<pre><code>import pandas as pd
import numpy as np
df = pd.read_excel(r'D:\cod_sheets\18_0... | <p>You just need to add the argument to <code>df_pivot.rename(columns = {"CDS_STATUS":"CDS"})</code> that is <code>df_pivot.rename(columns = {"CDS_STATUS":"CDS"},inplace=True)</code></p> | python|pandas|pivot-table|pandas-groupby | 1 |
6,426 | 52,107,106 | Visualizing cumsum python | <p>This is related to post <a href="https://stackoverflow.com/questions/52104500/rolling-difference-using-pandas">Rolling Difference using Pandas</a></p>
<p>Now that I have this dataframe below, i am trying to visualize this. </p>
<pre><code>Item Add Subtracts Month Net_Items Monthly_Available_Items
C 68 ... | <p>You can add a legend by specifying the <code>label</code></p>
<pre><code>ax2 = sns.barplot(x = 'Month', y = 'Monthly_Available_Items',
data = stack_df, color = 'purple',
label = "Monthly_Available_Items")
ax2.legend() # will show the legend for the barplot... | python|pandas|matplotlib|seaborn | 0 |
6,427 | 60,478,009 | Convert pandas DataFrame to 2-layer nested JSON using groupby | <p>Assume that I have a pandas dataframe called <code>df</code> similar to:</p>
<pre><code>source tables
src1 table1
src1 table2
src1 table3
src2 table1
src2 table2
</code></pre>
<p>I'm currently able to output a JSON file that iterates through ... | <p>Is this what you're looking for?</p>
<pre><code>data = [
{k: v}
for k, v in df.groupby('source')['tables'].agg(
lambda x: {v: {} for v in x}).items()
]
with open('data.json', 'w') as f:
json.dump(data, f, indent=2)
</code></pre>
<p>There are two layers to the answer here. To group the table... | python|json|pandas|dataframe|object | 1 |
6,428 | 72,769,631 | How use two columns as a single condition to get results in pyspark | <p>I have:</p>
<pre><code>+-----------+------+
|ColA |ColB |
+-----------+------+
| A | B|
| A | D|
| C | U|
| B | B|
| A | B|
+-----------+------+
</code></pre>
<p>and I want to get:</p>
<pre><code>+-----------+------+
|ColA |ColB |
+-----------... | <p>Need add parentheses and <code>|</code> for bitwise OR:</p>
<pre><code>pandasDF[(pandasDF["colA"]!="A") | (pandasDF["colB"]!="B")]
sparkDF.where((sparkDF['colA'] != 'A') | (sparkDF['colB'] != 'B')).show()
</code></pre> | pandas|apache-spark|pyspark | 1 |
6,429 | 72,799,969 | install tensorflow-decision-forests in windows | <p>I have to install tensorflow-decision-forests in windows. I tried:</p>
<pre><code>pip install tensorflow-decision-forests
pip3 install tensorflow-decision-forests
pip3 install tensorflow_decision_forests --upgrade
</code></pre>
<p>I get:</p>
<pre><code>ERROR: Could not find a version that satisfies the requirement t... | <p><code>tensorflow-decision-forests</code> 0.2.6 <a href="https://pypi.org/project/tensorflow-decision-forests/#files" rel="nofollow noreferrer">provides</a> binary wheels only for Linux and no source code. So <code>pip</code> cannot install it on non-Linux platforms.</p>
<p>There're <a href="https://github.com/tensor... | python|tensorflow|pip|tensorflow-decision-forests | 1 |
6,430 | 72,805,397 | How to create a new dataframe that contains the value changes from multiple columns between two exisitng DFs | <p>The first two tables represent snippets from the first and second dataframes, respectively. I am trying to create a new dataframe that contains the numerical changes for each attribute</p>
<p><img src="https://i.stack.imgur.com/2o0vM.png" alt="" /></p>
<p>Please also see my other post for how I framed the same quest... | <p>You can try setting <code>ID</code> and <code>Name</code> columns as index then do substraction</p>
<pre class="lang-py prettyprint-override"><code>out = (df2.set_index(['ID', 'Name']) - df1.set_index(['ID', 'Name'])).reset_index()
</code></pre>
<pre><code>print(df1)
ID Name A B
0 1 a 89 91
print(df2... | python|pandas|dataframe | 0 |
6,431 | 61,663,005 | Working with pandas to select/extract rows from dataframe | <p>I am trying to extract information from a number of countries from a dataset I downloaded. I was able to figure out how to pull one country, but have had syntax errors trying to pull more than one in the same line. Here it is with the output:</p>
<pre><code>ef=df1.loc[df1['countries'] == 'Hong Kong']
print(ef)
<... | <p>If you want to filter the dataframe based on multiple countries you can use <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.isin.html" rel="nofollow noreferrer"><code>Series.isin</code></a>.</p>
<pre><code>country_list = ['Hong Kong', 'US', 'Canada', 'India', 'Russia']
ef = df1[df1... | python|pandas|csv|dataframe | 2 |
6,432 | 61,964,496 | Pandas - json normalize inside dataframe | <p>I want to break down a column in a dataframe into multiple columns. </p>
<p>I have a dataframe with the following configuration:</p>
<pre><code>
GroupId,SubGroups,Type,Name
-4781505553015217258,"{'GroupId': -732592932641342965, 'SubGroups': [], 'Type': 'DefaultSite', 'Name': 'Default Site'}",OrganisationGr... | <p>You can try using <code>ast</code> package:-</p>
<pre><code>import pandas as pd
import ast
data = [[-4781505553015217258,"{'GroupId': -732592932641342965, 'SubGroups': [], 'Type': 'DefaultSite', 'Name': 'Default Site'}","OrganisationGroup","CompanyXYZ"],
[-4781505553015217258,"{'GroupId': 8123255835936628631, '... | json|pandas|dataframe | 0 |
6,433 | 62,020,666 | Is there any way to insert image logo/ Text in before saving to_html in pandas | <p>I am saving pandas output as to_html()</p>
<p>Is there any way to integrate the logo/Text at the top of the html page before saving.</p> | <p><a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_html.html" rel="nofollow noreferrer"><code>to_html</code></a> returns a string with the html if the first parameter <code>buf</code> is <code>None</code>. You can than prepend your image or text html to this string and then write... | pandas | 1 |
6,434 | 61,657,329 | How to resample a pandas dataframe to hourly mean, taking into account both a time and a column with a string value? | <p>I am trying to make an hourly mean of a dataframe in python, by taking into account the date info but also string info in a certain column. Please see the example below.</p>
<pre><code> station time temperature
0 EHAM 2020-01-01 13:30:00 2
1 EHAM 2020-01-01 13:50:00 5
2 ... | <p>Aggregate <code>mean</code> with convert <code>datetimes</code> to dates by <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.dt.date.html" rel="nofollow noreferrer"><code>Series.dt.date</code></a>:</p>
<pre><code>#convert sampel data to numeric
