You need to set `index=False` in `to_excel` in order for it to not write the index column out, this semantic is followed in other Pandas IO tools, see http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_excel.html and http://pandas.pydata.org/pandas-docs/stable/io.html

Example: index = False  

    import pandas as pd

    writer = pd.ExcelWriter(&quot;dataframe.xlsx&quot;, engine=&#39;xlsxwriter&#39;)
    dataframe.to_excel(writer,sheet_name = dataframe, index=False)
    writer.save() 

I did that and got the error message:

TypeError: &#39;DataFrame&#39; objects are mutable, thus they cannot be hashed.

The code is as follows where &#39;test&#39; is a dataframe with no column names

    test = pd.DataFrame(biglist)
    writer = pd.ExcelWriter(&quot;test.xlsx&quot;, engine=&#39;xlsxwriter&#39;)
    test.to_excel(writer,sheet_name=test, index=False)
    writer.save()

I am new to python and cannot quite understand the difference between find and index. 

    &gt;&gt;&gt; line
    &#39;hi, this is ABC oh my god!!&#39;
    &gt;&gt;&gt; line.find(&quot;o&quot;)
    16
    &gt;&gt;&gt; line.index(&quot;o&quot;)
    16

They always return the same result.
Thanks!!

difference between find and index

I&#39;m making and listening for normal DOM `CustomEvent`s to communicate to parent nodes:

In child:

&lt;!-- language: lang-js --&gt;

      var moveEvent = new CustomEvent(&#39;the-graph-group-move&#39;, { 
        detail: {
          nodes: this.props.nodes,
          x: deltaX,
          y: deltaY
        },
        bubbles: true
      });
      this.getDOMNode().dispatchEvent(moveEvent);

In parent: 

&lt;!-- language: lang-js --&gt;

    componentDidMount: function () {
      this.getDOMNode().addEventListener(&quot;the-graph-group-move&quot;, this.moveGroup);
    },

This works, but is there a React-specific way that would be better? 

react.js custom events for communicating with parent nodes

I&#39;m trying to print out a dataframe from pandas into Excel. Here I am using to_excel() functions. However, I found that the 1st column in Excel is the &quot;index&quot;, 

    0	6/6/2021 0:00	8/6/2021 0:00
    1	4/10/2024 0:00	6/10/2024 0:00
    2	4/14/2024 0:00	6/14/2024 0:00

Is there any ways to get rid of the first column?

Export from pandas to_excel without row names (index)?

<p>I'm trying to print out a dataframe from pandas into Excel. Here I am using to_excel() functions. However, I found that the 1st column in Excel is the "index",</p>
<pre><code class="hljs">0	6/6/2021 0:00	8/6/2021 0:00
1	4/10/2024 0:00	6/10/2024 0:00
2	4/14/2024 0:00	6/14/2024 0:00
</code></pre>
<p>Is there any ways to get rid of the first column?</p>


what would be the most efficient way to use groupby and in parallel apply a filter in pandas?

Basically I am asking for the equivalent in SQL of

    select *
    ...
    group by col_name
    having condition

I think there are many uses cases ranging from conditional means, sums, conditional probabilities, etc. which would make such a command very powerful.

I need a very good performance, so ideally such a command would not be the result of several layered operations done in python.





What is the equivalent of SQL &quot;GROUP BY HAVING&quot; on Pandas?

I cannot figure out how to do &quot;reverse melt&quot; using Pandas in python.
This is my starting data

    import pandas as pd

    from StringIO import StringIO

    origin = pd.read_table(StringIO(&#39;&#39;&#39;label	type	value
    x	a	1
    x	b	2
    x	c	3
    y	a	4
    y	b	5
    y	c	6
    z	a	7
    z	b	8
    z	c	9&#39;&#39;&#39;))

    origin
    Out[5]: 
      label type  value
    0     x    a      1
    1     x    b      2
    2     x    c      3
    3     y    a      4
    4     y    b      5
    5     y    c      6
    6     z    a      7
    7     z    b      8
    8     z    c      9
    
    
This is the output I would like to have:

        label	a	b	c
            x	1	2	3
            y	4	5	6
            z	7	8	9


            
I&#39;m sure there is an easy way to do this, but I don&#39;t know how.

Opposite of melt in python pandas

I want to subtract dates in &#39;A&#39; from dates in &#39;B&#39; and add a new column with the difference.

    df
              A        B
    one 2014-01-01  2014-02-28 
    two 2014-02-03  2014-03-01

I&#39;ve tried the following, but get an error when I try to include this in a for loop...

    import datetime
    date1=df[&#39;A&#39;][0]
    date2=df[&#39;B&#39;][0]
    mdate1 = datetime.datetime.strptime(date1, &quot;%Y-%m-%d&quot;).date()
    rdate1 = datetime.datetime.strptime(date2, &quot;%Y-%m-%d&quot;).date()
    delta =  (mdate1 - rdate1).days
    print delta

What should I do?

