 1604374500

# Python: Real-time Automated Long Short Term Memory (LSTM) Short-term Load Forecasting & Plotting

Python: Real-time Automated Long Short Term Memory (LSTM) Short-term Load Forecasting & Plotting

TABLE OF CONTENT

Introduction 00:00:00

• Introduction of LSTM 00:00:52
• Introduction of RNN 00:13:03

From RNN to LSTM 00:22:56

How to build a LSTM 00:31:41

Programming Exercise 00:42:59

• Details of short-term load forecasting problem 00:43:02

Python

• Data Preparation 00:44:00
• Developing LSTM 01:03:57
• Real-time Model Prediction 01:18:19
• Real-time Plotting 1:28:10

#python #machine-learning #data-science #artificial-intelligence #developer

## Buddha Community  1652748716

## Exploratory Data Analysis Tutorial | Basics of EDA with Python

Exploratory data analysis is used by data scientists to analyze and investigate data sets and summarize their main characteristics, often employing data visualization methods. It helps determine how best to manipulate data sources to get the answers you need, making it easier for data scientists to discover patterns, spot anomalies, test a hypothesis, or check assumptions. EDA is primarily used to see what data can reveal beyond the formal modeling or hypothesis testing task and provides a better understanding of data set variables and the relationships between them. It can also help determine if the statistical techniques you are considering for data analysis are appropriate or not.

🔹 Topics Covered:
00:00:00 Basics of EDA with Python
01:40:10 Multiple Variate Analysis
02:30:26 Outlier Detection
03:44:48 Cricket World Cup Analysis using Exploratory Data Analysis

## What is Exploratory Data Analysis(EDA)?

If we want to explain EDA in simple terms, it means trying to understand the given data much better, so that we can make some sense out of it.

We can find a more formal definition in Wikipedia.

In statistics, exploratory data analysis is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task.

EDA in Python uses data visualization to draw meaningful patterns and insights. It also involves the preparation of data sets for analysis by removing irregularities in the data.

Based on the results of EDA, companies also make business decisions, which can have repercussions later.

• If EDA is not done properly then it can hamper the further steps in the machine learning model building process.
• If done well, it may improve the efficacy of everything we do next.

1. Data Sourcing
2. Data Cleaning
3. Univariate analysis
4. Bivariate analysis
5. Multivariate analysis

## 1. Data Sourcing

Data Sourcing is the process of finding and loading the data into our system. Broadly there are two ways in which we can find data.

1. Private Data
2. Public Data

Private Data

As the name suggests, private data is given by private organizations. There are some security and privacy concerns attached to it. This type of data is used for mainly organizations internal analysis.

Public Data

This type of Data is available to everyone. We can find this in government websites and public organizations etc. Anyone can access this data, we do not need any special permissions or approval.

We can get public data on the following sites.

The very first step of EDA is Data Sourcing, we have seen how we can access data and load into our system. Now, the next step is how to clean the data.

## 2. Data Cleaning

After completing the Data Sourcing, the next step in the process of EDA is Data Cleaning. It is very important to get rid of the irregularities and clean the data after sourcing it into our system.

Irregularities are of different types of data.

• Missing Values
• Incorrect Format
• Anomalies/Outliers

To perform the data cleaning we are using a sample data set, which can be found here.

We are using Jupyter Notebook for analysis.

First, let’s import the necessary libraries and store the data in our system for analysis.

``````#import the useful libraries.
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

# Read the data set of "Marketing Analysis" in data.

# Printing the data
data``````

Now, the data set looks like this,

If we observe the above dataset, there are some discrepancies in the Column header for the first 2 rows. The correct data is from the index number 1. So, we have to fix the first two rows.

This is called Fixing the Rows and Columns. Let’s ignore the first two rows and load the data again.

``````#import the useful libraries.
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

# Read the file in data without first two rows as it is of no use.

#print the head of the data frame.

Now, the dataset looks like this, and it makes more sense.