df['temperature'] = df['temperature'].a... | python|string|pandas|dataframe|datetime | 1 |
6,435 | 61,806,786 | How to create multiple columns when map the column data in pandas | <p>I am trying to create three additional columns from one column , I have datetime and categorial data , I want to show the number of categories contain each row for example </p>
<p><strong>I have the date, categories and count. This is the dataframe</strong></p>
<p><a href="https://i.stack.imgur.com/k5l8d.png" rel=... | <p>You can make use of pandas <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pivot.html" rel="nofollow noreferrer">pivot</a></p>
<p>Considering your original dataframe containing columns <code>CreatedDate</code>, <code>Categories</code> and <code>count</code></p>
<pre><code>cate... | python|python-3.x|pandas | 0 |
6,436 | 58,019,367 | Selectively update dataframe column with a dictionary map | <p>I want to use a dictionary to map / replace row values in a pandas column - but only for a subset of rows based on a criteria</p>
<pre><code>df['var'] = df['var'].map(mydict)
</code></pre>
<p>but only where </p>
<pre><code>df['anothervar'] is somevalue
</code></pre>
<p>Can I do this?</p> | <p>Check with <code>np.where</code> </p>
<pre><code>df['var'] = np.where(df['anothervar']=='somevalue',df['var'].map(mydict),df['var'])
</code></pre> | python|pandas | 0 |
6,437 | 55,017,220 | Getting "key error" while plotting using Pandas | <p>I am trying to do a simple plot of a data from a text file. Below is the file:</p>
<pre><code>Date,Open,High,Low,Close
03-10-16,774.25,776.065002,769.578768,772.559998
04-10-16,776.03,778.710022,772.890015,776.429993
05-10-16,779.30,782.070007,775.650024,776.469971
06-10-16,779.00,780.479989,775.539978,776.859985
0... | <p>This modification will work. You misunderstood how to use df.plot(). Please refer this page <a href="https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html" rel="nofollow noreferrer">visualization</a>. This code below is just a basic visualization, you can change to <strong>df.plot.box()</strong>... | python-3.x|pandas | 0 |
6,438 | 54,769,135 | assign new value to repeated (or multiple) objective element(s) to a pandas dataframe | <p>I have a pandas dataframe:</p>
<pre><code>df = pd.DataFrame({'AKey':[1, 9999, 1, 1, 9999, 2, 2, 2],\
'AnotherKey':[1, 1, 1, 1, 2, 2, 2, 2]})
</code></pre>
<p>I want to assign a new value to a specific column and for each element having a specific value in that column.</p>
<p>Let say I want to assign the new v... | <p>You can use loc in this way:</p>
<pre><code>df.loc[df["AKey"]==9999, "AKey"] = 8888
</code></pre>
<p>Producing the following output:</p>
<p><a href="https://i.stack.imgur.com/OtxfV.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/OtxfV.png" alt="enter image description here"></a></p>
<p>With yo... | python|pandas|pandas-loc | 1 |
6,439 | 54,873,983 | merge consecutive matching rows | <p>I want to merge all consecutive rows by matching all 'X' fields and concatenating the 'Y' field.</p>
<p>Below is sample data -</p>
<pre><code>[Y X1 X2 X3 X4 X5
A NaN -3810 TRUE None None
B NaN -3810 TRUE None None
C NaN -3810 TRUE None None
D NaN -3810 None None None
E ... | <p>A little bit tricky , using <code>shift</code> create the groupkey , then <code>agg</code> </p>
<pre><code>df.fillna('NaN',inplace=True) # notice here NaN always no equal to NaN, so I replace it with string 'NaN'
df.groupby((df.drop('Y',1)!=df.drop('Y',1).shift()).any(1).cumsum()).\
agg(lambda x : ','.join(x) ... | python|pandas | 3 |
6,440 | 55,120,541 | How can I evaluate the accuracy loss of the converted ftlite model? | <p>I trained a model based on the ssd-moblienet algorithm.And use the eval.py script to evaluate the mAP of the model.</p>
<p>I need to use this model on iOS, so I converted it to a tflite model and it works now.</p>
<p>I want to analyze the precision loss when converting a model by the mAP value before and after the... | <p>There isn't any official script. You'll have a write a custom script which uses Tensorflow's metrics API found at <code>model/research/object_detection/metrics</code> passing the detections and groundtruth as arguments.</p>
<p>An example</p>
<pre><code># Append to $PYTHONPATH path to models/research and cocoapi/Pyth... | tensorflow|object-detection|tensorflow-lite | 3 |
6,441 | 49,607,116 | can anyone explain how does this code work? pandas in python | <pre><code>`
for column in list(df.columns[df.isnull().sum() > 0]):
mean = df[column].mean()
df[column].fillna(mean,inplace=True)
df.info()`
</code></pre>
<p>i don't understand the first line of code, i mean, ... <strong>list</strong>() ... does it means everything in the parenthesis will be return to the ... | <p><code>list</code> here convert <code>Index</code> (columns names with at least one <code>NaN</code>) to <code>list</code>:</p>
<pre><code>df = pd.DataFrame({'A':list('abcdef'),
'B':[4,5,4,5,5,4],
'C':[7,8,9,4,2,3],
'D':[1,np.nan,5,7,1,0],
'... | pandas | 0 |
6,442 | 49,366,765 | How can I read endlessly from a Tensorflow tf.data.Dataset? | <p>I'm switching my old datalayer (using Queues) to the "new" and recommended Dataset API. I'm using it for the first time, so I'm providing code examples in case I got something fundamentally wrong.</p>
<p>I create my Dataset from a generator (that will read a file, and provide n samples). It's a small dataset and n_... | <p>Datasets have <a href="https://www.tensorflow.org/api_docs/python/tf/data/Dataset#repeat" rel="nofollow noreferrer"><code>repeat</code></a> and <a href="https://www.tensorflow.org/api_docs/python/tf/data/Dataset#shuffle" rel="nofollow noreferrer"><code>shuffle</code></a> methods.</p>
<pre class="lang-py prettyprint... | tensorflow|tensorflow-datasets | 3 |
6,443 | 49,344,432 | ValueError: Cannot feed value of shape (64,) for Tensor 'x:0', which has shape '(?, 100, 100, 3)' | <p>From skimage import io, transform:</p>
<pre><code> import os
import tensorflow as tf
import numpy as np
import time
import glob
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
path = 'D:/data/datasets/flower_photos/'
model_path = 'D:/data... | <p>Since <strong>w = 100, h = 100, c = 3</strong> and you have defined your input placeholder <code>x</code> as follows,</p>
<pre><code>x=tf.placeholder(tf.float32,shape=[None,w,h,c],name='x')
</code></pre>
<p><code>x</code> has the shape of <strong>(?,100,100,3)</strong>. Here <strong>?</strong> refer the batch size... | python|tensorflow | 3 |
