I&#39;d do it like this: 

    colnames=[&#39;TIME&#39;, &#39;X&#39;, &#39;Y&#39;, &#39;Z&#39;] 
    user1 = pd.read_csv(&#39;dataset/1.csv&#39;, names=colnames, header=None)

If we are directly use data from csv it will give combine data based on comma separation value as it is .csv file.

    user1 = pd.read_csv(&#39;dataset/1.csv&#39;)

If you want to add column names using pandas, you have to do something like this. But below code will not show separate header for your columns.

    col_names=[&#39;TIME&#39;, &#39;X&#39;, &#39;Y&#39;, &#39;Z&#39;] 
    user1 = pd.read_csv(&#39;dataset/1.csv&#39;, names=col_names)

To solve above problem we have to add extra filled which is supported by pandas, It is **header=None**

    user1 = pd.read_csv(&#39;dataset/1.csv&#39;, names=col_names, header=None)

we can do it with a single line of code.

     user1 = pd.read_csv(&#39;dataset/1.csv&#39;, names=[&#39;TIME&#39;, &#39;X&#39;, &#39;Y&#39;, &#39;Z&#39;], header=None)

In addition to above answers if your dataset already has column names and you want to replace it with your custom names then set `header=0` instead of `header=None` 

    user1  = pd.read_csv(&#39;dataset/1.csv&#39;,  names=[&#39;Time&#39;,  &#39;X&#39;,  &#39;Y&#39;,  &#39;Z&#39;]) 


names parameter in read_csv function is used to define  column names. If you pass extra name in this list,  it will add another new column with that name with NaN values. 

header=None is used to trim column names is already exists in CSV file. 

Use `nrows=1` to avoid consuming loading time if the dataset is too large.

I am running a container on a VM. My container is writing logs by default to /var/lib/docker/containers/CONTAINER_ID/CONTAINER_ID-json.log file until the disk is full. 

Currently, I have to delete manually this file to avoid the disk to be full. I read that in Docker 1.8 there will be a parameter to [rotate the logs][1]. 
What would you recommend as the current workaround?

  [1]: https://github.com/docker/docker/pull/11485

Docker container logs taking all my disk space

Given a simple class

    class Foo {
      constructor(x) {
        if (!(this instanceof Foo)) return new Foo(x);
        this.x = x;
      }
      hello() {
        return `hello ${this.x}`;
      }
    }

Is it possible to call the class constructor without the `new` keyword?


Usage should allow

    (new Foo(&quot;world&quot;)).hello(); // &quot;hello world&quot;

Or

    Foo(&quot;world&quot;).hello();       // &quot;hello world&quot;

But the latter fails with

&lt;!-- language: lang-none --&gt;

    Cannot call a class as a function

ES6: call class constructor without new keyword

This is the example of my dataset.

    &gt;&gt;&gt; user1 = pd.read_csv(&#39;dataset/1.csv&#39;)
    &gt;&gt;&gt; print(user1)
              0  0.69464   3.1735   7.5048
    0  0.030639  0.14982  3.48680   9.2755
    1  0.069763 -0.29965  1.94770   9.1120
    2  0.099823 -1.68890  1.41650  10.1200
    3  0.129820 -2.17930  0.95342  10.9240
    4  0.159790 -2.30180  0.23155  10.6510
    5  0.189820 -1.41650  1.18500  11.0730

How to push down the first column and add the names column [TIME, X, Y, and Z] on the first column.

The desired output is like this:
       

           TIME        X        Y        Z
    0         0  0.69464   3.1735   7.5048
    1  0.030639  0.14982  3.48680   9.2755
    2  0.069763 -0.29965  1.94770   9.1120
    3  0.099823 -1.68890  1.41650  10.1200
    4  0.129820 -2.17930  0.95342  10.9240
    5  0.159790 -2.30180  0.23155  10.6510
    6  0.189820 -1.41650  1.18500  11.0730

Give column name when read csv file pandas

This is the example of my dataset.
<pre><code class="hljs language-python-repl">>>> user1 = pd.read_csv('dataset/1.csv')
>>> print(user1)
 0 0.69464 3.1735 7.5048
0 0.030639 0.14982 3.48680 9.2755
1 0.069763 -0.29965 1.94770 9.1120
2 0.099823 -1.68890 1.41650 10.1200
3 0.129820 -2.17930 0.95342 10.9240
4 0.159790 -2.30180 0.23155 10.6510
5 0.189820 -1.41650 1.18500 11.0730
</code></pre>
How to push down the first column and add the names column [TIME, X, Y, and Z] on the first column.
The desired output is like this:
<pre><code class="hljs language-css"> TIME X Y Z
0 0 0.69464 3.1735 7.5048
1 0.030639 0.14982 3.48680 9.2755
2 0.069763 -0.29965 1.94770 9.1120
3 0.099823 -1.68890 1.41650 10.1200
4 0.129820 -2.17930 0.95342 10.9240
5 0.159790 -2.30180 0.23155 10.6510
6 0.189820 -1.41650 1.18500 11.0730
</code></pre>