Dataset after fixing the rows and columns

Following are the steps to be taken while Fixing Rows and Columns:

1. Delete Summary Rows and Columns in the Dataset.
2. Delete Header and Footer Rows on every page.
3. Delete Extra Rows like blank rows, page numbers, etc.
4. We can merge different columns if it makes for better understanding of the data
5. Similarly, we can also split one column into multiple columns based on our requirements or understanding.
6. Add Column names, it is very important to have column names to the dataset.

Now if we observe the above dataset, the `customerid` column has of no importance to our analysis, and also the `jobedu` column has both the information of `job` and `education` in it.

So, what we’ll do is, we’ll drop the `customerid` column and we’ll split the `jobedu` column into two other columns `job` and `education` and after that, we’ll drop the `jobedu` column as well.

``````# Drop the customer id as it is of no use.
data.drop('customerid', axis = 1, inplace = True)

#Extract job  & Education in newly from "jobedu" column.
data['job']= data["jobedu"].apply(lambda x: x.split(","))
data['education']= data["jobedu"].apply(lambda x: x.split(","))

# Drop the "jobedu" column from the dataframe.
data.drop('jobedu', axis = 1, inplace = True)

# Printing the Dataset
data``````

Now, the dataset looks like this,

Dropping `Customerid `and jobedu columns and adding job and education columns

Missing Values

If there are missing values in the Dataset before doing any statistical analysis, we need to handle those missing values.

There are mainly three types of missing values.

1. MCAR(Missing completely at random): These values do not depend on any other features.
2. MAR(Missing at random): These values may be dependent on some other features.
3. MNAR(Missing not at random): These missing values have some reason for why they are missing.

Let’s see which columns have missing values in the dataset.

``````# Checking the missing values
data.isnull().sum()``````

The output will be,

As we can see three columns contain missing values. Let’s see how to handle the missing values. We can handle missing values by dropping the missing records or by imputing the values.

Drop the missing Values

Let’s handle missing values in the `age` column.

``````# Dropping the records with age missing in data dataframe.
data = data[~data.age.isnull()].copy()

# Checking the missing values in the dataset.
data.isnull().sum()``````

Let’s check the missing values in the dataset now.

Let’s impute values to the missing values for the month column.

Since the month column is of an object type, let’s calculate the mode of that column and impute those values to the missing values.

``````# Find the mode of month in data
month_mode = data.month.mode()

# Fill the missing values with mode value of month in data.
data.month.fillna(month_mode, inplace = True)

# Let's see the null values in the month column.
data.month.isnull().sum()``````

Now output is,

``````# Mode of month is
'may, 2017'
# Null values in month column after imputing with mode
0``````

Handling the missing values in the Response column. Since, our target column is Response Column, if we impute the values to this column it’ll affect our analysis. So, it is better to drop the missing values from Response Column.

``````#drop the records with response missing in data.
data = data[~data.response.isnull()].copy()
# Calculate the missing values in each column of data frame
data.isnull().sum()``````

Let’s check whether the missing values in the dataset have been handled or not,

All the missing values have been handled

We can also, fill the missing values as ‘NaN’ so that while doing any statistical analysis, it won’t affect the outcome.

Handling Outliers

We have seen how to fix missing values, now let’s see how to handle outliers in the dataset.

Outliers are the values that are far beyond the next nearest data points.

There are two types of outliers:

1. Univariate outliers: Univariate outliers are the data points whose values lie beyond the range of expected values based on one variable.
2. Multivariate outliers: While plotting data, some values of one variable may not lie beyond the expected range, but when you plot the data with some other variable, these values may lie far from the expected value.

So, after understanding the causes of these outliers, we can handle them by dropping those records or imputing with the values or leaving them as is, if it makes more sense.

Standardizing Values

To perform data analysis on a set of values, we have to make sure the values in the same column should be on the same scale. For example, if the data contains the values of the top speed of different companies’ cars, then the whole column should be either in meters/sec scale or miles/sec scale.

Now, that we are clear on how to source and clean the data, let’s see how we can analyze the data.

## 3. Univariate Analysis

If we analyze data over a single variable/column from a dataset, it is known as Univariate Analysis.

Categorical Unordered Univariate Analysis:

An unordered variable is a categorical variable that has no defined order. If we take our data as an example, the job column in the dataset is divided into many sub-categories like technician, blue-collar, services, management, etc. There is no weight or measure given to any value in the ‘job’ column.