6,444 | 73,349,869 | Not a gzipped file error when reading a gzipped file | <p>I saved a gzip parquet file:</p>
<pre><code>df.to_parquet("meth_450_clin_all_kipan.parquet.gz", compression="gzip")
</code></pre>
<p>And then I want to load it as matrix:</p>
<pre><code>matrix = pd.read_table('../input/meth-clin-kipan/meth_450_clin_all_kipan.parquet.gz')
</code></pre>
<p>Tracebac... | <p>The solution is to read the file using <code>read_parquet</code> and then convert it into a numpy array.</p>
<pre><code>matrix = pd.read_parquet('../input/meth-clin-kipan/meth_450_clin_all_kipan.parquet.gz').to_numpy()
</code></pre> | python|pandas|gzip | 1 |
6,445 | 67,334,116 | Shift rows in array independently | <p>I want to shift each row by its row-number and be relative to my desired output shape. An example:</p>
<pre><code>array([[0, 1, 2], array([[0, 1, 2], array([[0, 1, 2, 0, 0],
[1, 2, 3], -> [1, 2, 3], -> [0, 1, 2, 3, 0],
[2, 3, 4]]) ... | <p>Here is a solution for square matrices of size n.</p>
<p><code>np.concatenate((A,np.zeros((n,n))),axis=1).flatten()[0:-n].reshape([n,2*n-1])</code></p> | python|arrays|numpy | 2 |
6,446 | 60,225,723 | Ordering dataframe columns row by row | <p>I have a df that looks like this</p>
<pre><code>df = pd.DataFrame({'A' : ['1','2','3','7'],
'B' : [7,6,5,4],
'C' : [5,8,7,1],
'v' : [1,9,9,8]})
df=df.set_index('A')
df
B C v
A
1 7 5 1
2 6 8 9
3 5 7 9
7 4 1 8
</code>... | <p>If performance is important use <a href="https://numpy.org/doc/1.18/reference/generated/numpy.sort.html" rel="nofollow noreferrer"><code>numpy.sort</code></a> with <code>axis=1</code>:</p>
<pre><code>df[['B','C']] = np.sort(df[['B','C']], axis=1)
print (df)
B C v
A
1 5 7 1
2 6 8 9
3 5 7 9
7 ... | python|pandas | 4 |
6,447 | 60,196,260 | How to use keras ImageDataGenerator flow_from_directory given a map from image name to class label? | <p>Folder's structure is like following:</p>
<pre><code>-dataset
|---train_set
| |-- img01.jpg
| |-- img02.jpg
| |-.....
|
|---val_set
</code></pre>
<p>I don't have subfolders in train_set whose name is class label. However I have a dictionary from image name to class label like <cod... | <p>Keras ImageDataGenerator class also offers <a href="https://keras.io/preprocessing/image/#flow_from_dataframe" rel="nofollow noreferrer">flow_from_dataframe()</a> method, that should do the job. </p>
<p>Check out this <a href="https://medium.com/datadriveninvestor/keras-imagedatagenerator-methods-an-easy-guide-550e... | image-processing|keras|tensorflow2.0 | 0 |
6,448 | 65,313,416 | Getting pandas dataframe from json file | <p>I would like to flatten a JSON file to create a pandas DataFrame. The json output is:</p>
<pre><code>{
'info': {
'status': [
],
'weightcorp': {
'weight': 4.0
}
},
'results': [
{
'instrument': 'A',
'ts': [
{
'date': '2020-12-10',
'indicato... | <p>Let's try a custom function to your data:</p>
<pre><code>def flatten(d):
'''
remove all intermediate dictionaries and list
'''
ret = dict()
for k, v in d.items():
# case when value is a dictionary
if isinstance(v, dict):
sub = flatten(v)
for kk, vv in sub.i... | json|pandas|list|dataframe|dictionary | 2 |
6,449 | 65,461,750 | tensorflow.python.framework.errors_impl.UnknownError: Failed to rename; : Access is denied. ; Input/output error | <p>I am not able to download and load tensorflow dataset on my Windows 10 machine. It works okay on Google colab. Can someone please help me?</p>
<p><strong>Code:</strong></p>
<pre><code>import tensorflow_datasets as tfds
datasets, info = tfds.load("imdb_reviews", as_supervised=True, with_info=True)
</code><... | <p>Had this issues and upgrading the tensorflow-datasets solved this</p>
<pre><code>pip install -U tensorflow-datasets
</code></pre>
<p>this will replace the tensorflow-datasets-1.2.0 and install tensorflow-datasets-4.2.0</p>
<p>working on windows 10</p> | python|tensorflow|tensorflow2.x | 3 |
6,450 | 65,271,478 | Failed to save list of DataFrames to multisheet Excel spreadsheet | <p>I was trying to do the same thing with the question <a href="https://stackoverflow.com/questions/14225676/save-list-of-dataframes-to-multisheet-excel-spreadsheet">here</a>, and followed the method given by the answers. Here is my code:</p>
<pre><code>import pandas as pd
import xlsxwriter
mylist=[df_1,df_2,df_3]
fo... | <p>You have some pieces inside the loop that should be outside. Reference the docs here: <a href="https://xlsxwriter.readthedocs.io/example_pandas_multiple.html" rel="nofollow noreferrer">https://xlsxwriter.readthedocs.io/example_pandas_multiple.html</a></p>
<pre><code>writer = pd.ExcelWriter('data.xlsx', engine='xlsxw... | python|excel|pandas | 1 |
6,451 | 49,969,539 | Adding a name (string) column to an existing pandas DF | <p>I have an array(of float data type) from a FITS file of (1, 5000) dimension. I have created a pandas DF from it so that I can export it as a csv later. <a href="https://i.stack.imgur.com/CXCjB.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/CXCjB.png" alt="The data frame from the data"></a>. Howev... | <pre><code>FSC0029m4226 = pd.DataFrame(np.array(FSC0029m4226).byteswap().newbyteorder())
</code></pre> | python|string|python-2.7|pandas|dataframe | 0 |
6,452 | 63,906,723 | Keras image_dataset_from_directory not finding images | <p>I want to load the data from the directory where I have around 5000 images (type 'png'). But it returns me an error saying that there are no images when obviusly there are images.
This code:</p>
<pre><code>width=int(wb-wa)
height=int(hb-ha)
directory = '/content/drive/My Drive/Colab Notebooks/Hair/Images'
train_ds =... | <p>I have found the answer so I am posting in case it might help someone.</p>
<p>The problrem is the path, as I was using the path to the folder with the images whereas I should have used the directory (one folder above).</p>
<pre><code>directory = '/content/drive/My Drive/Colab Notebooks/Hair'
</code></pre>
<p>Note th... | tensorflow|keras|tensorflow-datasets|image-preprocessing | 14 |
6,453 | 46,914,292 | How to do reinforcement learning with an LSTM in PyTorch? | <p>Due to observations not revealing the entire state, I need to do reinforcement with a recurrent neural network so that the network has some sort of memory of what has happened in the past. For simplicity let's assume that we use an LSTM. </p>
<p>Now the in-built PyTorch LSTM requires you to feed it a an input of sh... | <p>Maybe you can feed your input sequence in a loop to your LSTM. Something, like this:</p>
<pre><code>h, c = Variable(torch.zeros()), Variable(torch.zeros())
for i in range(T):
input = Variable(...)