Add column with number of days between dates in DataFrame pandas

I have a DataFrame that contains numbers as strings with commas for the thousands marker. I need to convert them to floats.

    a = [[&#39;1,200&#39;, &#39;4,200&#39;], [&#39;7,000&#39;, &#39;-0.03&#39;], [ &#39;5&#39;, &#39;0&#39;]]
    df=pandas.DataFrame(a)

I am guessing I need to use locale.atof. Indeed 

    df[0].apply(locale.atof)

works as expected. I get a Series of floats.

But when I apply it to the DataFrame, I get an error.

    df.apply(locale.atof)

&gt;TypeError: (&quot;cannot convert the series to &lt;type &#39;float&#39;&gt;&quot;, u&#39;occurred at index 0&#39;)

and

    df[0:1].apply(locale.atof)

gives another error:

&gt;ValueError: (&#39;invalid literal for float(): 1,200&#39;, u&#39;occurred at index 0&#39;)

So, how do I convert this `DataFrame` of strings to a DataFrame of floats?


Convert number strings with commas in pandas DataFrame to float

Here is my code to generate a dataframe:

    import pandas as pd
    import numpy as np

    dff = pd.DataFrame(np.random.randn(1,2),columns=list(&#39;AB&#39;))

then I got the dataframe:

    +------------+---------+--------+
    |            |  A      |  B     |
    +------------+---------+---------
    |      0     | 0.626386| 1.52325|
    +------------+---------+--------+
When I type the commmand :
 
    dff.mean(axis=1)
I got :

    0    1.074821
    dtype: float64
According to the reference of pandas, axis=1 stands for columns and I expect the result of the command to be

    A    0.626386
    B    1.523255
    dtype: float64
So here is my question: what does axis in pandas mean?


What does axis in pandas mean?

How do I delete all the instances of a character in this string? Here is my code:

    def findreplace(char, string):
        place = string.index(char)
        string[place] = &#39;&#39;
        return string

However, if I run this, this is what happens:

    &gt;&gt;&gt; findreplace(&#39;i&#39;, &#39;it is icy&#39;)
    Traceback (most recent call last):
      File &quot;&lt;stdin&gt;&quot;, line 1, in &lt;module&gt;
      File &quot;&lt;stdin&gt;&quot;, line 3, in findreplace
    TypeError: &#39;str&#39; object does not support item assignment

Why is this?




How to delete all instances of a character in a string in python?

When I ran **Homebrew&#39;s** `brew doctor` (Mac OS X 10.9.2), I get the following warning message:

&gt; Warning: Python is installed at /Library/Frameworks/Python.framework
&gt; 
&gt; Homebrew only supports building against the System-provided Python or
&gt; a brewed Python. In particular, Pythons installed to /Library can
&gt; interfere with other software installs.

Therefore, I ran `brew install` and followed the steps provided in the installation&#39;s caveats output to install Homebrew&#39;s version of **Python**. Running `which python` confirms that Homebrew&#39;s version of it is indeed at the top of my *PATH*. Output is `/usr/local/bin/python`.

Despite all this, when I rerun `brew doctor`, I am still getting the *same warning message*. How do I suppress this warning? Do I need to delete the /Library/Frameworks/Python.framework directory from my computer? Am I just supposed to ignore it? Is there a different application on my computer that may be causing this warning to emit?

Note that I don&#39;t have any applications in particular that are running into errors due to this warning from `brew doctor`. Also note that this warning message didn&#39;t always print out when I ran `brew doctor`, it was something that started to appear recently. Also, I am using Python 2.7 on my computer, trying to stay away from Python 3.

Homebrew brew doctor warning about /Library/Frameworks/Python.framework, even with brew&#39;s Python installed

I am trying to run a very simple program. To open and jpg file and display it using the opencv library for python. Initially it all worked fine but now it just opens a window which doesn&#39;t show the image but says &#39;not responding&#39;. I need to go to the task manager and close it!
    
    from numpy import *
    import matplotlib as plt
    import cv2

    img = cv2.imread(&#39;amandapeet.jpg&#39;)
    print img.shape

    cv2.imshow(&#39;Amanda&#39;, img)

cv2.imshow() function is opening a window that always says not responding - python opencv

An SQL query gives me a list of tuples, like this:

    [(elt1, elt2), (elt1, elt2), (elt1, elt2), (elt1, elt2), (elt1, elt2), ...]

I&#39;d like to have all the first elements of each tuple. Right now I use this:

    rows = cur.fetchall()
    res_list = []
    for row in rows:
        res_list += [row[0]]

But I think there might be a better syntax to do it. Do you know a better way?

