This functionality is not built into [`seaborn.countplot`][1] as far as I know -  the `order` parameter only accepts a list of strings for the categories, and leaves the ordering logic to the user. 

This is not hard to do with [`value_counts()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.value_counts.html) provided you have a DataFrame though. For example, 

    import pandas as pd
    import seaborn as sns
    import matplotlib.pyplot as plt

    sns.set(style=&#39;darkgrid&#39;)

    titanic = sns.load_dataset(&#39;titanic&#39;)
    sns.countplot(x = &#39;class&#39;,
                  data = titanic,
                  order = titanic[&#39;class&#39;].value_counts().index)
    plt.show()

[![enter image description here][2]][2]


  [1]: https://seaborn.pydata.org/generated/seaborn.countplot.html
  [2]: https://i.stack.imgur.com/bewPB.png

Most often, a seaborn countplot is not really necessary. Just plot with pandas bar plot:

    import seaborn as sns; sns.set(style=&#39;darkgrid&#39;)
    import matplotlib.pyplot as plt
    
    df = sns.load_dataset(&#39;titanic&#39;)
    
    df[&#39;class&#39;].value_counts().plot(kind=&quot;bar&quot;)
    
    plt.show()

I&#39;m trying to configure Bcrypt for a node app that I&#39;m making and have several questions about salts that I hope someone here can help kindly answer.


- What is a salt &#39;round&#39;? For example, in the github docs (https://github.com/kelektiv/node.bcrypt.js/) it uses a salt round of 10. What does that mean exactly?

- Is the salt generated by Bcrypt always the same? For example, if I am saving user&#39;s hashed passwords to a DB, is the salt that it used to hash the password the same for every password?

- How is the salt stored? Is it secure from potential attacks?



What are Salt Rounds and how are Salts stored in Bcrypt?

I have 2 TextViews as in the xml below.
If I hide the `textView2` at runtime, I lose the bottom margin.
How can I keep the bottom margin between `textView` and parent to be 16dp when the `textView2` is gone.

    &lt;?xml version=&quot;1.0&quot; encoding=&quot;utf-8&quot;?&gt;
    &lt;android.support.constraint.ConstraintLayout
    xmlns:android=&quot;http://schemas.android.com/apk/res/android&quot;
    xmlns:app=&quot;http://schemas.android.com/apk/res-auto&quot;
    android:layout_width=&quot;match_parent&quot;
    android:layout_height=&quot;wrap_content&quot;&gt;

      &lt;TextView
          android:id=&quot;@+id/textView&quot;
          android:layout_width=&quot;0dp&quot;
          android:layout_height=&quot;wrap_content&quot;
          android:layout_marginBottom=&quot;16dp&quot;
          android:layout_marginEnd=&quot;8dp&quot;
          android:layout_marginStart=&quot;8dp&quot;
          android:layout_marginTop=&quot;8dp&quot;
          android:text=&quot;abc&quot;
          app:layout_constraintBottom_toTopOf=&quot;@+id/textView2&quot;
          app:layout_constraintEnd_toEndOf=&quot;parent&quot;
          app:layout_constraintStart_toStartOf=&quot;parent&quot;
          app:layout_constraintTop_toTopOf=&quot;parent&quot;
          app:layout_constraintVertical_chainStyle=&quot;packed&quot;
          /&gt;

      &lt;TextView
          android:id=&quot;@+id/textView2&quot;
          android:layout_width=&quot;0dp&quot;
          android:layout_height=&quot;wrap_content&quot;
          android:layout_marginBottom=&quot;32dp&quot;
          android:layout_marginEnd=&quot;8dp&quot;
          android:layout_marginStart=&quot;8dp&quot;
          android:text=&quot;xyz&quot;
          app:layout_constraintBottom_toBottomOf=&quot;parent&quot;
          app:layout_constraintEnd_toEndOf=&quot;parent&quot;
          app:layout_constraintStart_toStartOf=&quot;parent&quot;
          app:layout_constraintTop_toBottomOf=&quot;@+id/textView&quot;
          app:layout_constraintVertical_chainStyle=&quot;packed&quot;/&gt;
    &lt;/android.support.constraint.ConstraintLayout&gt;

keep the margin when the referenced view is gone in ConstraintLayout

I know that [`seaborn.countplot`](https://seaborn.pydata.org/generated/seaborn.countplot.html) has the attribute `order` which can be set to determine the order of the categories. But what I would like to do is have the categories be in order of descending count. I know that I can accomplish this by computing the count manually (using a `groupby` operation on the original dataframe, etc.) but I am wondering if this functionality exists with `seaborn.countplot`. Surprisingly, I cannot find an answer to this question anywhere.