_, (h, c) = lstm(input, (h,c))
</code></pre>
<p>Every timestep you can use (h,c) and input to evaluate action ... | recurrent-neural-network|backpropagation|reinforcement-learning|pytorch | 1 |
6,454 | 46,859,478 | Update columns in df2 based on df1 on index of date | <p>I want to to have a data frame <strong>df2</strong> that will contain the values from <strong>df1</strong>.
Both data frames have an index of date.
Both data frames contain the same columns. I just want to update the columns of df2 if the index of df2 exists in df1. </p>
<p><strong>df1</strong> </p>
<pre><code>Sym... | <p>You can try to merge on indices:</p>
<pre><code>df3 =df1.merge(df2, left_index=True, right_index=True, suffixes=("","_"), how='right')
df3= df3.drop(['K1_', 'K2_', 'K3_'], axis=1).fillna(0)
</code></pre> | python|pandas | 1 |
6,455 | 63,314,009 | fill NaN values of a df under condition | <p>I have a resampled df:</p>
<pre><code> Timestamp Loading Power Energy ID status
2020-04-09 06:45:00 1.0 1000 5000 1 on
2020-04-09 06:46:00 1.0 1000 5500 1 on
2020-04-09 06:47:00 NaN ... | <p>Use for loop like this</p>
<p>df["status"]=[df["status"].values[i-1] if pd.isna(x) else x for i,x in enumerate (df["status"] .values) ]</p> | python|pandas | 0 |
6,456 | 63,257,389 | Pandas Boolean Indexing to Compare DataFrame and Results in List of Dicts | <p>I have the below dataframes</p>
<pre><code>import pandas as pd
import numpy as np
df1 = pd.DataFrame([[70, np.nan, "hello"], [89, 3, 4], [210, 5, 64], [11, 75, 8]], columns=["ID", "A", "B"], dtype='object')
df2 = pd.DataFrame([[70, np.nan, "world"], [89, 33, 44], [... | <p>Let us try <code>where</code>, also I recommend output series not dict</p>
<pre><code>s=df2.set_index('ID').where(diff_mask.drop('ID',1).values).stack()
Out[74]:
ID
70 B world
89 A 33
B 44
21 B 6
11 A 7
dtype: object
</code></pre>
<p>to dict</p>
<pre><code>d=[y.unstack().rese... | python|pandas|numpy|dataframe | 4 |
6,457 | 63,089,129 | Question on restoring training after loading model | <p>Having trained for 24 hours, the training process saved the model files via <code>torch.save</code>. There was a power-off or other issues caused the process exited. Normally, we can load the model and continue training from the last step.</p>
<p>Why should not we load the states of optimizers (Adam, etc), is it nec... | <p>Yes, you can load the model from the last step and retrain it from that very step.</p>
<p>if you want to use it only for inference, you will save the state_dict of the model as</p>
<pre><code>torch.save(model, PATH)
</code></pre>
<p>And load it as</p>
<pre><code>model = torch.load(PATH)
model.eval()
</code></pre>
<p... | pytorch | 2 |
6,458 | 62,968,276 | Apply an Encoder-Decoder (Seq2Seq) inference model with Attention | <p>Hello a <strong>StackOverflow</strong> community!</p>
<p>I'm trying to create an inference model for a <strong>seq2seq</strong> (<em>Encoded-Decoded</em>) model with <strong>Attention</strong>. It's a definition of the inference model.</p>
<pre><code>model = compile_model(tf.keras.models.load_model(constant.MODEL_PA... | <p>I think you also need to take the encoder output as output from the encoder model and then give it as input to the decoder model as the attention part requires it. Maybe this changes could help-</p>
<pre><code>model = compile_model(tf.keras.models.load_model(constant.MODEL_PATH, compile=False))
encoder_input = model... | python|tensorflow|keras|seq2seq|encoder-decoder | 0 |
6,459 | 67,859,542 | API to get mean and standard deviation for TFLite Models | <p>How can we get these values of mean and standard deviation for any TFLite model ? Is there any API to fetch mean and std deviation ? where is this information stored ?</p>
<p>How can TFLite users know with what values they have to normalize the input ?
Can these values obtained run time ?</p>
<p>To change these mean... | <p>If you're talking about the quantization parameters (mean and std to convert float to int), see <a href="https://www.tensorflow.org/lite/api_docs/java/org/tensorflow/lite/Tensor.QuantizationParams" rel="nofollow noreferrer">https://www.tensorflow.org/lite/api_docs/java/org/tensorflow/lite/Tensor.QuantizationParams</... | python|tensorflow|tensorflow2.0|tensorflow-lite | 0 |
6,460 | 67,889,133 | What is the sql equivalent function of the python function .size()? | <p>I am trying to solve a problem on bigquery; list of customers with consistent transactions for 6 months. I already solved it with python but I don't know how to replicate the code on sql. This is the code</p>
<pre><code>df.groupby(['Month','accounttoken'])['transactionid'].value_counts()
a=df[df.groupby(['Month','ac... | <p>count(*) is counting the number of rows in a group.</p>
<pre><code>SELECT count(*) as num_transactions
FROM data
GROUP BY Month, accounttoken, name
HAVING count(*) >= 5
</code></pre>
<p>You can use these SQL Query for the replacement of last two line of python code given by you. I hope SQL query given by you is a... | python|pandas|google-bigquery | 0 |
6,461 | 67,684,718 | How to display `.value_counts()` in interval in pandas dataframe | <p>I need to display <code>.value_counts()</code> in interval in pandas dataframe. Here's my code</p>
<pre><code>prob['bucket'] = pd.qcut(prob['prob good'], 20)
grouped = prob.groupby('bucket', as_index = False)
kstable = pd.DataFrame()
kstable['min_prob'] = grouped.min()['prob good']
kstable['max_prob'] = grouped.max(... | <p>Use named aggregation for simplify your code, for counts is used <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.size.html" rel="nofollow noreferrer"><code>GroupBy.size</code></a> to new column <code>counts</code> and is apply function for column <code>bucket</code>:</p... | python|pandas|dataframe | 1 |
6,462 | 61,522,733 | Reshape model input LSTM | <p>Hello everyone i'm trying to build a model to predict emotion in speech.
Since the audio have different lengths the feature matrixes also have different lengths and therefore i have a variable timestep.