Get the first element of each tuple in a list in Python

I&#39;m using MongoDB and have a collection with roughly 75 million records.
I have added a compound index on two &quot;fields&quot; by using the following command: 

    db.my_collection.ensureIndex({&quot;data.items.text&quot;:1, &quot;created_at&quot;:1},{background:true}).

Two days later I&#39;m trying to see the status of the index creation. Running `db.currentOp()` returns `{}`, however when I try to create another index I get this error message:

    cannot add index with a background operation in progress.

Is there a way to check the status/progress of the index creation job?

One thing to add - I am using mongodb version 2.0.6. Thanks!




mongodb status of index creation job

I&#39;m confused about the rules Pandas uses when deciding that a selection from a dataframe is a copy of the original dataframe, or a view on the original.

If I have, for example,

    df = pd.DataFrame(np.random.randn(8,8), columns=list(&#39;ABCDEFGH&#39;), index=range(1,9))

I understand that a `query` returns a copy so that something like

    foo = df.query(&#39;2 &lt; index &lt;= 5&#39;)
    foo.loc[:,&#39;E&#39;] = 40

will have no effect on the original dataframe, `df`. I also understand that scalar or named slices return a view, so that assignments to these, such as 

    df.iloc[3] = 70

or 

    df.ix[1,&#39;B&#39;:&#39;E&#39;] = 222

will change `df`. But I&#39;m lost when it comes to more complicated cases. For example, 

    df[df.C &lt;= df.B] = 7654321

changes `df`, but

    df[df.C &lt;= df.B].ix[:,&#39;B&#39;:&#39;E&#39;]

does not.

Is there a simple rule that Pandas is using that I&#39;m just missing? What&#39;s going on in these specific cases; and in particular, how do I change all values (or a subset of values) in a dataframe that satisfy a particular query (as I&#39;m attempting to do in the last example above)?

---

Note: This is not the same as [this question][1]; and I have read [the documentation][2], but am not enlightened by it. I&#39;ve also read through the &quot;Related&quot; questions on this topic, but I&#39;m still missing the simple rule Pandas is using, and how I&#39;d apply it to — for example —&#160;modify the values (or a subset of values) in a dataframe that satisfy a particular query.


  [1]: https://stackoverflow.com/q/17960511/656912
  [2]: http://pandas.pydata.org/pandas-docs/dev/indexing.html#returning-a-view-versus-a-copy

What rules does Pandas use to generate a view vs a copy?

As I can understand [documentation][1] the following definitions are equivalent:

    create table foo (
        id serial primary key,
        code integer,
        label text,
        constraint foo_uq unique (code, label));

    create table foo (
        id serial primary key,
        code integer,
        label text);
    create unique index foo_idx on foo using btree (code, label);    

However, a note in [the manual for Postgres 9.4][2] says:

&gt; The preferred way to add a unique constraint to a table is `ALTER TABLE ... ADD CONSTRAINT`. The use of indexes to enforce unique constraints
&gt; could be considered an implementation detail that should not be
&gt; accessed directly.

(Edit: this note was removed from the manual with Postgres 9.5.)

Is it only a matter of good style? What are practical consequences of choice one of these variants (e.g. in performance)?


  [1]: https://www.postgresql.org/docs/current/indexes-unique.html
  [2]: https://www.postgresql.org/docs/9.4/indexes-unique.html

Postgres unique constraint vs index

I have a `DataFrame` that looks like

      Emp1    Empl2           date       Company
    0    0        0     2012-05-01         apple
    1    0        1     2012-05-29         apple
    2    0        1     2013-05-02         apple
    3    0        1     2013-11-22         apple
    18   1        0     2011-09-09        google
    19   1        0     2012-02-02        google
    20   1        0     2012-11-26        google
    21   1        0     2013-05-11        google

I want to pass the company and date for setting a `MultiIndex` for this `DataFrame`. Currently it has a default index. I am using `df.set_index([&#39;Company&#39;, &#39;date&#39;], inplace=True)`

    df = pd.DataFrame()
    for c in company_list:
            row = pd.DataFrame([dict(company = &#39;%s&#39; %s, date = datetime.date(2012, 05, 01))])
            df = df.append(row, ignore_index = True)
            for e in emp_list:
                dataset  = pd.read_sql(&quot;select company, emp_name, date(date), count(*) from company_table where  = &#39;&quot;+s+&quot;&#39; and emp_name = &#39;&quot;+b+&quot;&#39; group by company, date, name LIMIT 5 &quot;, con)
                    if len(dataset) == 0:
                    row = pd.DataFrame([dict(sitename=&#39;%s&#39; %s, name = &#39;%s&#39; %b, date = datetime.date(2012, 05, 01), count = np.nan)])
                    dataset = dataset.append(row, ignore_index=True)
                dataset = dataset.rename(columns = {&#39;count&#39;: &#39;%s&#39; %b})
                dataset = dataset.groupby([&#39;company&#39;, &#39;date&#39;, &#39;emp_name&#39;], as_index = False).sum()
                
                dataset = dataset.drop(&#39;emp_name&#39;, 1)
                df = pd.merge(df, dataset, how = &#39;&#39;)
                df = df.sort(&#39;date&#39;, ascending = True)
                df.fillna(0, inplace = True)

    df.set_index([&#39;Company&#39;, &#39;date&#39;], inplace=True)            
    print df

