You can find it called [Offset Aliases](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases):
&gt;A number of string aliases are given to useful common time series frequencies. We will refer to these aliases as offset aliases.


    Alias    Description
    B        business day frequency
    C        custom business day frequency
    D        calendar day frequency
    W        weekly frequency
    M        month end frequency
    SM       semi-month end frequency (15th and end of month)
    BM       business month end frequency
    CBM      custom business month end frequency
    MS       month start frequency
    SMS      semi-month start frequency (1st and 15th)
    BMS      business month start frequency
    CBMS     custom business month start frequency
    Q        quarter end frequency
    BQ       business quarter end frequency
    QS       quarter start frequency
    BQS      business quarter start frequency
    A, Y     year end frequency
    BA, BY   business year end frequency
    AS, YS   year start frequency
    BAS, BYS business year start frequency
    BH       business hour frequency
    H        hourly frequency
    T, min   minutely frequency
    S        secondly frequency
    L, ms    milliseconds
    U, us    microseconds
    N        nanoseconds

I am using scikit-learn for classification of text documents(22000) to 100 classes. I use scikit-learn&#39;s confusion matrix method for computing the confusion matrix.

    model1 = LogisticRegression()
    model1 = model1.fit(matrix, labels)
    pred = model1.predict(test_matrix)
    cm=metrics.confusion_matrix(test_labels,pred)
    print(cm)
    plt.imshow(cm, cmap=&#39;binary&#39;)
This is how my confusion matrix looks like:

    [[3962  325    0 ...,    0    0    0]
     [ 250 2765    0 ...,    0    0    0]
     [   2    8   17 ...,    0    0    0]
     ..., 
     [   1    6    0 ...,    5    0    0]
     [   1    1    0 ...,    0    0    0]
     [   9    0    0 ...,    0    0    9]]

However, I do not receive a clear or legible plot. Is there a better way to do this?

How can I plot a confusion matrix?

How do you detect an onBlur event in Angular2?
I want to use it with

    &lt;input type=&quot;text&quot;&gt;

Can anyone help me understand how to use it?

How to use onBlur event on Angular2?

I am new to Pandas, and am trying to use `date_range`.  I came across all kinds of good things for `freq`, like `BME` and `BMS` and I would like to be able to quickly look up the proper strings to get what I want.  Yesterday I found a nicely formatted table somewhere in the documentation, but the title of the table was so obtuse that I can not use search to find it again today.

What values are valid in Pandas &#39;Freq&#39; tags?

What values are valid in Pandas &#39;Freq&#39; tags?

<p>I am new to Pandas, and am trying to use <code>date_range</code>.  I came across all kinds of good things for <code>freq</code>, like <code>BME</code> and <code>BMS</code> and I would like to be able to quickly look up the proper strings to get what I want.  Yesterday I found a nicely formatted table somewhere in the documentation, but the title of the table was so obtuse that I can not use search to find it again today.</p>
<p>What values are valid in Pandas 'Freq' tags?</p>


I am creating a AWS Lambda python deployment package. I am using one external dependency requests . I installed the external dependency using the AWS documentation http://docs.aws.amazon.com/lambda/latest/dg/lambda-python-how-to-create-deployment-package.html. Below is my python code. 

    import requests
    
    print(&#39;Loading function&#39;)
    
    s3 = boto3.client(&#39;s3&#39;)
    
    
    def lambda_handler(event, context):
        #print(&quot;Received event: &quot; + json.dumps(event, indent=2))
    