Seaborn.countplot : order categories by count

<p>I know that <a href="https://seaborn.pydata.org/generated/seaborn.countplot.html" target="_blank" rel="noopener noreferrer"><code>seaborn.countplot</code></a> has the attribute <code>order</code> which can be set to determine the order of the categories. But what I would like to do is have the categories be in order of descending count. I know that I can accomplish this by computing the count manually (using a <code>groupby</code> operation on the original dataframe, etc.) but I am wondering if this functionality exists with <code>seaborn.countplot</code>. Surprisingly, I cannot find an answer to this question anywhere.</p>


In this way, I want to set my cookie. But it fails to set.

    @app.route(&#39;/&#39;)
    def index():
        res = flask.make_response()
        res.set_cookie(&quot;name&quot;, value=&quot;I am cookie&quot;)


When I print `res` it shows `&lt;Response 0 bytes [200 OK]` But not set cookie


How to set cookie in Python Flask?

A `tuple` takes less memory space in Python:

    &gt;&gt;&gt; a = (1,2,3)
    &gt;&gt;&gt; a.__sizeof__()
    48

whereas `list`s takes more memory space:

    &gt;&gt;&gt; b = [1,2,3]
    &gt;&gt;&gt; b.__sizeof__()
    64

What happens internally on the Python memory management? 

Why do tuples take less space in memory than lists?

I have a particular np.array *data* which represents a particular grayscale image.
I need to use SimpleBlobDetector() that unfortunately only accepts 8bit images, so I need to convert this image, obviously having a quality-loss.

I&#39;ve already tried:

    import numpy as np
    import cv2
    [...]
    data = data / data.max() #normalizes data in range 0 - 255
    data = 255 * data
    img = data.astype(np.uint8)
    cv2.imshow(&quot;Window&quot;, img)

But *cv2.imshow* is not giving the image as expected, but with strange distortion...

In the end, I only need to convert a np.float64 to np.uint8 scaling all the values and truncating the rest, eg. 65535 becomes 255, 65534 becomes 254 and so on.... Any help?

Thanks.


Convert np.array of type float64 to type uint8 scaling values

Please explain why Python does not have the switch-case feature implemented in it.

Why doesn&#39;t Python have switch-case?

I&#39;m trying to build a Flask app using Postgres with Docker. I&#39;d like to connect to an AWS RDS instance of Postgres, but use Docker for my Flask app. However, when trying to set up `psycopg2` it runs into an error because it can&#39;t find `pg_config`. Here&#39;s the error:

    Building api
    Step 1/5 : FROM python:3.6.3-alpine3.6
     ---&gt; 84c98ca3b5c5
    Step 2/5 : WORKDIR /usr/src/app
     ---&gt; Using cache
     ---&gt; 407c158f5ee4
    Step 3/5 : COPY . .
     ---&gt; 966df18d329e
    Step 4/5 : RUN pip install -r requirements.txt
     ---&gt; Running in 284cc97aeb63
    Collecting aniso8601==1.3.0 (from -r requirements.txt (line 1))
      Downloading aniso8601-1.3.0.tar.gz (57kB)
    Collecting click==6.7 (from -r requirements.txt (line 2))
      Downloading click-6.7-py2.py3-none-any.whl (71kB)
    Collecting Flask==0.12.2 (from -r requirements.txt (line 3))
      Downloading Flask-0.12.2-py2.py3-none-any.whl (83kB)
    Collecting Flask-RESTful==0.3.6 (from -r requirements.txt (line 4))
      Downloading Flask_RESTful-0.3.6-py2.py3-none-any.whl
    Collecting Flask-SQLAlchemy==2.3.2 (from -r requirements.txt (line 5))
      Downloading Flask_SQLAlchemy-2.3.2-py2.py3-none-any.whl
    Collecting itsdangerous==0.24 (from -r requirements.txt (line 6))
      Downloading itsdangerous-0.24.tar.gz (46kB)
    Collecting Jinja2==2.9.6 (from -r requirements.txt (line 7))
      Downloading Jinja2-2.9.6-py2.py3-none-any.whl (340kB)
    Collecting MarkupSafe==1.0 (from -r requirements.txt (line 8))
      Downloading MarkupSafe-1.0.tar.gz
    Collecting psycopg2==2.7.3.1 (from -r requirements.txt (line 9))
      Downloading psycopg2-2.7.3.1.tar.gz (425kB)
        Complete output from command python setup.py egg_info:
        running egg_info
        creating pip-egg-info/psycopg2.egg-info
        writing pip-egg-info/psycopg2.egg-info/PKG-INFO
        writing dependency_links to pip-egg-info/psycopg2.egg-info/dependency_links.txt
        writing top-level names to pip-egg-info/psycopg2.egg-info/top_level.txt
        writing manifest file &#39;pip-egg-info/psycopg2.egg-info/SOURCES.txt&#39;
        Error: pg_config executable not found.
    
        Please add the directory containing pg_config to the PATH
        or specify the full executable path with the option:
    
            python setup.py build_ext --pg-config /path/to/pg_config build ...
    
        or with the pg_config option in &#39;setup.cfg&#39;.
    
        ----------------------------------------
    Command &quot;python setup.py egg_info&quot; failed with error code 1 in /tmp/pip-build-01lf5grh/psycopg2/
    ERROR: Service &#39;api&#39; failed to build: The command &#39;/bin/sh -c pip install -r requirements.txt&#39; returned a non-zero code: 1

Here&#39;s my `Dockerfile`:

    FROM python:3.6.3-alpine3.6
    
    WORKDIR /usr/src/app
    
    COPY . .
    
    RUN pip install -r requirements.txt
    
    CMD [&quot;python&quot;, &quot;app.py&quot;]

**Many others seem to have a similar issue locally, but none of them involve using Docker. This seems like a Docker issue because I can set up a local virtual environment and the setup works just fine since I have Postgres installed locally and it&#39;s able to find my local `pg_config`.** 

It appears that during the container build/setup, Docker is trying to find `pg_config` within the container. Is there a way to install a `pg_config` in the container, even though I won&#39;t be using a containerized instance of Postgres, but rather the instance on RDS?

Any and all suggestions on how to get around this are welcomed.

Error: pg_config executable not found when installing psycopg2 on Alpine in Docker

I&#39;m trying to figure out how to add 3 months to a date in a Pandas dataframe, while keeping it in the date format, so I can use it to lookup a range.

This is what I&#39;ve tried:

    #create dataframe
    df = pd.DataFrame([pd.Timestamp(&#39;20161011&#39;),
                       pd.Timestamp(&#39;20161101&#39;) ], columns=[&#39;date&#39;])

    #create a future month period
    plus_month_period = 3

    #calculate date + future period
    df[&#39;future_date&#39;] = plus_month_period.astype(&quot;timedelta64[M]&quot;)

However, I get the following error:

    AttributeError: &#39;int&#39; object has no attribute &#39;astype&#39;


Add months to a date in Pandas

Is it possible to use TQDM progress bar when importing and indexing large datasets using Pandas?

Here is an example of of some 5-minute data I am importing, indexing, and using to_datetime.  It takes a while and it would be nice to see a progress bar.

    #Import csv files into a Pandas dataframes and convert to Pandas datetime and set to index
    
    eurusd_ask = pd.read_csv(&#39;EURUSD_Candlestick_5_m_ASK_01.01.2012-05.08.2017.csv&#39;)
    eurusd_ask.index = pd.to_datetime(eurusd_ask.pop(&#39;Gmt time&#39;))



Use TQDM Progress Bar with Pandas

Hi I want to get the counts of unique values of the dataframe. count_values implements this however I want to use its output somewhere else. How can I convert .count_values output to a pandas dataframe. here is an example code:

    import pandas as pd
    df = pd.DataFrame({&#39;a&#39;:[1, 1, 2, 2, 2]})
    value_counts = df[&#39;a&#39;].value_counts(dropna=True, sort=True)
    print(value_counts)
    print(type(value_counts))

output is:

    2    3
    1    2
    Name: a, dtype: int64
    &lt;class &#39;pandas.core.series.Series&#39;&gt;

What I need is a dataframe like this:

    unique_values  counts
    2              3
    1              2

Thank you. 


