IPython: Productive Interactive Computing

Overview

Welcome to IPython. Our full documentation is available on ipython.readthedocs.io and contains information on how to install, use, and contribute to the project. IPython (Interactive Python) is a command shell for interactive computing in multiple programming languages, originally developed for the Python programming language, that offers introspection, rich media, shell syntax, tab completion, and history.

IPython versions and Python Support

Starting with IPython 7.10, IPython follows NEP 29

IPython 7.17+ requires Python version 3.7 and above.

IPython 7.10+ requires Python version 3.6 and above.

IPython 7.0 requires Python version 3.5 and above.

IPython 6.x requires Python version 3.3 and above.

IPython 5.x LTS is the compatible release for Python 2.7. If you require Python 2 support, you must use IPython 5.x LTS. Please update your project configurations and requirements as necessary.

The Notebook, Qt console and a number of other pieces are now parts of Jupyter. See the Jupyter installation docs if you want to use these.

Main features of IPython

Comprehensive object introspection.

Input history, persistent across sessions.

Caching of output results during a session with automatically generated references.

Extensible tab completion, with support by default for completion of python variables and keywords, filenames and function keywords.

Extensible system of ‘magic’ commands for controlling the environment and performing many tasks related to IPython or the operating system.

A rich configuration system with easy switching between different setups (simpler than changing $PYTHONSTARTUP environment variables every time).

Session logging and reloading.

Extensible syntax processing for special purpose situations.

Access to the system shell with user-extensible alias system.

Easily embeddable in other Python programs and GUIs.

Integrated access to the pdb debugger and the Python profiler.

Development and Instant running

You can find the latest version of the development documentation on readthedocs.

You can run IPython from this directory without even installing it system-wide by typing at the terminal:

$ python -m IPython

Or see the development installation docs for the latest revision on read the docs.

Documentation and installation instructions for older version of IPython can be found on the IPython website

IPython requires Python version 3 or above

Starting with version 6.0, IPython does not support Python 2.7, 3.0, 3.1, or 3.2.

For a version compatible with Python 2.7, please install the 5.x LTS Long Term Support version.

If you are encountering this error message you are likely trying to install or use IPython from source. You need to checkout the remote 5.x branch. If you are using git the following should work:

$ git fetch origin
$ git checkout 5.x

If you encounter this error message with a regular install of IPython, then you likely need to update your package manager, for example if you are using pip check the version of pip with:

$ pip --version

You will need to update pip to the version 9.0.1 or greater. If you are not using pip, please inquiry with the maintainers of the package for your package manager.

For more information see one of our blog posts:

https://blog.jupyter.org/release-of-ipython-5-0-8ce60b8d2e8e

As well as the following Pull-Request for discussion:

https://github.com/ipython/ipython/pull/9900

This error does also occur if you are invoking setup.py directly – which you should not – or are using easy_install If this is the case, use pip install . instead of setup.py install , and pip install -e . instead of setup.py develop If you are depending on IPython as a dependency you may also want to have a conditional dependency on IPython depending on the Python version:

install_req = ['ipython']
if sys.version_info[0] < 3 and 'bdist_wheel' not in sys.argv:
    install_req.remove('ipython')
    install_req.append('ipython<6')

setup(
    ...
    install_requires=install_req
)

Alternatives to IPython

IPython may not be to your taste; if that's the case there might be similar project that you might want to use:

• The classic Python REPL.
• bpython
• mypython
• ptpython and ptipython
• Xonsh

Ignoring commits with git blame.ignoreRevsFile

As of git 2.23, it is possible to make formatting changes without breaking git blame. See the git documentation for more details.

To use this feature you must:

• Install git >= 2.23

Configure your local git repo by running:

• POSIX: tools\configure-git-blame-ignore-revs.sh
• Windows: tools\configure-git-blame-ignore-revs.bat

Download Details:
Author: ipython
Source Code: https://github.com/ipython/ipython
License: View license

#python 

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An Overview of our PH Launch for AskMakers v2.0

Oh…I am so sleepy…because I launched AskMakers 2.0 on Product Hunt and I have been monitoring it almost without sleeping😪

The first version of it was launched about 10 months ago.