Now, let’s analyze the job category by using plots. Since Job is a category, we will plot the bar plot.

``````# Let's calculate the percentage of each job status category.
data.job.value_counts(normalize=True)

#plot the bar graph of percentage job categories
data.job.value_counts(normalize=True).plot.barh()
plt.show()``````

The output looks like this,

By the above bar plot, we can infer that the data set contains more number of blue-collar workers compared to other categories.

Categorical Ordered Univariate Analysis:

Ordered variables are those variables that have a natural rank of order. Some examples of categorical ordered variables from our dataset are:

• Month: Jan, Feb, March……
• Education: Primary, Secondary,……

Now, let’s analyze the Education Variable from the dataset. Since we’ve already seen a bar plot, let’s see how a Pie Chart looks like.

``````#calculate the percentage of each education category.
data.education.value_counts(normalize=True)

#plot the pie chart of education categories
data.education.value_counts(normalize=True).plot.pie()
plt.show()``````

The output will be,

By the above analysis, we can infer that the data set has a large number of them belongs to secondary education after that tertiary and next primary. Also, a very small percentage of them have been unknown.

This is how we analyze univariate categorical analysis. If the column or variable is of numerical then we’ll analyze by calculating its mean, median, std, etc. We can get those values by using the describe function.

``data.salary.describe()``

The output will be,

## 4. Bivariate Analysis

If we analyze data by taking two variables/columns into consideration from a dataset, it is known as Bivariate Analysis.

a) Numeric-Numeric Analysis:

Analyzing the two numeric variables from a dataset is known as numeric-numeric analysis. We can analyze it in three different ways.

• Scatter Plot
• Pair Plot
• Correlation Matrix

Scatter Plot

Let’s take three columns ‘Balance’, ‘Age’ and ‘Salary’ from our dataset and see what we can infer by plotting to scatter plot between `salary` `balance` and `age` `balance`

``````#plot the scatter plot of balance and salary variable in data
plt.scatter(data.salary,data.balance)
plt.show()

#plot the scatter plot of balance and age variable in data
data.plot.scatter(x="age",y="balance")
plt.show()``````

Now, the scatter plots looks like,

Pair Plot

Now, let’s plot Pair Plots for the three columns we used in plotting Scatter plots. We’ll use the seaborn library for plotting Pair Plots.

``````#plot the pair plot of salary, balance and age in data dataframe.
sns.pairplot(data = data, vars=['salary','balance','age'])
plt.show()``````

The Pair Plot looks like this,

Correlation Matrix

Since we cannot use more than two variables as x-axis and y-axis in Scatter and Pair Plots, it is difficult to see the relation between three numerical variables in a single graph. In those cases, we’ll use the correlation matrix.

``````# Creating a matrix using age, salry, balance as rows and columns
data[['age','salary','balance']].corr()

#plot the correlation matrix of salary, balance and age in data dataframe.
sns.heatmap(data[['age','salary','balance']].corr(), annot=True, cmap = 'Reds')
plt.show()``````

First, we created a matrix using age, salary, and balance. After that, we are plotting the heatmap using the seaborn library of the matrix.

b) Numeric - Categorical Analysis

Analyzing the one numeric variable and one categorical variable from a dataset is known as numeric-categorical analysis. We analyze them mainly using mean, median, and box plots.

Let’s take `salary` and `response` columns from our dataset.

First check for mean value using `groupby`

``````#groupby the response to find the mean of the salary with response no & yes separately.
data.groupby('response')['salary'].mean()``````

The output will be,

There is not much of a difference between the yes and no response based on the salary.

Let’s calculate the median,

``````#groupby the response to find the median of the salary with response no & yes separately.
data.groupby('response')['salary'].median()``````

The output will be,

By both mean and median we can say that the response of yes and no remains the same irrespective of the person’s salary. But, is it truly behaving like that, let’s plot the box plot for them and check the behavior.

``````#plot the box plot of salary for yes & no responses.
sns.boxplot(data.response, data.salary)
plt.show()``````

The box plot looks like this,

As we can see, when we plot the Box Plot, it paints a very different picture compared to mean and median. The IQR for customers who gave a positive response is on the higher salary side.