        # Get the object from the event and show its content type
        bucket = event[&#39;Records&#39;][0][&#39;s3&#39;][&#39;bucket&#39;][&#39;name&#39;]
        key = urllib.unquote_plus(event[&#39;Records&#39;][0][&#39;s3&#39;][&#39;object&#39;][&#39;key&#39;]).decode(&#39;utf8&#39;)
        try:
            response = s3.get_object(Bucket=bucket, Key=key)
            s3.download_file(bucket,key, &#39;/tmp/data.txt&#39;)
            lines = [line.rstrip(&#39;\n&#39;) for line in open(&#39;/tmp/data.txt&#39;)]
            for line in lines:
                col=line.split(&#39;,&#39;)
                print(col[5],col[6])
            print(&quot;CONTENT TYPE: &quot; + response[&#39;ContentType&#39;])
            return response[&#39;ContentType&#39;]
        except Exception as e:
            print(e)
            print(&#39;Error getting object {} from bucket {}. Make sure they exist and your bucket is in the same region as this function.&#39;.format(key, bucket))
            raise e

Created the Zip the content of the project-dir directory and uploaded to the lambda(Zip the directory content, not the directory). When I am execute the function I am getting the below mentioned error. 

    START RequestId: 9e64e2c7-d0c3-11e5-b34e-75c7fb49d058 Version: $LATEST
    **Unable to import module &#39;lambda_function&#39;: No module named lambda_function**
    
    END RequestId: 9e64e2c7-d0c3-11e5-b34e-75c7fb49d058
    REPORT RequestId: 9e64e2c7-d0c3-11e5-b34e-75c7fb49d058	Duration: 19.63 ms	Billed Duration: 100 ms 	Memory Size: 128 MB	Max Memory Used: 9 MB


Kindly help me to debug the error.
	

AWS Lambda import module error in python

I&#39;m trying to get a numpy array image from a Matplotlib figure and I&#39;m currently doing it by saving to a file, then reading the file back in, but I feel like there has to be a better way.  Here&#39;s what I&#39;m doing now:

    from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
    from matplotlib.figure import Figure

    fig = Figure()
    canvas = FigureCanvas(fig)
    ax = fig.gca()
    
    ax.text(0.0,0.0,&quot;Test&quot;, fontsize=45)
    ax.axis(&#39;off&#39;)
        
    canvas.print_figure(&quot;output.png&quot;)
    image = plt.imread(&quot;output.png&quot;)

I tried this:

    image = np.fromstring( canvas.tostring_rgb(), dtype=&#39;uint8&#39; )

from an example I found but it gives me an error saying that &#39;FigureCanvasAgg&#39; object has no attribute &#39;renderer&#39;.


Matplotlib figure to image as a numpy array

I am trying to pull data from a database and assign them to different lists.
This specific error is giving me a lot of trouble &quot;TypeError: tuple indices must be integers, not str&quot; 
I tried converting it to float and etc, but to no success.

The code goes as below

    conn=MySQLdb.connect(*details*)
    cursor=conn.cursor()
    ocs={}
    oltv={}
    query=&quot;select pool_number, average_credit_score as waocs, average_original_ltv as waoltv from *tablename* where as_of_date= *date*&quot;
    cursor.execute(query)
    result=cursor.fetchall()

    for row in result:
     print row
     ocs[row[&quot;pool_number&quot;]]=int(row[&quot;waocs&quot;])
     oltv[row[&quot;pool_number&quot;]]=int(row[&quot;waoltv&quot;])


Sample output of print statement is as follows :

    (&#39;MA3146&#39;, 711L, 81L)
    (&#39;MA3147&#39;, 679L, 83L)
    (&#39;MA3148&#39;, 668L, 86L)

And this is the exact error I am getting:

    ocs[row[&quot;pool_number&quot;]]=int(row[&quot;waocs&quot;])
    TypeError: tuple indices must be integers, not str

Any help would be appreciated! Thanks people!

TypeError: tuple indices must be integers, not str



I am lost in the scikit learn 0.18 user manual (http://scikit-learn.org/dev/modules/generated/sklearn.neural_network.MLPClassifier.html#sklearn.neural_network.MLPClassifier):

       hidden_layer_sizes : tuple, length = n_layers - 2, default (100,)
       The ith element represents the number of neurons in the ith hidden layer.

If I am looking for only 1 hidden layer and 7 hidden units in my model, should I put like this?  Thanks!

        hidden_layer_sizes=(7, 1)



Python scikit learn MLPClassifier &quot;hidden_layer_sizes&quot;

I want to change a dataframes&#39; index (rows) from float64 to string or unicode. 