I worked so hard on it and it got over 100 upvotes within 24 hours for the first time🤩To make an app useful, I sent emails to ask successful makers to join AskMakers.

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How to Predict Housing Prices with Linear Regression?

The final objective is to estimate the cost of a certain house in a Boston suburb. In 1970, the Boston Standard Metropolitan Statistical Area provided the information. To examine and modify the data, we will use several techniques such as data pre-processing and feature engineering. After that, we'll apply a statistical model like regression model to anticipate and monitor the real estate market.

Project Outline:

• EDA
• Feature Engineering
• Pick and Train a Model
• Interpret
• Conclusion

EDA

Before using a statistical model, the EDA is a good step to go through in order to:

• Recognize the data set
• Check to see if any information is missing.
• Find some outliers.
• To get more out of the data, add, alter, or eliminate some features.

Importing the Libraries

• Recognize the data set
• Check to see if any information is missing.
• Find some outliers.
• To get more out of the data, add, alter, or eliminate some features.

# Import the libraries #Dataframe/Numerical libraries import pandas as pd import numpy as np #Data visualization import plotly.express as px import matplotlib import matplotlib.pyplot as plt import seaborn as sns #Machine learning model from sklearn.linear_model import LinearRegression

Reading the Dataset with Pandas

#Reading the data path='./housing.csv' housing_df=pd.read_csv(path,header=None,delim_whitespace=True)

	CRIM	ZN	INDUS	CHAS	NOX	RM	AGE	DIS	RAD	TAX	PTRATIO	B	LSTAT	MEDV
0	0.00632	18.0	2.31	0	0.538	6.575	65.2	4.0900	1	296.0	15.3	396.90	4.98	24.0
1	0.02731	0.0	7.07	0	0.469	6.421	78.9	4.9671	2	242.0	17.8	396.90	9.14	21.6
2	0.02729	0.0	7.07	0	0.469	7.185	61.1	4.9671	2	242.0	17.8	392.83	4.03	34.7
3	0.03237	0.0	2.18	0	0.458	6.998	45.8	6.0622	3	222.0	18.7	394.63	2.94	33.4
4	0.06905	0.0	2.18	0	0.458	7.147	54.2	6.0622	3	222.0	18.7	396.90	5.33	36.2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
501	0.06263	0.0	11.93	0	0.573	6.593	69.1	2.4786	1	273.0	21.0	391.99	9.67	22.4
502	0.04527	0.0	11.93	0	0.573	6.120	76.7	2.2875	1	273.0	21.0	396.90	9.08	20.6
503	0.06076	0.0	11.93	0	0.573	6.976	91.0	2.1675	1	273.0	21.0	396.90	5.64	23.9
504	0.10959	0.0	11.93	0	0.573	6.794	89.3	2.3889	1	273.0	21.0	393.45	6.48	22.0
505	0.04741	0.0	11.93	0	0.573	6.030	80.8	2.5050	1	273.0	21.0	396.90	7.88	11.9

Have a Look at the Columns

Crime: It refers to a town's per capita crime rate.

ZN: It is the percentage of residential land allocated for 25,000 square feet.

Indus: The amount of non-retail business lands per town is referred to as the indus.

CHAS: CHAS denotes whether or not the land is surrounded by a river.

NOX: The NOX stands for nitric oxide content (part per 10m)

RM: The average number of rooms per home is referred to as RM.

AGE: The percentage of owner-occupied housing built before 1940 is referred to as AGE.

DIS: Weighted distance to five Boston employment centers are referred to as dis.

RAD: Accessibility to radial highways index

TAX: The TAX columns denote the rate of full-value property taxes per $10,000 dollars.

B: B=1000(Bk — 0.63)2 is the outcome of the equation, where Bk is the proportion of blacks in each town.

PTRATIO: It refers to the student-to-teacher ratio in each community.

LSTAT: It refers to the population's lower socioeconomic status.