Python Pandas: Convert &quot;.value_counts&quot; output to dataframe

Hi: I am trying to use the `Pandas` `DataFrame.to_csv` method to save a `dataframe` to a `csv` file:

    filename = &#39;./dir/name.csv&#39;

    df.to_csv(filename)


However I am getting the error:

    IOError: [Errno 2] No such file or directory: &#39;./dir/name.csv&#39;

Shouldn&#39;t the `to_csv` method be able to create the file if it doesn&#39;t exist? This is what I am intending for it to do.




Pandas DataFrame.to_csv raising IOError: No such file or directory

- What is pivot?
- How do I pivot?
- Is this a pivot?
- Long format to wide format?

I&#39;ve seen a lot of questions that ask about pivot tables.  Even if they don&#39;t know that they are asking about pivot tables, they usually are.  It is virtually impossible to write a canonical question and answer that encompasses all aspects of pivoting...

... But I&#39;m going to give it a go.

___

The problem with existing questions and answers is that often the question is focused on a nuance that the OP has trouble generalizing in order to use a number of the existing good answers.  However, none of the answers attempt to give a comprehensive explanation (because it&#39;s a daunting task)

Look a few examples from my [**Google Search**][1]

1. https://stackoverflow.com/q/28337117/2336654
 - Good question and answer.  But the answer only answers the specific question with little explanation.
2. https://stackoverflow.com/q/42708193/2336654
 - In this question, the OP is concerned with the output of the pivot.  Namely how the columns look.  OP wanted it to look like R.  This isn&#39;t very helpful for pandas users.
3. https://stackoverflow.com/q/11400181/2336654
- Another decent question but the answer focuses on one method, namely `pd.DataFrame.pivot`

So whenever someone searches for `pivot` they get sporadic results that are likely not going to answer their specific question.

___

# Setup

You may notice that I conspicuously named my columns and relevant column values to correspond with how I&#39;m going to pivot in the answers below.

    import numpy as np
    import pandas as pd
    from numpy.core.defchararray import add

    np.random.seed([3,1415])
    n = 20

    cols = np.array([&#39;key&#39;, &#39;row&#39;, &#39;item&#39;, &#39;col&#39;])
    arr1 = (np.random.randint(5, size=(n, 4)) // [2, 1, 2, 1]).astype(str)

    df = pd.DataFrame(
        add(cols, arr1), columns=cols
    ).join(
        pd.DataFrame(np.random.rand(n, 2).round(2)).add_prefix(&#39;val&#39;)
    )
    print(df)

         key   row   item   col  val0  val1
    0   key0  row3  item1  col3  0.81  0.04
    1   key1  row2  item1  col2  0.44  0.07
    2   key1  row0  item1  col0  0.77  0.01
    3   key0  row4  item0  col2  0.15  0.59
    4   key1  row0  item2  col1  0.81  0.64
    5   key1  row2  item2  col4  0.13  0.88
    6   key2  row4  item1  col3  0.88  0.39
    7   key1  row4  item1  col1  0.10  0.07
    8   key1  row0  item2  col4  0.65  0.02
    9   key1  row2  item0  col2  0.35  0.61
    10  key2  row0  item2  col1  0.40  0.85
    11  key2  row4  item1  col2  0.64  0.25
    12  key0  row2  item2  col3  0.50  0.44
    13  key0  row4  item1  col4  0.24  0.46
    14  key1  row3  item2  col3  0.28  0.11
    15  key0  row3  item1  col1  0.31  0.23
    16  key0  row0  item2  col3  0.86  0.01
    17  key0  row4  item0  col3  0.64  0.21
    18  key2  row2  item2  col0  0.13  0.45
    19  key0  row2  item0  col4  0.37  0.70