This is how we analyze Numeric-Categorical variables, we use mean, median, and Box Plots to draw some sort of conclusions.

c) Categorical — Categorical Analysis

Since our target variable/column is the Response rate, we’ll see how the different categories like Education, Marital Status, etc., are associated with the Response column. So instead of ‘Yes’ and ‘No’ we will convert them into ‘1’ and ‘0’, by doing that we’ll get the “Response Rate”.

``````#create response_rate of numerical data type where response "yes"= 1, "no"= 0
data['response_rate'] = np.where(data.response=='yes',1,0)
data.response_rate.value_counts()``````

The output looks like this,

Let’s see how the response rate varies for different categories in marital status.

``````#plot the bar graph of marital status with average value of response_rate
data.groupby('marital')['response_rate'].mean().plot.bar()
plt.show()``````

The graph looks like this,

By the above graph, we can infer that the positive response is more for Single status members in the data set. Similarly, we can plot the graphs for Loan vs Response rate, Housing Loans vs Response rate, etc.

## 5. Multivariate Analysis

If we analyze data by taking more than two variables/columns into consideration from a dataset, it is known as Multivariate Analysis.

Let’s see how ‘Education’, ‘Marital’, and ‘Response_rate’ vary with each other.

First, we’ll create a pivot table with the three columns and after that, we’ll create a heatmap.

``````result = pd.pivot_table(data=data, index='education', columns='marital',values='response_rate')
print(result)

#create heat map of education vs marital vs response_rate
sns.heatmap(result, annot=True, cmap = 'RdYlGn', center=0.117)
plt.show()``````

The Pivot table and heatmap looks like this,

Based on the Heatmap we can infer that the married people with primary education are less likely to respond positively for the survey and single people with tertiary education are most likely to respond positively to the survey.

Similarly, we can plot the graphs for Job vs marital vs response, Education vs poutcome vs response, etc.

Conclusion

This is how we’ll do Exploratory Data Analysis. Exploratory Data Analysis (EDA) helps us to look beyond the data. The more we explore the data, the more the insights we draw from it. As a data analyst, almost 80% of our time will be spent understanding data and solving various business problems through EDA.

Thank you for reading and Happy Coding!!!

#dataanalysis #python 1561523460

## Matplotlib Cheat Sheet: Plotting in Python

This Matplotlib cheat sheet introduces you to the basics that you need to plot your data with Python and includes code samples.

Data visualization and storytelling with your data are essential skills that every data scientist needs to communicate insights gained from analyses effectively to any audience out there.

For most beginners, the first package that they use to get in touch with data visualization and storytelling is, naturally, Matplotlib: it is a Python 2D plotting library that enables users to make publication-quality figures. But, what might be even more convincing is the fact that other packages, such as Pandas, intend to build more plotting integration with Matplotlib as time goes on.

However, what might slow down beginners is the fact that this package is pretty extensive. There is so much that you can do with it and it might be hard to still keep a structure when you're learning how to work with Matplotlib.

DataCamp has created a Matplotlib cheat sheet for those who might already know how to use the package to their advantage to make beautiful plots in Python, but that still want to keep a one-page reference handy. Of course, for those who don't know how to work with Matplotlib, this might be the extra push be convinced and to finally get started with data visualization in Python.

You'll see that this cheat sheet presents you with the six basic steps that you can go through to make beautiful plots.

Check out the infographic by clicking on the button below: With this handy reference, you'll familiarize yourself in no time with the basics of Matplotlib: you'll learn how you can prepare your data, create a new plot, use some basic plotting routines to your advantage, add customizations to your plots, and save, show and close the plots that you make.

What might have looked difficult before will definitely be more clear once you start using this cheat sheet! Use it in combination with the Matplotlib Gallery, the documentation.

Matplotlib

Matplotlib is a Python 2D plotting library which produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms.

## Prepare the Data

### 1D Data

``````>>> import numpy as np
>>> x = np.linspace(0, 10, 100)
>>> y = np.cos(x)
>>> z = np.sin(x)``````