MEDV: It refers to the 1000-dollar median value of owner-occupied residences.

Data Preprocessing

# Check if there is any missing values. housing_df.isna().sum() CRIM       0 ZN         0 INDUS      0 CHAS       0 NOX        0 RM         0 AGE        0 DIS        0 RAD        0 TAX        0 PTRATIO    0 B          0 LSTAT      0 MEDV       0 dtype: int64

No missing values are found

We examine our data's mean, standard deviation, and percentiles.

housing_df.describe()

	CRIM	ZN	INDUS	CHAS	NOX	RM	AGE	DIS	RAD	TAX	PTRATIO	B	LSTAT	MEDV
count	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000
mean	3.613524	11.363636	11.136779	0.069170	0.554695	6.284634	68.574901	3.795043	9.549407	408.237154	18.455534	356.674032	12.653063	22.532806
std	8.601545	23.322453	6.860353	0.253994	0.115878	0.702617	28.148861	2.105710	8.707259	168.537116	2.164946	91.294864	7.141062	9.197104
min	0.006320	0.000000	0.460000	0.000000	0.385000	3.561000	2.900000	1.129600	1.000000	187.000000	12.600000	0.320000	1.730000	5.000000
25%	0.082045	0.000000	5.190000	0.000000	0.449000	5.885500	45.025000	2.100175	4.000000	279.000000	17.400000	375.377500	6.950000	17.025000
50%	0.256510	0.000000	9.690000	0.000000	0.538000	6.208500	77.500000	3.207450	5.000000	330.000000	19.050000	391.440000	11.360000	21.200000
75%	3.677083	12.500000	18.100000	0.000000	0.624000	6.623500	94.075000	5.188425	24.000000	666.000000	20.200000	396.225000	16.955000	25.000000
max	88.976200	100.000000	27.740000	1.000000	0.871000	8.780000	100.000000	12.126500	24.000000	711.000000	22.000000	396.900000	37.970000	50.000000

The crime, area, sector, nitric oxides, 'B' appear to have multiple outliers at first look because the minimum and maximum values are so far apart. In the Age columns, the mean and the Q2(50 percentile) do not match.

We might double-check it by examining the distribution of each column.

Inferences

1. The rate of crime is rather low. The majority of values are in the range of 0 to 25. With a huge value and a value of zero.
2. The majority of residential land is zoned for less than 25,000 square feet. Land zones larger than 25,000 square feet represent a small portion of the dataset.
3. The percentage of non-retial commercial acres is mostly split between two ranges: 0-13 and 13-23.
4. The majority of the properties are bordered by the river, although a tiny portion of the data is not.
5. The content of nitrite dioxide has been trending lower from.3 to.7, with a little bump towards.8. It is permissible to leave a value in the range of 0.1–1.
6. The number of rooms tends to cluster around the average.
7. With time, the proportion of owner-occupied units rises.
8. As the number of weights grows, the weight distance between 5 employment centers reduces. It could indicate that individuals choose to live in new high-employment areas.
9. People choose to live in places with limited access to roadways (0-10). We have a 30th percentile outlier.
10. The majority of dwelling taxes are in the range of $200-450, with large outliers around $700,000.
11. The percentage of people with lower status tends to cluster around the median. The majority of persons are of lower social standing.

Because the model is overly generic, removing all outliers will underfit it. Keeping all outliers causes the model to overfit and become excessively accurate. The data's noise will be learned.

The approach is to establish a happy medium that prevents the model from becoming overly precise. When faced with a new set of data, however, they generalise well.

We'll keep numbers below 600 because there's a huge anomaly in the TAX column around 600.

new_df=housing_df[housing_df['TAX']<600]

Looking at the Distribution

The overall distribution, particularly the TAX, PTRATIO, and RAD, has improved slightly.

Correlation

Perfect correlation is denoted by the clear values. The medium correlation between the columns is represented by the reds, while the negative correlation is represented by the black.