### Question(s)

1. Why do I get `ValueError: Index contains duplicate entries, cannot reshape`

2. How do I pivot `df` such that the `col` values are columns, `row` values are the index, and mean of `val0` are the values?

        col   col0   col1   col2   col3  col4
        row
        row0  0.77  0.605    NaN  0.860  0.65
        row2  0.13    NaN  0.395  0.500  0.25
        row3   NaN  0.310    NaN  0.545   NaN
        row4   NaN  0.100  0.395  0.760  0.24

3. How do I pivot `df` such that the `col` values are columns, `row` values are the index, mean of `val0` are the values, and missing values are `0`?

        col   col0   col1   col2   col3  col4
        row
        row0  0.77  0.605  0.000  0.860  0.65
        row2  0.13  0.000  0.395  0.500  0.25
        row3  0.00  0.310  0.000  0.545  0.00
        row4  0.00  0.100  0.395  0.760  0.24

4. Can I get something other than `mean`, like maybe `sum`?

        col   col0  col1  col2  col3  col4
        row
        row0  0.77  1.21  0.00  0.86  0.65
        row2  0.13  0.00  0.79  0.50  0.50
        row3  0.00  0.31  0.00  1.09  0.00
        row4  0.00  0.10  0.79  1.52  0.24

5. Can I do more that one aggregation at a time?

               sum                          mean
        col   col0  col1  col2  col3  col4  col0   col1   col2   col3  col4
        row
        row0  0.77  1.21  0.00  0.86  0.65  0.77  0.605  0.000  0.860  0.65
        row2  0.13  0.00  0.79  0.50  0.50  0.13  0.000  0.395  0.500  0.25
        row3  0.00  0.31  0.00  1.09  0.00  0.00  0.310  0.000  0.545  0.00
        row4  0.00  0.10  0.79  1.52  0.24  0.00  0.100  0.395  0.760  0.24

6. Can I aggregate over multiple value columns?

              val0                             val1
        col   col0   col1   col2   col3  col4  col0   col1  col2   col3  col4
        row
        row0  0.77  0.605  0.000  0.860  0.65  0.01  0.745  0.00  0.010  0.02
        row2  0.13  0.000  0.395  0.500  0.25  0.45  0.000  0.34  0.440  0.79
        row3  0.00  0.310  0.000  0.545  0.00  0.00  0.230  0.00  0.075  0.00
        row4  0.00  0.100  0.395  0.760  0.24  0.00  0.070  0.42  0.300  0.46

7. Can Subdivide by multiple columns?

        item item0             item1                         item2
        col   col2  col3  col4  col0  col1  col2  col3  col4  col0   col1  col3  col4
        row
        row0  0.00  0.00  0.00  0.77  0.00  0.00  0.00  0.00  0.00  0.605  0.86  0.65
        row2  0.35  0.00  0.37  0.00  0.00  0.44  0.00  0.00  0.13  0.000  0.50  0.13
        row3  0.00  0.00  0.00  0.00  0.31  0.00  0.81  0.00  0.00  0.000  0.28  0.00
        row4  0.15  0.64  0.00  0.00  0.10  0.64  0.88  0.24  0.00  0.000  0.00  0.00

8. Or

        item      item0             item1                         item2
        col        col2  col3  col4  col0  col1  col2  col3  col4  col0  col1  col3  col4
        key  row
        key0 row0  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.86  0.00
             row2  0.00  0.00  0.37  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.50  0.00
             row3  0.00  0.00  0.00  0.00  0.31  0.00  0.81  0.00  0.00  0.00  0.00  0.00
             row4  0.15  0.64  0.00  0.00  0.00  0.00  0.00  0.24  0.00  0.00  0.00  0.00
        key1 row0  0.00  0.00  0.00  0.77  0.00  0.00  0.00  0.00  0.00  0.81  0.00  0.65
             row2  0.35  0.00  0.00  0.00  0.00  0.44  0.00  0.00  0.00  0.00  0.00  0.13
             row3  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.28  0.00
             row4  0.00  0.00  0.00  0.00  0.10  0.00  0.00  0.00  0.00  0.00  0.00  0.00
        key2 row0  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.40  0.00  0.00
             row2  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.00  0.13  0.00  0.00  0.00
             row4  0.00  0.00  0.00  0.00  0.00  0.64  0.88  0.00  0.00  0.00  0.00  0.00

