try 

   

    pip3 install --upgrade pandas



**Simple Solution**, just type the below:


    conda update pandas 


Type this in your preferred shell (on Windows, use Anaconda Prompt as administrator).

Add your C:\WinPython-64bit-3.4.4.1\python_***\Scripts folder to your system PATH variable by doing the following:

 1. Select Start, select Control Panel. double click System, and select
    the Advanced tab.
 2. Click Environment Variables. ...
    
 3. In the Edit System Variable (or New System Variable) window, specify
        the value of the PATH environment variable. ...
 4. Reopen Command prompt window

The easiest way to do that is to run the code in the Jupitar notebook

   
     !pip install -U pandas

If it gives an error message ((**Could not install packages due to an EnvironmentError: [WinError 5] Access is denied: Consider using the `--user` option or check the permissions.**)) please add &#39;--user&#39; next the above code. as 

    !pip install -U pandas --user

According to an article on Medium, this will work:

`install --upgrade pandas==1.0.0rc0`



In my case, the problem was solved by doing

    from pandas_datareader import data as pdr
    
    import yfinance as yf
    yf.pdr_override() # &lt;== that&#39;s all it takes :-)

I simply ran conda cmd as admin then used 

    pip install --upgrade pandas

How to do the similar conditional one-line check in Elixir?

```lang-elixir
if (x &gt; 0) ? x : nil
```

Is this the only equivalent in elixir world?

```lang-elixir
if true, do: 1, else: 2
```

Simulate ternary operator in Elixir

I have a pointer, `ptr`, and a condition, `cond`. I need the fastest possible way to reset `ptr` if `cond` is `true`, or keep `ptr` unchanged if `cond` is `false`. The current implementation is, trivially:

    void reset_if_true(void*&amp; ptr, bool cond)
    {
        if (cond)
            ptr = nullptr;
    }

I&#39;m aware that the above code performance is good, and I cannot expect a major performance boost optimizing it. However, this code is called several millions of times per second and every little nanosecond saved is relevant.

I was thinking about something that get rid of the branch, e.g.:

    void* p[] = { ptr, nullptr };
    ptr = p[cond];

but I&#39;m not sure it&#39;s the best way to proceed.


What is the fastest way to update a variable on a condition?

I am running Python on a Windows machine at the following path:

    C:\WinPython-64bit-3.4.4.1

I am trying to upgrade to the latest version of pandas (currently running &#39;0.17.1&#39;) but am having problems.

I have looked at previous posts and have tried on the command line using :

    c:/&gt;pip install --upgrade pandas

but just got `&#39;pip is not recognised as an internal or external command, operable program or batch file&#39;`.


Upgrade version of Pandas

I am running Python on a Windows machine at the following path:
<pre><code class="hljs language-makefile">C:\WinPython-64bit-3.4.4.1
</code></pre>
I am trying to upgrade to the latest version of pandas (currently running '0.17.1') but am having problems.
I have looked at previous posts and have tried on the command line using :
<pre><code class="hljs language-less">c:/>pip install --upgrade pandas
</code></pre>
but just got <code>'pip is not recognised as an internal or external command, operable program or batch file'</code>.

&gt; The information below may be superfluous if you are trying to understand the error message. Please start off by reading [the answer](https://stackoverflow.com/a/37968229/6292000)
 by **@user707650**.

Using MatPlotLib, I wanted a generalizable script that creates the following from my data. 

A window containing ***a*** subplots arranged so that there are ***b*** subplots per column. I want to be able to change the values of ***a*** and ***b***. 

If I have data for ***2a*** subplots, I want 2 windows, each with the previously described &quot;***a*** subplots arranged according to ***b*** subplots per column&quot;.

The x and y data I am plotting are floats stored in np.arrays and are structured as follows:

 - The x data is always the same for all plots and is of length 5.

         &#39;x_vector&#39;: [0.000, 0.005, 0.010, 0.020, 0.030, 0.040]

 - The y data of all plots are stored in **y_vector** where the data for the first plot is stored at indexes 0 through 5. The data for the second plot is stored at indexes 6 through 11. The third plot gets 12-18, the fourth 19-24, and so on.

In total, for this dataset, I have 91 plots (i.e. 91*6 = 546 values stored in y_vector). 