I thought this would work but apparently not:

    #check type
    type(df.index)
    &#39;pandas.core.index.Float64Index&#39;

    #change type to unicode
    if not isinstance(df.index, unicode):
        df.index = df.index.astype(unicode)

error message:

    TypeError: Setting &lt;class &#39;pandas.core.index.Float64Index&#39;&gt; dtype to anything other than float64 or object is not supported

pandas - change df.index from float64 to unicode or string

I&#39;m working with pandas and need to read some csv files, the structure is something like this:

&gt;folder/folder2/scripts_folder/script.py
&gt;
&gt;folder/folder2/data_folder/data.csv

How can I open the `data.csv` file from the script in `scripts_folder`?


I&#39;ve tried this:


    absolute_path = os.path.abspath(os.path.dirname(&#39;data.csv&#39;))

    pandas.read_csv(absolute_path + &#39;/data.csv&#39;)

I get this error:

    File folder/folder2/data_folder/data.csv does not exist






How to open my files in data_folder with pandas using relative path?

I would like to run a pivot on a pandas `DataFrame`, with the index being two columns, not one. For example, one field for the year, one for the month, an &#39;item&#39; field which shows &#39;item 1&#39; and &#39;item 2&#39; and a &#39;value&#39; field with numerical values. I want the index to be year + month.

The only way I managed to get this to work was to combine the two fields into one, then separate them again. is there a better way?

Minimal code copied below. Thanks a lot!

PS Yes, I am aware there are other questions with the keywords &#39;pivot&#39; and &#39;multi-index&#39;, but I did not understand if/how they can help me with this question.

    import pandas as pd
    import numpy as np
    
    df= pd.DataFrame()
    month = np.arange(1, 13)
    values1 = np.random.randint(0, 100, 12)
    values2 = np.random.randint(200, 300, 12)
    
    
    df[&#39;month&#39;] = np.hstack((month, month))
    df[&#39;year&#39;] = 2004
    df[&#39;value&#39;] = np.hstack((values1, values2))
    df[&#39;item&#39;] = np.hstack((np.repeat(&#39;item 1&#39;, 12), np.repeat(&#39;item 2&#39;, 12)))
    
    # This doesn&#39;t work: 
    # ValueError: Wrong number of items passed 24, placement implies 2
    # mypiv = df.pivot([&#39;year&#39;, &#39;month&#39;], &#39;item&#39;, &#39;value&#39;)
    
    # This doesn&#39;t work, either:
    # df.set_index([&#39;year&#39;, &#39;month&#39;], inplace=True)
    # ValueError: cannot label index with a null key
    # mypiv = df.pivot(columns=&#39;item&#39;, values=&#39;value&#39;)
    
    # This below works but is not ideal: 
    # I have to first concatenate then separate the fields I need
    df[&#39;new field&#39;] = df[&#39;year&#39;] * 100 + df[&#39;month&#39;]
    
    mypiv = df.pivot(&#39;new field&#39;, &#39;item&#39;, &#39;value&#39;).reset_index()
    mypiv[&#39;year&#39;] = mypiv[&#39;new field&#39;].apply( lambda x: int(x) / 100)  
    mypiv[&#39;month&#39;] = mypiv[&#39;new field&#39;] % 100

pandas: how to run a pivot with a multi-index?

I have a Pandas DataFrame with one column:

    df = pd.DataFrame({&quot;teams&quot;: [[&quot;SF&quot;, &quot;NYG&quot;] for _ in range(7)]})

           teams
    0  [SF, NYG]
    1  [SF, NYG]
    2  [SF, NYG]
    3  [SF, NYG]
    4  [SF, NYG]
    5  [SF, NYG]
    6  [SF, NYG]

How can split this column of lists into two columns?