I have the following for loop:

    for i in links:
         data = urllib2.urlopen(str(i)).read()
         data = json.loads(data)
         data = pd.DataFrame(data.items())
         data = data.transpose()
         data.columns = data.iloc[0]
         data = data.drop(data.index[[0]])
         
Each dataframe so created has most columns in common with the others but not all of them. Moreover, they all have just one row. What I need to to is to add to the dataframe all the distinct columns and each row from each dataframe produced by the for loop

I tried pandas concatenate or similar but nothing seemed to work. Any idea? Thanks.


How to append rows in a pandas dataframe in a for loop?

Introduction
============

For Java, Dependency Injection works as pure OOP, i.e. you provide an interface to be implemented and in your framework code accept an instance of a class that implements the defined interface.

Now for Python, you are able to do the same way, but I think that method was too much overhead right in case of Python. So then how would you implement it in the Pythonic way?

Use Case
========

Say this is the framework code:

    class FrameworkClass():
        def __init__(self, ...):
            ...
    
        def do_the_job(self, ...):
            # some stuff
            # depending on some external function

The Basic Approach
==================

The most naive (and maybe the best?) way is to require the external function to be supplied into the `FrameworkClass` constructor, and then be invoked from the `do_the_job` method.

***Framework Code:***

    class FrameworkClass():
        def __init__(self, func):
            self.func = func
    
        def do_the_job(self, ...):
            # some stuff
            self.func(...)

***Client Code:***

    def my_func():
        # my implementation
    
    framework_instance = FrameworkClass(my_func)
    framework_instance.do_the_job(...)

Question
========

The question is short. Is there any better commonly used Pythonic way to do this? Or maybe any libraries supporting such functionality?

UPDATE: Concrete Situation
==========================

Imagine I develop a micro web framework, which handles authentication using tokens. This framework needs a function to supply some `ID` obtained from the token and get the user corresponding to that `ID`.

Obviously, the framework does not know anything about users or any other application specific logic, so the client code must inject the user getter functionality into the framework to make the authentication work.

What is a Pythonic way for Dependency Injection?

Using the **code** below for svm in python:  

    from sklearn import datasets
    from sklearn.multiclass import OneVsRestClassifier
    from sklearn.svm import SVC
    iris = datasets.load_iris()
    X, y = iris.data, iris.target
    clf = OneVsRestClassifier(SVC(kernel=&#39;linear&#39;, probability=True, class_weight=&#39;auto&#39;))
    clf.fit(X, y)
    proba = clf.predict_proba(X)

But it is taking a huge amount of time.

**Actual Data Dimensions**:

    train-set (1422392,29)
    test-set (233081,29)
How can I speed it up(parallel or some other way)? Please help.
I have already tried PCA and downsampling. 

I have 6 classes.
Edit:
Found http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html
but I wish for probability estimates and it seems not to so for svm.

**Edit:**

    from sklearn import datasets
    from sklearn.multiclass import OneVsRestClassifier
    from sklearn.svm import SVC,LinearSVC
    from sklearn.linear_model import SGDClassifier
    import joblib
    import numpy as np
    from sklearn import grid_search
    import multiprocessing
    import numpy as np
    import math
    
    def new_func(a):                              #converts array(x) elements to (1/(1 + e(-x)))
    	a=1/(1 + math.exp(-a))
    	return a
    	
    if __name__ == &#39;__main__&#39;:
    	iris = datasets.load_iris()
    	cores=multiprocessing.cpu_count()-2
    	X, y = iris.data, iris.target						#loading dataset
    