With a value of 0.89, we can see that 'MEDV', which is the medium price we wish to anticipate, is substantially connected with the number of rooms 'RM'. The proportion of black people in area 'B' with a value of 0.19 is followed by the residential land 'ZN' with a value of 0.32 and the percentage of black people in area 'ZN' with a value of 0.32.

The metrics that are most connected with price will be plotted.

Feature Engineering

Feature Scaling

Gradient descent is aided by feature scaling, which ensures that all features are on the same scale. It makes locating the local optimum much easier.

Mean standardization is one strategy to employ. It substitutes (target-mean) for the target to ensure that the feature has a mean of nearly zero.

def standard(X):    '''Standard makes the feature 'X' have a zero mean'''    mu=np.mean(X) #mean    std=np.std(X) #standard deviation    sta=(X-mu)/std # mean normalization    return mu,std,sta     mu,std,sta=standard(X) X=sta X

	CRIM	ZN	INDUS	CHAS	NOX	RM	AGE	DIS	RAD	TAX	PTRATIO	B	LSTAT
0	-0.609129	0.092792	-1.019125	-0.280976	0.258670	0.279135	0.162095	-0.167660	-2.105767	-0.235130	-1.136863	0.401318	-0.933659
1	-0.575698	-0.598153	-0.225291	-0.280976	-0.423795	0.049252	0.648266	0.250975	-1.496334	-1.032339	-0.004175	0.401318	-0.219350
2	-0.575730	-0.598153	-0.225291	-0.280976	-0.423795	1.189708	0.016599	0.250975	-1.496334	-1.032339	-0.004175	0.298315	-1.096782
3	-0.567639	-0.598153	-1.040806	-0.280976	-0.532594	0.910565	-0.526350	0.773661	-0.886900	-1.327601	0.403593	0.343869	-1.283945
4	-0.509220	-0.598153	-1.040806	-0.280976	-0.532594	1.132984	-0.228261	0.773661	-0.886900	-1.327601	0.403593	0.401318	-0.873561
...	...	...	...	...	...	...	...	...	...	...	...	...	...
501	-0.519445	-0.598153	0.585220	-0.280976	0.604848	0.306004	0.300494	-0.936773	-2.105767	-0.574682	1.445666	0.277056	-0.128344
502	-0.547094	-0.598153	0.585220	-0.280976	0.604848	-0.400063	0.570195	-1.027984	-2.105767	-0.574682	1.445666	0.401318	-0.229652
503	-0.522423	-0.598153	0.585220	-0.280976	0.604848	0.877725	1.077657	-1.085260	-2.105767	-0.574682	1.445666	0.401318	-0.820331
504	-0.444652	-0.598153	0.585220	-0.280976	0.604848	0.606046	1.017329	-0.979587	-2.105767	-0.574682	1.445666	0.314006	-0.676095
505	-0.543685	-0.598153	0.585220	-0.280976	0.604848	-0.534410	0.715691	-0.924173	-2.105767	-0.574682	1.445666	0.401318	-0.435703

Choose and Train the Model

For the sake of the project, we'll apply linear regression.

Typically, we run numerous models and select the best one based on a particular criterion.

Linear regression is a sort of supervised learning model in which the response is continuous, as it relates to machine learning.

Form of Linear Regression

y= θX+θ1 or y= θ1+X1θ2 +X2θ3 + X3θ4

y is the target you will be predicting

0 is the coefficient

x is the input

We will Sklearn to develop and train the model

#Import the libraries to train the model from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression

Allow us to utilise the train/test method to learn a part of the data on one set and predict using another set using the train/test approach.

X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.4) #Create and Train the model model=LinearRegression().fit(X_train,y_train) #Generate prediction predictions_test=model.predict(X_test) #Compute loss to evaluate the model coefficient= model.coef_ intercept=model.intercept_ print(coefficient,intercept) [7.22218258] 24.66379606613584

In this example, you will learn the model using below hypothesis:

Price= 24.85 + 7.18* Room

It is interpreted as:

For a decided price of a house:

A 7.18-unit increase in the price is connected with a growth in the number of rooms.

As a side note, this is an association, not a cause!