### 2D Data or Images

``````>>> data = 2 * np.random.random((10, 10))
>>> data2 = 3 * np.random.random((10, 10))
>>> Y, X = np.mgrid[-3:3:100j, -3:3:100j]
>>> U = 1 X** 2 + Y
>>> V = 1 + X Y**2
>>> from matplotlib.cbook import get_sample_data

## Create Plot

``>>> import matplotlib.pyplot as plt``

### Figure

``````>>> fig = plt.figure()
>>> fig2 = plt.figure(figsize=plt.figaspect(2.0))``````

### Axes

``````>>> fig.add_axes()
>>> fig3, axes = plt.subplots(nrows=2,ncols=2)
>>> fig4, axes2 = plt.subplots(ncols=3)``````

## Save Plot

``````>>> plt.savefig('foo.png') #Save figures
>>> plt.savefig('foo.png',  transparent=True) #Save transparent figures``````

## Show Plot

``>>> plt.show()``

## 1D Data

``````>>> fig, ax = plt.subplots()
>>> lines = ax.plot(x,y) #Draw points with lines or markers connecting them
>>> ax.scatter(x,y) #Draw unconnected points, scaled or colored
>>> axes[0,0].bar([1,2,3],[3,4,5]) #Plot vertical rectangles (constant width)
>>> axes[1,0].barh([0.5,1,2.5],[0,1,2]) #Plot horiontal rectangles (constant height)
>>> axes[1,1].axhline(0.45) #Draw a horizontal line across axes
>>> axes[0,1].axvline(0.65) #Draw a vertical line across axes
>>> ax.fill(x,y,color='blue') #Draw filled polygons
>>> ax.fill_between(x,y,color='yellow') #Fill between y values and 0``````

### 2D Data

``````>>> fig, ax = plt.subplots()
>>> im = ax.imshow(img, #Colormapped or RGB arrays
cmap= 'gist_earth',
interpolation= 'nearest',
vmin=-2,
vmax=2)
>>> axes2.pcolor(data2) #Pseudocolor plot of 2D array
>>> axes2.pcolormesh(data) #Pseudocolor plot of 2D array
>>> CS = plt.contour(Y,X,U) #Plot contours
>>> axes2.contourf(data1) #Plot filled contours
>>> axes2= ax.clabel(CS) #Label a contour plot``````

### Vector Fields

``````>>> axes[0,1].arrow(0,0,0.5,0.5) #Add an arrow to the axes
>>> axes[1,1].quiver(y,z) #Plot a 2D field of arrows
>>> axes[0,1].streamplot(X,Y,U,V) #Plot a 2D field of arrows``````

### Data Distributions

``````>>> ax1.hist(y) #Plot a histogram
>>> ax3.boxplot(y) #Make a box and whisker plot
>>> ax3.violinplot(z)  #Make a violin plot``````

## Plot Anatomy & Workflow

### Plot Anatomy

y-axis x-axis

### Workflow

The basic steps to creating plots with matplotlib are:

1 Prepare Data
2 Create Plot
3 Plot
4 Customized Plot
5 Save Plot
6 Show Plot

``````>>> import matplotlib.pyplot as plt
>>> x = [1,2,3,4]  #Step 1
>>> y = [10,20,25,30]
>>> fig = plt.figure() #Step 2
>>> ax = fig.add_subplot(111) #Step 3
>>> ax.plot(x, y, color= 'lightblue', linewidth=3)  #Step 3, 4
>>> ax.scatter([2,4,6],
[5,15,25],
color= 'darkgreen',
marker= '^' )
>>> ax.set_xlim(1, 6.5)
>>> plt.savefig('foo.png' ) #Step 5
>>> plt.show() #Step 6``````

## Close and Clear

``````>>> plt.cla()  #Clear an axis
>>> plt.clf(). #Clear the entire figure
>>> plt.close(). #Close a window``````

## Plotting Customize Plot

### Colors, Color Bars & Color Maps

``````>>> plt.plot(x, x, x, x**2, x, x** 3)
>>> ax.plot(x, y, alpha = 0.4)
>>> ax.plot(x, y, c= 'k')
>>> fig.colorbar(im, orientation= 'horizontal')
>>> im = ax.imshow(img,
cmap= 'seismic' )``````

### Markers