9. Can I aggregate the frequency in which the column and rows occur together, aka &quot;cross tabulation&quot;?

        col   col0  col1  col2  col3  col4
        row
        row0     1     2     0     1     1
        row2     1     0     2     1     2
        row3     0     1     0     2     0
        row4     0     1     2     2     1

10. How do I convert a DataFrame from long to wide by pivoting on ONLY two columns? Given,

        np.random.seed([3, 1415])
        df2 = pd.DataFrame({&#39;A&#39;: list(&#39;aaaabbbc&#39;), &#39;B&#39;: np.random.choice(15, 8)})
        df2
           A   B
        0  a   0
        1  a  11
        2  a   2
        3  a  11
        4  b  10
        5  b  10
        6  b  14
        7  c   7

    The expected should look something like

              a     b    c
        0   0.0  10.0  7.0
        1  11.0  10.0  NaN
        2   2.0  14.0  NaN
        3  11.0   NaN  NaN

11. How do I flatten the multiple index to single index after `pivot`?

    From

           1  2
           1  1  2
        a  2  1  1
        b  2  1  0
        c  1  0  0


    To

           1|1  2|1  2|2
        a    2    1    1
        b    2    1    0
        c    1    0    0


  [1]: https://www.google.com/search?q=how%20to%20pivot%20a%20pandas%20dataframe&amp;oq=How%20do%20I%20pivot%20a%20pandas%20dataframe





How can I pivot a dataframe?

&lt;!-- What are you trying to accomplish? (Please include sample data.) --&gt;
I have created a figure using matplotlib but I have realised the plot axis and the drawn line gets zoomed out. 
[![enter image description here][1]][1]

Reading this [earlier discussion thread][2], it explains how to set the figure size.
&lt;!-- Paste the part of the code that shows the problem. (Please indent 4 spaces.) --&gt;

`fig, ax = plt.subplots()`

`fig.set_size_inches(3, 1.5)`

`plt.savefig(file.jpeg, edgecolor=&#39;black&#39;, dpi=400, facecolor=&#39;black&#39;, transparent=True)`

&lt;!-- What do you expect the result to be? --&gt;
With the above code (other configurations removed for brevity), I do get a resulting image file with `1200 X 600` desired dimensions(should we say resolution too?) and desired file size.

&lt;!-- What is the actual result you get? (Please include any errors.) --&gt;
The projected image is scaled out in an unusual way, annotations for example are enlarged. While I can set the size of the labels on the axis, the figure doesn&#39;t look proportional with respect to the scale since the bottom and right spines are  large and so are the plotted lines.

The question therefore is, what configurations are going wrong?


  [1]: https://i.stack.imgur.com/Zncs9.jpg
  [2]: https://stackoverflow.com/questions/332289/how-do-you-change-the-size-of-figures-drawn-with-matplotlib/638443#638443

Relationship between dpi and figure size

I am trying out Seaborn to make my plot visually better than matplotlib. I have a dataset which has a column &#39;Year&#39; which I want to plot on the X-axis and 4 Columns say A,B,C,D on the Y-axis using different coloured lines. I was trying to do this using the sns.lineplot method but it allows for only one variable on the X-axis and one on the Y-axis. I tried doing this

    sns.lineplot(data_preproc[&#39;Year&#39;],data_preproc[&#39;A&#39;], err_style=None)
    sns.lineplot(data_preproc[&#39;Year&#39;],data_preproc[&#39;B&#39;], err_style=None)
    sns.lineplot(data_preproc[&#39;Year&#39;],data_preproc[&#39;C&#39;], err_style=None)
    sns.lineplot(data_preproc[&#39;Year&#39;],data_preproc[&#39;D&#39;], err_style=None)
But this way I don&#39;t get a legend in the plot to show which coloured line corresponds to what. I tried checking the documentation but couldn&#39;t find a proper way to do this.

How do I create a multiline plot using seaborn?