Interpretation

You will need a metric to determine whether our hypothesis was right. The RMSE approach will be used.

Root Means Square Error (RMSE) is defined as the square root of the mean of square error. The difference between the true and anticipated numbers called the error. It's popular because it can be expressed in y-units, which is the median price of a home in our scenario.

def rmse(predict,actual):    return np.sqrt(np.mean(np.square(predict - actual))) # Split the Data into train and test set X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.4) #Create and Train the model model=LinearRegression().fit(X_train,y_train) #Generate prediction predictions_test=model.predict(X_test) #Compute loss to evaluate the model coefficient= model.coef_ intercept=model.intercept_ print(coefficient,intercept) loss=rmse(predictions_test,y_test) print('loss: ',loss) print(model.score(X_test,y_test)) #accuracy [7.43327725] 24.912055881970886 loss: 3.9673165450580714 0.7552661033654667 Loss will be 3.96

This means that y-units refer to the median value of occupied homes with 1000 dollars.

This will be less by 3960 dollars.

While learning the model you will have a high variance when you divide the data. Coefficient and intercept will vary. It's because when we utilized the train/test approach, we choose a set of data at random to place in either the train or test set. As a result, our theory will change each time the dataset is divided.

This problem can be solved using a technique called cross-validation.

Improvisation in the Model

With 'Forward Selection,' we'll iterate through each parameter to assist us choose the numbers characteristics to include in our model.

Forward Selection

1. Choose the most appropriate variable (in our case based on high correlation)
2. Add the next best variable to the model
3. Some predetermined conditions must meet.

We'll use a random state of 1 so that each iteration yields the same outcome.

cols=[] los=[] los_train=[] scor=[] i=0 while i < len(high_corr_var):    cols.append(high_corr_var[i])        # Select inputs variables    X=new_df[cols]        #mean normalization    mu,std,sta=standard(X)    X=sta        # Split the data into training and testing    X_train,X_test,y_train,y_test= train_test_split(X,y,random_state=1)        #fit the model to the training    lnreg=LinearRegression().fit(X_train,y_train)        #make prediction on the training test    prediction_train=lnreg.predict(X_train)        #make prediction on the testing test    prediction=lnreg.predict(X_test)        #compute the loss on train test    loss=rmse(prediction,y_test)    loss_train=rmse(prediction_train,y_train)    los_train.append(loss_train)    los.append(loss)        #compute the score    score=lnreg.score(X_test,y_test)    scor.append(score)        i+=1

We have a big 'loss' with a smaller collection of variables, yet our system will overgeneralize in this scenario. Although we have a reduced 'loss,' we have a large number of variables. However, if the model grows too precise, it may not generalize well to new data.

In order for our model to generalize well with another set of data, we might use 6 or 7 features. The characteristic chosen is descending based on how strong the price correlation is.

high_corr_var ['RM', 'ZN', 'B', 'CHAS', 'RAD', 'DIS', 'CRIM', 'NOX', 'AGE', 'TAX', 'INDUS', 'PTRATIO', 'LSTAT']

With 'RM' having a high price correlation and LSTAT having a negative price correlation.

# Create a list of features names feature_cols=['RM','ZN','B','CHAS','RAD','CRIM','DIS','NOX'] #Select inputs variables X=new_df[feature_cols] # Split the data into training and testing sets X_train,X_test,y_train,y_test= train_test_split(X,y, random_state=1) # feature engineering mu,std,sta=standard(X) X=sta # fit the model to the trainning data lnreg=LinearRegression().fit(X_train,y_train) # make prediction on the testing test prediction=lnreg.predict(X_test) # compute the loss loss=rmse(prediction,y_test) print('loss: ',loss) lnreg.score(X_test,y_test) loss: 3.212659865936143 0.8582338376696363

The test set yielded a loss of 3.21 and an accuracy of 85%.

Other factors, such as alpha, the learning rate at which our model learns, could still be tweaked to improve our model. Alternatively, return to the preprocessing section and working to increase the parameter distribution.

For more details regarding scraping real estate data you can contact Scraping Intelligence today

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