I read on other answers that i can leave the input shape of LSTM as follows: <code>model.add(LSTM(1, input_shape=(... | <p>I am also new to this but from what I have found:
The input to LSTM is 3-D [samples,timesteps,features]
For more information, please visit this link :
<a href="https://stats.stackexchange.com/questions/264546/difference-between-samples-time-steps-and-features-in-neural-network">https://stats.stackexchange.com/questi... | python|tensorflow|keras|lstm|recurrent-neural-network | 0 |
6,463 | 61,581,042 | Make a new column based on other columns id values - Pandas | <p>How can i make new columns based on another columns id values? </p>
<p>The data look like this.</p>
<pre><code>value id
551 54089
12 54089
99 54089
55 73516
123 73516
431 73516
742 74237
444 74237
234 74237
</code></pre>
<p>I want the datase... | <p>Use <code>groupby</code> with <code>unstack</code>:</p>
<pre><code>df = df.groupby('id')['value'].apply(lambda x: pd.Series(x.tolist(),
index=['v1', 'v2', 'v3']))\
.unstack()
# or
df.groupby('id')['value'].apply(lambda x: pd.Da... | python|pandas|dataset | 3 |
6,464 | 68,657,888 | Pandas Taking the Nlargest ignoring 0 | <p>Hi is there a way in Pandas to do nlargest but only take values excluding 0? For example if we have [5, 0, 0] and we want to take the 2 largest, then it would only return 5 because the other values are 0?</p> | <p>You can simply remove the value 0 before you do nlargest:</p>
<pre><code>l = pd.Series([5,0,0])
l[l != 0].nlargest(2)
0 5
dtype: int64
</code></pre>
<p>Similarly, if you have a list of numbers you want to exclude:</p>
<pre><code>l[~l.isin(list_of_excluded_numbers)].nlargest(2)
</code></pre> | pandas | -1 |
6,465 | 53,299,543 | Is there any method in tensorflow like get_output in lasagne | <p>I found that it is easy to use lasagne to make a graph like this.</p>
<pre class="lang-py prettyprint-override"><code>import lasagne.layers as L
class A:
def __init__(self):
self.x = L.InputLayer(shape=(None, 3), name='x')
self.y = x + 1
def get_y_sym(self, x_var, **kwargs):
y = L.get_output(self... | <p>I'm not familiar with lasagne but you should know that ALL of TensorFlow uses graph based computation (unless you use tf.Eager, but that's another story). So by default something like:</p>
<p><code>net = tf.nn.conv2d(...)</code> </p>
<p>returns a reference to a Tensor object. In other words, <code>net</code> is N... | tensorflow|theano|lasagne | 0 |
6,466 | 52,996,993 | reshaping daily data on intraday values pandas | <p>I have a DF that looks like this:</p>
<pre><code> Last
1996-02-26 09:31:00 65.750000
1996-02-26 09:32:00 65.890625
1996-02-26 09:33:00 NaN
1996-03-27 09:31:00 266.710000
1996-03-27 09:32:00 266.760000
1996-03-27 09:33:00 266.780000
</code></pre>
<p>I want to ... | <p>If your index is <code>str</code> dtype, create a MultiIndex and call <code>unstack</code>:</p>
<pre><code>idx = pd.MultiIndex.from_arrays(zip(*df.index.str.split()))
df = df.set_index(idx)['Last'].unstack(0)
print(df)
1996-02-26 1996-03-27
09:31:00 65.750000 266.71
09:32:00 65.890625 266.... | python|pandas | 2 |
6,467 | 65,756,217 | custom sorting values of dataframe | <p>I want to custom sort my dataframe. This is sample dataframe which has structure like mine:</p>
<pre><code>data = {'name':['name1','name1','name1','name2','name2','name2','name3','name3','name3'],
'col1':[19, 38, 25, 10, 39, 28, 25, 20, 23],
'col2':[29, 28, 25, 20, 19, 18, 15, 10, 13],
'col3'... | <p>If I understand, you want to sort groups of three rows:</p>
<pre><code>groups = df.reset_index().loc[:,"date":].groupby(lambda x: int(x/3))
df[:] = groups.apply(lambda x: x.sort_values("name", ascending = False)).values
print(df)
# date name col1 col2 col3
#0 2020-12-31 name3 2... | pandas|dataframe|sorting | 0 |
6,468 | 65,654,800 | Plot from matplotlib and pandas is not working with data json file because ValueError | <p>I tried generate a simple plot, but error here.
My code:</p>
<pre><code>import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
real_estates = pd.read_json('/Users/pablo/Desktop/project/REscraper/real_estates.json')
# print(real_estates)
plt.plot(real_estates.size, real_estates.price)
plt.show()
... | <p><a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.size.html" rel="nofollow noreferrer" title="Pandas docs">size</a> happens to be an attribute of a DataFrame. Use a <a href="https://docs.python.org/3/reference/expressions.html#subscriptions" rel="nofollow noreferrer" title="Python docs">subscrip... | python|json|pandas|matplotlib | 0 |
6,469 | 65,751,866 | pandas pivot table issue - assuming it is how i am structuring it? | <p>i have a dataset that contains video game platforms, and the year that games were released for it.</p>
<p>what i'm trying to do is end up with a dataframe that has the count of titles for each year released by platform.</p>
<p>my initial dataframe looks like this:</p>
<pre><code>platform year
0 Wii 2006.0
1... | <p>Here is the solution with pivot table:</p>
<pre><code>res = pd.pivot_table(df,index=['year', 'platform'],aggfunc = 'size')
>>> print(res)
year platform
1984.0 NES 1
1985.0 NES 1
1989.0 GB 1
1990.0 SNES 1
1996.0 GB 1
1999.0 GB 1
2004.0 PS2 ... | python|pandas|pandas-groupby|pivot-table | 1 |
6,470 | 65,578,017 | Error: Error when checking input: expected dense_Dense1_input to have 3 dimension(s). but got array with shape 1,9 | <p>Im really new to tensorflow.js and im trying to do simple model that tell you on what side of the canvas you cliked</p>