    	C_range = 10.0 ** np.arange(-4, 4);					 #c value range 
    	param_grid = dict(estimator__C=C_range.tolist())              
    	
    	svr = OneVsRestClassifier(LinearSVC(class_weight=&#39;auto&#39;),n_jobs=cores) ################LinearSVC Code faster		
    	#svr = OneVsRestClassifier(SVC(kernel=&#39;linear&#39;, probability=True,  ##################SVC code slow
    	#	class_weight=&#39;auto&#39;),n_jobs=cores)
    	
    	clf = grid_search.GridSearchCV(svr, param_grid,n_jobs=cores,verbose=2)  #grid search
    	clf.fit(X, y)													#training svm model										
    	
    	decisions=clf.decision_function(X)                             #outputs decision functions
    	#prob=clf.predict_proba(X)                                     #only for SVC outputs probablilites
    	print decisions[:5,:]
    	vecfunc = np.vectorize(new_func)
    	prob=vecfunc(decisions)                                        #converts deicision to (1/(1 + e(-x)))
    	print prob[:5,:]

**Edit 2:**
The answer by user3914041 yields very poor probability estimates.

Making SVM run faster in python

I know how to use the `zip()` function in Python 3. My question is regarding the following which I somehow feel quite peculiar:

I define two lists:

    lis1 = [0, 1, 2, 3]
    lis2 = [4, 5, 6, 7]

and I use the `zip()` on these in the following ways:

    1. test1 = zip( lis1, lis2)

    2. test2 = list(zip(lis1, lis2))

when I type `test1` at the interpreter, I get this:

    &quot;zip object at 0x1007a06c8&quot;

So, I type `list(test1)` at the interpreter and I get the intended result, but  when I type `list(test1)` again, I get an empty list.

What I find peculiar is that no matter how many times I type `test2` at the interpreter I always get the intended result and never an empty list.



The zip() function in Python 3

Sometimes I download the python source code from `github` and don&#39;t know how to install all the dependencies. If there is no `requirements.txt` file I have to create it by hands.  
The question is:  
Given the python source code directory is it possible to create `requirements.txt` automatically from the import section?
 

Automatically create requirements.txt

I am importing a CSV file like the one below, using `pandas.read_csv`:

    df = pd.read_csv(Input, delimiter=&quot;;&quot;)

Example of CSV file:

    10;01.02.2015 16:58;01.02.2015 16:58;-0.59;0.1;-4.39;NotApplicable;0.79;0.2
    11;01.02.2015 16:58;01.02.2015 16:58;-0.57;0.2;-2.87;NotApplicable;0.79;0.21

The problem is that when I later on in my code try to use these values I get this error: `TypeError: can&#39;t multiply sequence by non-int of type &#39;float&#39;` 

**The error is because the number I&#39;m trying to use is not written with a dot (`.`) as a decimal separator but a comma(`,`)**. After manually changing the commas to a dots my program works.

I can&#39;t change the format of my input, and thus have to replace the commas in my DataFrame in order for my code to work, and I want python to do this without the need of doing it manually. Do you have any suggestions?

Convert commas decimal separators to dots within a Dataframe

I am a little confused about how this code works:

    fig, axes = plt.subplots(nrows=2, ncols=2)
    plt.show()

How does the fig, axes work in this case? What does it do?

Also why wouldn&#39;t this work to do the same thing:

    fig = plt.figure()
    axes = fig.subplots(nrows=2, ncols=2)



How to plot in multiple subplots

How do I get the name of a DataFrame and print it as a string? 

Example: 

`boston` (var name assigned to a csv file)

    import pandas as pd
    boston = pd.read_csv(&#39;boston.csv&#39;)

    print(&#39;The winner is team A based on the %s table.) % boston

Get the name of a pandas DataFrame

I would like to convert everything but the first column of a pandas dataframe into a numpy array. For some reason using the `columns=` parameter of `DataFrame.to_matrix()` is not working.

df:

      viz  a1_count  a1_mean     a1_std
    0   n         3        2   0.816497
    1   n         0      NaN        NaN 
    2   n         2       51  50.000000

I tried `X=df.as_matrix(columns=[df[1:]])` but this yields an array of all `NaN`s

Content Type	Original Author	Original Content on Stackoverflow
Question	markov zain	View Question on Stackoverflow
Solution 1 - Python	wmoco_6725	View Answer on Stackoverflow
Solution 2 - Python	Jitesh Mohite	View Answer on Stackoverflow
Solution 3 - Python	Hariharan AR	View Answer on Stackoverflow
Solution 4 - Python	Mohit Burkule	View Answer on Stackoverflow
Solution 5 - Python	Mahendra	View Answer on Stackoverflow
Solution 6 - Python	Jiaxiang	View Answer on Stackoverflow

Give column name when read csv file pandas

Python Problem Overview

Python Solutions

Solution 1 - Python

Solution 2 - Python

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Solution 6 - Python

ES6: call class constructor without new keyword

Docker container logs taking all my disk space

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