But when I print this `DataFrame`, it prints `None`. Is this not the correct way of doing it? Also I want to shuffle the positions of the columns company and date so that company becomes the first index, and date becomes the second in Hierarchy. Any ideas on this?

Set MultiIndex of an existing DataFrame in pandas

I know the importance of indexes and how order of joins can change performance. I&#39;ve done a bunch of reading related to multi-column indexes and haven&#39;t found the answer to my question.

I&#39;m curious if I do a multi-column index, if the order that they are specified matters at all. My guess is that it would not, and that the engine would treat them as a group, where ordering doesn&#39;t matter. But I wish to verify.

For example, from mysql&#39;s website (http://dev.mysql.com/doc/refman/5.0/en/multiple-column-indexes.html)

    CREATE TABLE test (
        id         INT NOT NULL,
        last_name  CHAR(30) NOT NULL,
        first_name CHAR(30) NOT NULL,
        PRIMARY KEY (id),
        INDEX name (last_name,first_name)
    );

Would there be any benifit in any cases where the following would be better, or is it equivalent?

    CREATE TABLE test (
        id         INT NOT NULL,
        last_name  CHAR(30) NOT NULL,
        first_name CHAR(30) NOT NULL,
        PRIMARY KEY (id),
        INDEX name (first_name,last_name)
    );

Specificially:

    INDEX name (last_name,first_name)

vs

    INDEX name (first_name,last_name)

Does Order of Fields of Multi-Column Index in MySQL Matter

I was trying to generate a Report using Export to Excell, PDF, TextFile. Well I am doing this in MVC. I have a class which I named SPBatch (which is the exact name of my Stored Procedure in my SQL) and it contains the following:

    public string BatchNo { get; set; }
    public string ProviderName { get; set; }
    public Nullable&lt;System.Int32&gt; NoOfClaims { get; set; }
    public Nullable&lt;System.Int32&gt; TotalNoOfClaims { get; set; }
    public Nullable&lt;System.Decimal&gt; TotalBilled { get; set; }
    public Nullable&lt;System.Decimal&gt; TotalInputtedBill { get; set; }
    public Nullable&lt;System.DateTime&gt; DateCreated { get; set; }
    public Nullable&lt;System.DateTime&gt; DateSubmitted { get; set; }
    public Nullable&lt;System.DateTime&gt; DueDate { get; set; }
    public string Status { get; set; }
    public string RefNo { get; set; }
    public string BatchStatus { get; set; }
    public string ClaimType { get; set; }

as you can see some of my Columns are declared as Nullable. It went smoothly from searching and displaying the results in a table. I have several buttons below which are image buttons for export and every time I try to export in Excel, I always get the problem &quot;**DataSet does not support System.Nullable&lt;&gt;**&quot; in this part of my code:

    foreach (MemberInfo mi in miArray)
    {
        if (mi.MemberType == MemberTypes.Property)
        {
            PropertyInfo pi = mi as PropertyInfo;
            dt.Columns.Add(pi.Name, pi.PropertyType); //where the error pop&#39;s up.
    
        }
        else if (mi.MemberType == MemberTypes.Field)
        {
            FieldInfo fi = mi as FieldInfo;
            dt.Columns.Add(fi.Name, fi.FieldType);
        }
    }

the error shows up on the one with a comment. Can you help me what to do? I tried adding  DBNull in my code but still I get the same error. I tried removing Nullable in my SPBatch but I get an error that some tables are need to be declared as Nullable. 

What should I do?


Content Type	Original Author	Original Content on Stackoverflow
Question	lsheng	View Question on Stackoverflow
Solution 1 - Pandas	EdChum	View Answer on Stackoverflow
Solution 2 - Pandas	Anurag Singh	View Answer on Stackoverflow
Solution 3 - Pandas	GTaylor	View Answer on Stackoverflow

Export from pandas to_excel without row names (index)?

Pandas Problem Overview

Pandas Solutions

Solution 1 - Pandas

Solution 2 - Pandas

Solution 3 - Pandas

react.js custom events for communicating with parent nodes

difference between find and index

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