Using the data frame

    df = pd.DataFrame({
        &quot;date&quot; : [&quot;2018-01-01&quot;, &quot;2018-01-02&quot;, &quot;2018-01-03&quot;, &quot;2018-01-04&quot;],
        &quot;column1&quot; : [555,525,532,585],
        &quot;column2&quot; : [50,48,49,51]
    })

one can plot with `seaborn` say `column1` with `sns.tsplot(data=df.column1, color=&quot;g&quot;)`.

How can we plot both time series with two y-axis in seaborn ?

Second y-axis time series seaborn

I feel I am probably not thinking of something obvious. I want to put in the same figure, the box plot of every column of a dataframe, where on the x-axis I have the columns&#39; names. In the `seaborn.boxplot()` this would be equal to `groupby` by every column. 

In pandas I would do 

    df = pd.DataFrame(data = np.random.random(size=(4,4)), columns = [&#39;A&#39;,&#39;B&#39;,&#39;C&#39;,&#39;D&#39;])
    df.boxplot()

which yields

[![enter image description here][1]][1]

Now I would like to get the same thing in seaborn. But when I try `sns.boxplot(df)`, I get only one grouped boxplot. How do I reproduce the same figure in seaborn? 


  [1]: https://i.stack.imgur.com/6NHvp.png

Boxplot of Multiple Columns of a Pandas Dataframe on the Same Figure (seaborn)

I have some geometrically distributed data. When I want to take a look at it, I use

    sns.distplot(data, kde=False, norm_hist=True, bins=100)

which results is a picture:

[![Plot 1a][1]][1]

However, bins heights don&#39;t add up to 1, which means y axis doesn&#39;t show probability, it&#39;s something different. If instead we use

    weights = np.ones_like(np.array(data))/float(len(np.array(data)))
    plt.hist(data, weights=weights, bins = 100)

the y axis shall show probability, as bins heights sum up to 1:

[![Plot 1b][2]][2]

It can be seen more clearly here: suppose we have a list

    l = [1, 3, 2, 1, 3]

We have two 1s, two 3s and one 2, so their respective probabilities are 2/5, 2/5 and 1/5. When we use seaborn histplot with 3 bins:

    sns.distplot(l, kde=False, norm_hist=True, bins=3)

we get:

[![Plot 2a][3]][3]

As you can see, the 1st and the 3rd bin sum up to 0.6+0.6=1.2 which is already greater than 1, so y axis is not a probability. When we use 

    weights = np.ones_like(np.array(l))/float(len(np.array(l)))
    plt.hist(l, weights=weights, bins = 3)

we get:

[![enter image description here][4]][4]

and the y axis is probability, as 0.4+0.4+0.2=1 as expected.

The amount of bins in these 2 cases are is the same for both methods used in each case: 100 bins for geometrically distributed data, 3 bins for small array l with 3 possible values. So bins amount is not the issue.

**My question is:** in seaborn distplot called with norm_hist=True, what is the meaning of y axis?


  [1]: https://i.stack.imgur.com/jE09g.png
  [2]: https://i.stack.imgur.com/6yo35.png
  [3]: https://i.stack.imgur.com/AuzZ1.png
  [4]: https://i.stack.imgur.com/FHpvu.png

What is y axis in seaborn distplot?

I can&#39;t find out anywhere how to change the marker size on seaborn scatterplots. There is a `size` option listed in the [documentation][1] but it is only for when you want variable size across points. I want the same size for all points but larger than the default!

I tried making a new column of integers in my dataframe and set that as the size, but it looks like the actual value doesn&#39;t matter, it changes the marker size on a relative basis, so in this case all the markers were still the same size as the default.


Edit: here&#39;s some code

    ax = sns.scatterplot(x=&quot;Data Set Description&quot;, y=&quot;R Squared&quot;, data=mean_df)
    plt.show()

I just tried something and it worked, not sure if it&#39;s the best method though. I added size=[1, 1, 1, 1, 1, 1] and sizes=(500, 500). So essentially I&#39;m setting all sizes to be the same, and the range of sizes to be only at 500.



  [1]: https://seaborn.pydata.org/generated/seaborn.scatterplot.html

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