<pre><code>const model = tf.sequential();
model.add(
tf.layers.dense({
units: 200,
activation: "sigmoid",
inputShape: [0, 1],
})
... | <p>The inputShape is of size 2, therefore, the features (here xtrain and xtest) should be of dimension 3.</p>
<p>Additionnally, it does not make sense to have a dimension size to be 0 (that will mean that the tensor is empty).</p>
<p>Given your xtrain and xtest shape, <code>[a, b]</code>, the inputShape, should be <cod... | javascript|tensorflow|tensorflow.js | 0 |
6,471 | 63,515,884 | Classify text from a Pandas Series | <p>I have the following dataframe which is built from parsing raw text files into a list and then into the dataframe.</p>
<pre><code> Content
0 POLITICS
1 A Renewed Pus... | <h2>Addresses: <em>At some point the pattern changes</em></h2>
<ul>
<li>Using a list comprehension, find data for each header
<ul>
<li>The order of list creation matters, <code>time</code>, <code>top</code>, and then <code>head</code>.</li>
<li>The <code>time</code> pattern must be consistent with 2 character time zone... | python|pandas | 3 |
6,472 | 53,458,023 | for loop for row wise comparison in pandas | <p>I have following pandas dataframe</p>
<pre><code>code tank prod_receipt tank_prod
12345 1 MS MS
23452 2 MS No Data
23333 2 HS HS
14567 3 MS No Data
12343 2 MS MS
</code></p... | <p>Dont use loops, because slow, better here is use <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.select.html" rel="nofollow noreferrer"><code>numpy.select</code></a>:</p>
<pre><code>m1 = df['tank_prod'] == 'No Data'
m2 = df['prod_receipt'] == df['tank_prod']
df['new'] = np.select([m1, m2], ['No ... | python|pandas | 4 |
6,473 | 71,893,469 | Converting a dataframe stringcolumn into multiple columns and rearrange each column based on the labels | <p>I want to convert a stringcolumn with multiple labels into separate columns for each label and rearrange the dataframe that identical labels are in the same column. For e.g.:</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th>ID</th>
<th>Label</th>
</tr>
</thead>
<tbody>
<tr>
<td>0</td>
<td... | <p>Try:</p>
<pre class="lang-py prettyprint-override"><code>df = df.assign(xxx=df.Label.str.split(r"\s*,\s*")).explode("xxx")
df["Col"] = df.groupby("xxx").ngroup()
df = (
df.set_index(["ID", "Label", "Col"])
.unstack(2)
.droplevel(0, axi... | python|pandas|dataframe|sorting | 1 |
6,474 | 55,224,109 | Custom upsampling of images with TensorFlow | <p>I have trouble implementing a layer function in TensorFlow. Maybe someone with more experience has an idea how to solve this. The usage of the function should be the following:</p>
<p>In: a <code>[B x W x H x 2]</code> tensor called <code>A</code></p>
<p>Out: a new tensor called <code>B</code> of size <code>[B x p... | <p>You can do that in a vectorized way (which should be much faster than looping or mapping) like this:</p>
<pre><code>import tensorflow as tf
import numpy as np
def gaussian_upsampling(A, p, q):
s = tf.shape(A)
B, W, H, C = s[0], s[1], s[2], s[3]
# Add two dimensions to A for tiling
A_exp = tf.expand... | python|tensorflow | 1 |
6,475 | 56,519,204 | How to compute a moving average of value for the previous date untill the first event? | <p>I have a dataframe with data where the first column is an identification number, ID1, the second is a date, DATE, and the third is some value, VALUE.</p>
<pre><code>d = {'ID1': [1,2,3,4,1,2,4,1,3,2,4,1],
'DATE': ['1/06/2016', '1/06/2016','2/06/2016','1/06/2016','3/06/2016', '4/06/2016','2/06/2016','5/06/2016... | <p>I think you are looking for <code>expanding</code></p>
<pre><code>s=df.groupby('ID1').VALUE.expanding(min_periods=1).mean().reset_index(level=0,drop=True)
df['new']=s
</code></pre> | python|pandas|moving-average | 1 |
6,476 | 66,964,560 | How do I calculate percentage change of a timeseries of daily data | <p>I have a daily timeseries of indexdata and want to take yearly pct changes of it. If I use <code>DataFrame.pct_change(periods=...)</code> I will have to define the exact number of days till the same day last year which is not correct as the number of working days differs from year to year. Do anyone have any idea ho... | <p><strong>EDIT</strong>: starting from the good answer from @Pablo C: given OP's definition of the DataFrame, we first need to convert the index to <code>DatetimeIndex</code>, otherwise @Pablo C's answer will throw <code>NotImplementedError: Not supported for type Index</code></p>
<pre><code>import pandas as pd
list=... | python|pandas|percentage | 0 |
6,477 | 68,107,160 | fill all nan values in a dataframe with values from a column | <p>I have a df like this</p>
<pre><code>index a b c
0 0 0 1
1 nan 1 2
2 0 1 3
3 1 nan 4
4 1 0 5
5 nan 0 6
6 nan nan 7
</code></pre>
<p>I want to fill the first 2 columns(actually fi... | <p>Just do</p>
<pre><code>out = df.fillna(dict.fromkeys(list(df),df.c))
Out[206]:
a b c
0 0.0 0.0 1
1 2.0 1.0 2
2 0.0 1.0 3
3 1.0 4.0 4
4 1.0 0.0 5
5 6.0 0.0 6
6 7.0 7.0 7
</code></pre> | python|pandas|dataframe | 2 |
6,478 | 59,081,876 | How to overcome the u200d unicode while reading excel files using pandas | <p>The excel file contains Indian language data. The excel file is being read but while displaying the content it shows \u200d in between. I need to avoid it to do further processing of data. Kindly help.</p> | <p>try this </p>
<pre><code>s = 'This is some \u200d text that has to be cleaned\u200d! it\u200d annoying!'
s.encode('ascii', 'ignore')
output :
This is some text that has to be cleaned! it annoying!'