Desired result:

      team1 team2
    0    SF   NYG
    1    SF   NYG
    2    SF   NYG
    3    SF   NYG
    4    SF   NYG
    5    SF   NYG
    6    SF   NYG



Split a Pandas column of lists into multiple columns

Say we have used pandas `dataframe[column].value_counts()` which outputs:

     apple   5 
     sausage 2
     banana  2
     cheese  1



How do you extract the values in the order same as shown above from max to min ?

e.g: `[apple,sausage,banana,cheese]`

Extract values in Pandas value_counts()

I&#39;m trying to document a method and trying to use `@link` and `@code` as in [JavaDoc][1].

I know in kotlin there is a [kDoc][2] but I can&#39;t find them or at least something similar.


  [1]: http://www.oracle.com/technetwork/articles/java/index-137868.html
  [2]: https://kotlinlang.org/docs/reference/kotlin-doc.html

How to use @link and @code in kotlin kDoc

I&#39;ve got this JSON file

    {
        &quot;a&quot;: 1, 
        &quot;b&quot;: 2
    }

which has been obtained with Python json.dump method.
Now, I want to read this file into a DataFrame in Spark, using pyspark. Following documentation, I&#39;m doing this

&gt; sc = SparkContext()
&gt;
&gt; sqlc = SQLContext(sc)
&gt;
&gt; df = sqlc.read.json(&#39;my_file.json&#39;)
&gt;
&gt; print df.show()

The print statement spits out this though:

    +---------------+
    |_corrupt_record|
    +---------------+
    |              {|
    |       &quot;a&quot;: 1, |
    |         &quot;b&quot;: 2|
    |              }|
    +---------------+

Anyone knows what&#39;s going on and why it is not interpreting the file correctly?

_corrupt_record error when reading a JSON file into Spark

I am trying to  convert all the headers / column names of a `DataFrame` in Spark-Scala. as of now I come up with following code which only replaces a single column name. 

    for( i &lt;- 0 to origCols.length - 1) {
      df.withColumnRenamed(
        df.columns(i), 
        df.columns(i).toLowerCase
      );
    }

Renaming column names of a DataFrame in Spark Scala

I am having some trouble replacing values in a dataframe. I would like to replace values based on a separate table. Below is an example of what I am trying to do.

I have a table where every row is a customer and every column is an animal they purchased. Lets call this dataframe `table`.

    &gt; table
    #       P1     P2     P3
    # 1    cat lizard parrot
    # 2 lizard parrot    cat
    # 3 parrot    cat lizard

I also have a table that I will reference called `lookUp`.

    &gt; lookUp
    #      pet   class
    # 1    cat  mammal
    # 2 lizard reptile
    # 3 parrot    bird

What I want to do is create a new table called `new` with a function replaces all values in `table` with the `class` column in `lookUp`. I tried this myself using an `lapply` function, but I got the following warnings.

    new &lt;- as.data.frame(lapply(table, function(x) {
      gsub(&#39;.*&#39;, lookUp[match(x, lookUp$pet) ,2], x)}), stringsAsFactors = FALSE)
    
    Warning messages:
    1: In gsub(&quot;.*&quot;, lookUp[match(x, lookUp$pet), 2], x) :
      argument &#39;replacement&#39; has length &gt; 1 and only the first element will be used
    2: In gsub(&quot;.*&quot;, lookUp[match(x, lookUp$pet), 2], x) :
      argument &#39;replacement&#39; has length &gt; 1 and only the first element will be used
    3: In gsub(&quot;.*&quot;, lookUp[match(x, lookUp$pet), 2], x) :
      argument &#39;replacement&#39; has length &gt; 1 and only the first element will be used

Any ideas on how to make this work? 

Content Type	Original Author	Original Content on Stackoverflow
Question	sundowner	View Question on Stackoverflow
Solution 1 - Python	jezrael	View Answer on Stackoverflow

What values are valid in Pandas 'Freq' tags?

Python Problem Overview

Python Solutions

Solution 1 - Python

How to use onBlur event on Angular2?

How can I plot a confusion matrix?

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