**Attempt:**

    import matplotlib.pyplot as plt
    
    # Options:
    plots_tot = 14 # Total number of plots. In reality there is going to be 7*13 = 91 plots.
    location_of_ydata = 6 # The values for the n:th plot can be found in the y_vector at index &#39;n*6&#39; through &#39;n*6 + 6&#39;.
    plots_window = 7 # Total number of plots per window.
    rows = 2 # Number of rows, i.e. number of subplots per column.
    
    # Calculating number of columns:
    prim_cols = plots_window / rows
    extra_cols = 0
    if plots_window % rows &gt; 0:
    	extra_cols = 1
    cols = prim_cols + extra_cols
    
    print &#39;cols:&#39;, cols
    print &#39;rows:&#39;, rows
    
    # Plotting:
    n=0
    x=0
    fig, ax = plt.subplots(rows, cols)
    while x &lt;= plots_tot:
    	ax[x].plot(x_vector, y_vector[n:(n+location_of_ydata)], &#39;ro&#39;)
    	if x % plots_window == plots_window - 1:
    		plt.show() # New window for every 7 plots.
    	n = n+location_of_ydata
    	x = x+1

**I get the following error:**

    cols: 4
    rows: 2
    Traceback (most recent call last):
      File &quot;Script.py&quot;, line 222, in &lt;module&gt;
        ax[x].plot(x_vector, y_vector[n:(n+location_of_ydata)], &#39;ro&#39;)
    AttributeError: &#39;numpy.ndarray&#39; object has no attribute &#39;plot&#39;

Axes from plt.subplots() is a &quot;numpy.ndarray&quot; object and has no attribute &quot;plot&quot;

I&#39;m kind of confused what is going at the backend when I draw plots in matplotlib, tbh, I&#39;m not clear with the hierarchy of plot, axes and figure. I read the documentation and it was helpful but I&#39;m still confused...

The below code draws the same plot in three different ways - 

    #creating the arrays for testing
    x = np.arange(1, 100)
    y = np.sqrt(x)
    #1st way
    plt.plot(x, y)
    #2nd way
    ax = plt.subplot()
    ax.plot(x, y)
    #3rd way
    figure = plt.figure()
    new_plot = figure.add_subplot(111)
    new_plot.plot(x, y)

Now my question is -

1. What is the difference between all the three, I mean what is going under the hood when any of the 3 methods are called? 

2. Which method should be used when and what are the pros and cons of using any on those?  

What is the difference between drawing plots using plot, axes or figure in matplotlib?

I installed anaconda into my computer using python. After I install the software, I found there is one program called anaconda prompt.

What is the difference between anaconda prompt and command prompt? If I want to update the package, which one I should use or either one. Like (conda update conda)

Thank you

difference between command prompt and anaconda prompt

Given the following data frame:

    import pandas as pd
    import numpy as np
    df=pd.DataFrame({&#39;A&#39;:[&#39;A&#39;,&#39;A&#39;,&#39;A&#39;,&#39;B&#39;,&#39;B&#39;,&#39;B&#39;],
                    &#39;B&#39;:[&#39;a&#39;,&#39;a&#39;,&#39;b&#39;,&#39;a&#39;,&#39;a&#39;,&#39;a&#39;],
                    })
    df

     	A 	B
    0 	A 	a 
    1 	A 	a 
    2 	A 	b 
    3 	B 	a 
    4 	B 	a 
    5 	B 	a

I&#39;d like to create column &#39;C&#39;, which numbers the rows within each group in columns A and B like this:

     	A 	B   C
    0 	A 	a 	1
    1 	A 	a 	2
    2 	A 	b 	1
    3 	B 	a   1
    4 	B 	a   2
    5 	B 	a   3

I&#39;ve tried this so far:

    df[&#39;C&#39;]=df.groupby([&#39;A&#39;,&#39;B&#39;])[&#39;B&#39;].transform(&#39;rank&#39;)

...but it doesn&#39;t work!

Pandas number rows within group in increasing order

I have installed tensorflow in my ubuntu 16.04 using the second answer [here][1] with ubuntu&#39;s builtin apt cuda installation.