</code></pre> | python|pandas|nlp | 0 |
6,479 | 59,305,007 | Compute dataframe columns from a string formula in variables? | <p>I use an excel file in which I determine the names of sensor, and a formula allowing me to create a new "synthetic" sensor based on real sensors. I would like to write the formula as string like for example "y1 + y2 + y3" and not "df ['y1'] + df ['y2'] + df ['y3]" but I don't see which ... | <p>I think pandas.query is what you want. <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html" rel="nofollow noreferrer">https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html</a></p>
<p>Example:</p>
<pre><code>formula_cell = "y1 + y2 + y3 + ... | python|pandas|dataframe|variables|formula | 1 |
6,480 | 46,129,407 | How to extract a item from list of items in a python dataframe column? | <p>I have a Dataframe like this :</p>
<pre><code> Date sdate
0 2012-3-12 [2012, 03, 12]
1 2012-3-25 [2012, 03, 25]
2 2012-4-20 [2012, 04, 20]
3 2012-4-12 [2012, 04, 12]
4 2012-4-26 [2012, 04, 26]
</code></pre>
<p>I need to extract the year,month and day to separate columns like this </p>
<pre><c... | <p>Use <code>apply</code> with <code>pd.Series</code> and <code>rename</code> the columns</p>
<pre><code>In [784]: df.sdate.apply(pd.Series).rename(columns={0:'year',1:'month',2:'day'})
Out[784]:
year month day
0 2012 3 12
1 2012 3 25
2 2012 4 20
3 2012 4 12
4 2012 4 26
</c... | python|pandas|for-loop|dataframe | 2 |
6,481 | 46,134,201 | How to get original values after using factorize() in Python? | <p>I'm a beginner trying to create a predictive model using Random Forest in Python, using train and test datasets. train["ALLOW/BLOCK"] can take 1 out of 4 expected values (all strings). test["ALLOW/BLOCK"] is what needs to be predicted. </p>
<pre><code>y,_ = pd.factorize(train["ALLOW/BLOCK"])
y
Out[293]: array([0, ... | <p>First, you need to save the <code>label</code> returned by <code>pd.factorize</code> as follows:</p>
<pre><code>y, label = pd.factorize(train["ALLOW/BLOCK"])
</code></pre>
<p>And then after you got the numeric predictions, you can extract the corresponding labels by <code>label[pred]</code>:</p>
<pre><code>pred =... | python|pandas|random-forest|prediction | 7 |
6,482 | 45,847,893 | Concatenate rows in python dataframe | <p>This question may be very basic, but I would like to concatenate three columns in a pandas DataFrame.<br>
I would like to concatenate col1, col2 and col3 into col4. I know in R this could be done with the paste function quite easily.</p>
<pre><code>df = pd.DataFrame({'col1': [2012, 2013, 2014], 'col2': 'q', 'col3'... | <p>Use <code>pd.DataFrame.sum</code> with <code>axis=1</code> after converting to strings.<br>
I use <code>pd.DataFrame.assign</code> to create a copy with the new column</p>
<pre><code>df.assign(col4=df[['col1', 'col2', 'col3']].astype(str).sum(1))
col1 col2 col3 col4
0 2012 q 1 2012q1
1 2013 q ... | python|pandas | 4 |
6,483 | 51,089,531 | Read CSV file with features and labels in the same row in Tensorflow | <p>I have a .csv file with around 5000 rows and 3757 columns. The first 3751 columns of each row are the features and the last 6 columns are the labels. Each row is a set of features-labels pair.</p>
<p>I'd like to know if there are built-in functions or any fast ways that I can:</p>
<ol>
<li>Parse the first 3751 col... | <p>You could look into <code>tf.data.Dataset</code>'s input pipelines (<a href="https://www.tensorflow.org/api_docs/python/tf/data/Dataset" rel="nofollow noreferrer">LINK</a>). What you basically do is you can read a csv file, possibly batch/shuffle/map it somehow and create an iterator over the dataset. Whenever you e... | python|python-3.x|pandas|tensorflow | 2 |
6,484 | 51,078,007 | Keras array shape error | <p>How come I am getting a shape <code>ValueError</code> for my neural network when what I am passing in is an array of shape <code>(8,1)</code> but the error I am getting is that the neural network is complaining about getting a <code>(1,)</code>?</p>
<p>Neural Network:</p>
<pre><code>>>> observation_dimens... | <p><code>model.predict</code> takes a batch of samples, if you give it a single sample with the wrong shape, it will interpret the first dimension as the batch one.</p>
<p>A simple solution is to add a dimension with a value of one:</p>
<pre><code>q_network.predict(obs.reshape(1, 8))
</code></pre> | python|tensorflow|neural-network|keras | 2 |
6,485 | 51,080,137 | Adding missing rows from the another table based on 2 columns | <p>I have a subset of a dataframe like bellow</p>
<pre><code>ID var1 var2 var3
111 A 1 1
222 A 1 1
333 A 1 1
444 A 2 1
555 A 2 1
666 A 2 1
</code></pre>
<p>and I want to join missing information from dataframe bellow. But only those ID that subset contains var1 and var2</p>
... | <p>Use <code>merge</code></p>
<pre><code>In [164]: df2.merge(df1[['var1', 'var2']].drop_duplicates())
Out[164]:
ID var1 var2 var3
0 111 A 1 1
1 222 A 1 1
2 333 A 1 1
3 777 A 1 0
4 888 A 1 0
5 444 A 2 1
6 555 A 2 1
7 666 A ... | python|pandas|numpy | 1 |
6,486 | 66,573,190 | how to get correct correlation plot on time series data with matplotlib/seaborn? | <p>I have time-series data that collected weekly basis, where I want to see the correlation of its two columns. to do so, I could able to find a correlation between two columns and want to see how rolling correlation moves each year. my current approach works fine but I need to normalize the two columns before doing ro... | <p>Customizing the legend in esaborn is painstaking, so I created the code in matplotlib.</p>
<ol>
<li>Corrected the method for calculating the correlation coefficient. Your code gave me an error, so please correct me if I'm wrong.</li>
<li>The color of the line graph seems to be the color of the tableau from the desir... | python|pandas|matplotlib|seaborn | 1 |
6,487 | 66,387,891 | Pandas df comparing two dates condition | <p>I'd like to add 1 if <code>date_</code> > <code>buy_date</code> larger than 12 months else 0</p>
<p>example df</p>
<pre><code>customer_id date_ buy_date
34555 2019-01-01 2017-02-01
24252 2019-01-01 2018-02-10
96477 2019-01-01 2017-02-18
</code></pre>
<p>output df</p>
<pre>... | <p>Based on what I understand, you can try adding a year to <code>buy_date</code> and then subtract from <code>date_</code> , then check if days are + or -.</p>
<pre><code> df['buy_date>_than_12_months'] = ((df['date_'] -
(df['buy_date']+pd.offsets.DateOffset(years=1)))
... | python|pandas | 4 |
6,488 | 66,383,718 | How to find column with specific row is zero in pandas | <p>I have pandas dataframe like below.</p>
<pre><code> index col_A col_B col_C col_D col_E
a 12 15 28 34 23
b 23 37 46 34 92
c 34 32 24 93 12
d 12 0 1 0 0
</code></pre>
<p>I want ... | <p>Use <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html" rel="nofollow noreferrer"><code>DataFrame.loc</code></a> for select index <code>d</code> and then filter by another <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html" rel="nofo... | python|pandas | 2 |
6,489 | 66,744,172 | How to pass the image buffer to Tensorflow JS decodeImage method? | <p>So I have the following code;
<code>CLIENT</code></p>
<pre><code>const imageData = context.getImageData(0, 0, 320, 180);
const buffer = imageData.data.buffer;
socket.emit("signal", buffer); //Pass it to the server through websocket
</code></pre>
<p><code>BACKEND</code></p>
<pre><code>socket.on("signal... | <p>The buffer data contains already the decoded image. To get the tensor out of it, here is simply what it takes on the server side:</p>
<pre><code>tf.tensor(data).reshape([IMAGE_H, IMAGE_W, -1]);