Now my question is how can I test if tensorflow is really using gpu? I have a gtx 960m gpu. When I `import tensorflow` this is the output

    I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcublas.so locally
    I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcudnn.so locally
    I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcufft.so locally
    I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcuda.so.1 locally
    I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcurand.so locally

Is this output enough to check if tensorflow is using gpu ? 
  [1]: https://devtalk.nvidia.com/default/topic/936429/-solved-tensorflow-with-gpu-in-anaconda-env-ubuntu-16-04-cuda-7-5-cudnn-/

How to tell if tensorflow is using gpu acceleration from inside python shell?

My dataframe has a `DOB` column (example format **`1/1/2016`**) which by default gets converted to Pandas dtype &#39;object&#39;.

Converting this to date format with `df[&#39;DOB&#39;] = pd.to_datetime(df[&#39;DOB&#39;])`, the date gets converted to: **`2016-01-26`** and its `dtype` is: `datetime64[ns]`.

Now I want to convert this date format to **`01/26/2016` or any other general date format**. How do I do it?

(Whatever the method I try, it always shows the date in `2016-01-26` format.)

How to change the datetime format in Pandas

I currently have this code. It works perfectly.

It loops through excel files in a folder,
removes the first 2 rows, then saves them as individual excel files,
and it also saves the files in the loop as an appended file.

Currently the appended file **overwrites** the existing file each time I run the code.

I need to append the new data to the bottom of the **already existing excel sheet** (&#39;master_data.xlsx)


    dfList = []
    path = &#39;C:\\Test\\TestRawFile&#39; 
    newpath = &#39;C:\\Path\\To\\New\\Folder&#39;
    
    for fn in os.listdir(path): 
      # Absolute file path
      file = os.path.join(path, fn)
      if os.path.isfile(file): 
        # Import the excel file and call it xlsx_file 
        xlsx_file = pd.ExcelFile(file) 
        # View the excel files sheet names 
        xlsx_file.sheet_names 
        # Load the xlsx files Data sheet as a dataframe 
        df = xlsx_file.parse(&#39;Sheet1&#39;,header= None) 
        df_NoHeader = df[2:] 
        data = df_NoHeader 
        # Save individual dataframe
        data.to_excel(os.path.join(newpath, fn))
    
        dfList.append(data) 
    
    appended_data = pd.concat(dfList)
    appended_data.to_excel(os.path.join(newpath, &#39;master_data.xlsx&#39;))


I thought this would be a simple task, but I guess not.
I think I need to bring in the master_data.xlsx file as a dataframe, then match the index up with the new appended data, and save it back out. Or maybe there is an easier way. Any Help is appreciated.


Append existing excel sheet with new dataframe using python pandas

Suppose I have a dataframe with countries that goes as:

    cc | temp
    US | 37.0
    CA | 12.0
    US | 35.0
    AU | 20.0

I know that there is a pd.get_dummies function to convert the countries to &#39;one-hot encodings&#39;. However, I wish to convert them to indices instead such that I will get `cc_index = [1,2,1,3]` instead. 

I&#39;m assuming that there is a faster way than using the get_dummies along with a numpy where clause as shown below:

`[np.where(x) for x in df.cc.get_dummies().values]`

This is somewhat easier to do in R using &#39;factors&#39; so I&#39;m hoping pandas has something similar.


Pandas: convert categories to numbers

How do I convert data from a Scikit-learn Bunch object to a Pandas DataFrame?

    from sklearn.datasets import load_iris
    import pandas as pd
    data = load_iris()
    print(type(data))
    data1 = pd. # Is there a Pandas method to accomplish this?


Content Type	Original Author	Original Content on Stackoverflow
Question	Stacey	View Question on Stackoverflow
Solution 1 - Python	Tim Seed	View Answer on Stackoverflow
Solution 2 - Python	Gil Baggio	View Answer on Stackoverflow
Solution 3 - Python	Dmitry Andreev	View Answer on Stackoverflow
Solution 4 - Python	S. Mandal	View Answer on Stackoverflow
Solution 5 - Python	Kevin Gao	View Answer on Stackoverflow
Solution 6 - Python	efueyo	View Answer on Stackoverflow
Solution 7 - Python	Umar Faruk	View Answer on Stackoverflow

Upgrade version of Pandas

Python Problem Overview

Python Solutions

Solution 1 - Python

Solution 2 - Python

Solution 3 - Python

Solution 4 - Python

Solution 5 - Python

Solution 6 - Python

Solution 7 - Python

What is the fastest way to update a variable on a condition?

Simulate ternary operator in Elixir

Attributions