// IMAGE_H is the height of the image, here 180
// IMAGE_W is the width of the image, here 320
</code></pre> | node.js|reactjs|socket.io|tensorflow.js | 0 |
6,490 | 57,713,794 | Pandas rank subset of rows based on condition column | <p>I want to rank the below dataframe by <code>score</code>, only for rows where<code>condition</code> is <code>False</code>. The rest should have a rank of <code>NaN</code>.</p>
<pre><code>df=pd.DataFrame(np.array([[34, 65, 12, 98, 5],[False, False, True, False, False]]).T, index=['A', 'B','C','D','E'], columns=['sco... | <p>You can filter by condition column <code>rank</code>:</p>
<pre><code>df['new'] = df.loc[~df['condition'].astype(bool), 'score'].rank()
print (df)
score condition new
A 34 0 2.0
B 65 0 3.0
C 12 1 NaN
D 98 0 4.0
E 5 0 1.0
</code></pre> | pandas|dataframe|conditional-statements|rank | 4 |
6,491 | 57,574,467 | How to get deviation from a reference value for multindex dataframe | <p>I'm interested in finding deviations from my simulated data from experiment, in a manner similar to the following:</p>
<pre><code>my_frame = pd.DataFrame(data={'simulation1':[71,4.8,65,4.7],
'simulation2':[71,4.8,69,4.7],
'simulation3':[70,3.8,68,4.9],
... | <p>Create new DataFrame by subtract column <code>experiment</code> by <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sub.html" rel="nofollow noreferrer"><code>DataFrame.sub</code></a> and then change <code>MultiIndex</code>:</p>
<pre><code>df = my_frame.sub(my_frame['experiment'], ... | python|pandas | 2 |
6,492 | 57,609,350 | Can I use a boolean loop to find equal values btw two df cols then set df1['col1'] = df2['col2'] | <p>I have to find the values of <code>df2 col1</code> that are equal to <code>df1 col1</code>, then replace <code>df1 col2</code> with <code>df2 col2</code> from the same row. </p>
<p>I've already tried <code>.isin()</code> (possibly incorrectly) and multiple conditions i.e. <code>if (df1['col1'] == df2['col1']) &... | <p>Please, if you find a solution without using loops, it is always better. In your case, finding rows that are in an other column can be solved by an inner join. Here is, I hope, a code that can solve your issue.</p>
<pre class="lang-py prettyprint-override"><code>In [1]:
## Set the exemple with replicable code
impor... | python|pandas|loops|iterator | 0 |
6,493 | 73,018,028 | To check whether female household income is higher than the average male house hold income (using dataframe given) | <p>This is the code that I have done to check whether females' household income is higher than males' average household income.</p>
<pre class="lang-py prettyprint-override"><code>#Get total number of female customers
df_Female = df[df['Gender']=='Female']
FemaleIncomeArray = df_Female.loc[:,'Income'].values #get femal... | <p>sometimes when you check a condition on series or dataframes, your output is a series such as ( , False).
In this case you must use any, all, item,...
use print function for your condition to see the series</p> | python|pandas|dataframe | 0 |
6,494 | 51,528,643 | How to unpack multiple dictionary objects inside list within a row of dataframe? | <p>I have a dataframe with the below dictionaries within a single list in every row and per row, the list are different sizes with they are of different sizes as below:</p>
<pre><code>ID unnest_column
1 [{'abc': 11, 'def': 1},{'abc': 15, 'def': 1},
{'abc': 16, 'def': 1},
{'abc': 17, 'def': 1},
{... | <p>IIUC, from</p>
<pre><code>df = pd.DataFrame()
df['x'] = [[{'QuestionId': 11, 'ResponseId': 1},{'QuestionId': 15, 'ResponseId': 1},
{'QuestionId': 16, 'ResponseId': 1},
{'QuestionId': 17, 'ResponseId': 1},
{'QuestionId': 18, 'ResponseId': 1, 'Value': 'abc'},
{'QuestionId': 23, 'DataLabel': 'xxx', 'ResponseId': 1... | python|python-3.x|pandas | 3 |
6,495 | 36,238,101 | Using Pandas to export multiple rows of data to a csv | <p>I have a matching algorithm which links students to projects. It's working, and I have trouble exporting the data to a csv file. It only takes the last value and exports that only, when there are 200 values to be exported. </p>
<p>The data that's exported uses each number as a value when I would like to get the who... | <p>You keep overwriting in the loop so you only end up with the last bit of data, you need to append to the csv with <code>df.to_csv('try.csv',mode="a",header=False)</code> or create one df and append to that and write outside the loop, something like:</p>
<pre><code>df = pd.DataFrame()
for m in M:
s = m['student'... | python|csv|pandas | 1 |
6,496 | 35,814,769 | python pandas HDF5Store append new dataframe with long string columns fails | <p>For a given path, i process many GigaBytes of files inside, and yield dataframes for every processed one.
For every dataframe that is yield, which includes two string columns of varying size, I want to dump them to disk using the very efficient HDF5 format. The error is raised when the HDFStore.append procedure is c... | <p>Have you seen this post? <a href="https://stackoverflow.com/questions/22710738/pandas-pytable-how-to-specify-min-itemsize-of-the-elements-of-a-multiindex">stackoverflow</a> </p>
<pre><code>data_store.append('df',dataframe,min_itemsize={ 'string' : 5761 })
</code></pre>
<p>Change 'string' to your type.</p> | python|pandas|hdf5 | 0 |
6,497 | 38,028,762 | OutOfRangeError: RandomShuffleQueue | <p>Hi I am trying to run a conv. neural network addapted from MINST2 tutorial in tensorflow. I am having the following error, but i am not sure what is going on:</p>
<pre><code>W tensorflow/core/framework/op_kernel.cc:909] Invalid argument: Shape mismatch in tuple component 0. Expected [784], got [6272]
W tensorflow/c... | <p>This looks like an issue with your image.set_shape([784]). The error is saying that it was expecting something of size [784] but it got [6272]. I'm semi-familiar with this tutorial and the images should be 28x28 which would give you a size of 784 but maybe there are 6272 images and your dimensions are confused becau... | python|tensorflow|conv-neural-network | 0 |
6,498 | 31,550,031 | ValueError with Pandas - shaped of passed values | <p>I'm trying to use Pandas and PyODBC to pull from a SQL Server View and dump the contents to an excel file.</p>
<p>However, I'm getting the error when dumping the data frame (I can print the colums and dataframe content):</p>
<pre><code>ValueError: Shape of passed values is (1, 228), indices imply (2, 228)
</code><... | <p>To query data from a database, you can better use the built-in <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_sql_query.html" rel="nofollow"><code>read_sql_query</code></a> function instead of doing the execute and converting to dataframe manually.<br>
For your example, this would give so... | python|pandas|pyodbc | 2 |
6,499 | 64,509,263 | How do I rewrite the following SQL code in Pandas to display the query and not just the headers? | <p>I have a dataset and I am trying to write SQL query into Pandas.</p>
<p>The SQL query code is:</p>
<pre><code>`SELECT Industry_type, No_of_Employees, Employee_Insurance_Premium, Percent_Female_Employees FROM cdc_new
WHERE Industry_type= 'Hospitals' AND Employee_Insurance_Premium='Decreased'
ORDER BY Percent_Female_E... | <p>Assuming you have read in the entire table from sql with something like:</p>
<pre><code>cdc_new = pd.read_sql(query, conn)
</code></pre>
<p>You can use the following syntax:</p>
<pre><code>df = (cdc_new.loc[(cdc_new['Industry_type'] == 'Hospitals') &
(cdc_new['Employee_Insurance_Premium'] == 'D... | python|sql|pandas | 1 |
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