``````>>> fig, ax = plt.subplots()
>>> ax.scatter(x,y,marker= ".")
>>> ax.plot(x,y,marker= "o")``````

### Linestyles

``````>>> plt.plot(x,y,linewidth=4.0)
>>> plt.plot(x,y,ls= 'solid')
>>> plt.plot(x,y,ls= '--')
>>> plt.plot(x,y,'--' ,x**2,y**2,'-.' )
>>> plt.setp(lines,color= 'r',linewidth=4.0)``````

### Text & Annotations

``````>>> ax.text(1,
-2.1,
'Example Graph',
style= 'italic' )
>>> ax.annotate("Sine",
xy=(8, 0),
xycoords= 'data',
xytext=(10.5, 0),
textcoords= 'data',
arrowprops=dict(arrowstyle= "->",
connectionstyle="arc3"),)``````

### Mathtext

``>>> plt.title(r '\$sigma_i=15\$', fontsize=20)``

### Limits, Legends and Layouts

Limits & Autoscaling

``````>>> ax.margins(x=0.0,y=0.1) #Add padding to a plot
>>> ax.axis('equal')  #Set the aspect ratio of the plot to 1
>>> ax.set(xlim=[0,10.5],ylim=[-1.5,1.5])  #Set limits for x-and y-axis
>>> ax.set_xlim(0,10.5) #Set limits for x-axis``````

Legends

``````>>> ax.set(title= 'An Example Axes',  #Set a title and x-and y-axis labels
ylabel= 'Y-Axis',
xlabel= 'X-Axis')
>>> ax.legend(loc= 'best')  #No overlapping plot elements``````

Ticks

``````>>> ax.xaxis.set(ticks=range(1,5),  #Manually set x-ticks
ticklabels=[3,100, 12,"foo" ])
>>> ax.tick_params(axis= 'y', #Make y-ticks longer and go in and out
direction= 'inout',
length=10)``````

Subplot Spacing

``````>>> fig3.subplots_adjust(wspace=0.5,   #Adjust the spacing between subplots
hspace=0.3,
left=0.125,
right=0.9,
top=0.9,
bottom=0.1)
>>> fig.tight_layout() #Fit subplot(s) in to the figure area``````

Axis Spines

``````>>> ax1.spines[ 'top'].set_visible(False) #Make the top axis line for a plot invisible
>>> ax1.spines['bottom' ].set_position(( 'outward',10))  #Move the bottom axis line outward``````

Have this Cheat Sheet at your fingertips

Original article source at https://www.datacamp.com

#matplotlib #cheatsheet #python 1647064260

## dotnet script

Run C# scripts from the .NET CLI, define NuGet packages inline and edit/debug them in VS Code - all of that with full language services support from OmniSharp.

## Installing

### Prerequisites

The only thing we need to install is .NET Core 3.1 or .NET 5.0 SDK.

### .NET Core Global Tool

.NET Core 2.1 introduced the concept of global tools meaning that you can install `dotnet-script` using nothing but the .NET CLI.

``````dotnet tool install -g dotnet-script

You can invoke the tool using the following command: dotnet-script
Tool 'dotnet-script' (version '0.22.0') was successfully installed.
``````

The advantage of this approach is that you can use the same command for installation across all platforms. .NET Core SDK also supports viewing a list of installed tools and their uninstallation.

``````dotnet tool list -g

Package Id         Version      Commands
---------------------------------------------
dotnet-script      0.22.0       dotnet-script
``````
``````dotnet tool uninstall dotnet-script -g

Tool 'dotnet-script' (version '0.22.0') was successfully uninstalled.
``````

### Windows

``````choco install dotnet.script
``````

We also provide a PowerShell script for installation.

``````(new-object Net.WebClient).DownloadString("https://raw.githubusercontent.com/filipw/dotnet-script/master/install/install.ps1") | iex
``````

### Linux and Mac

``````curl -s https://raw.githubusercontent.com/filipw/dotnet-script/master/install/install.sh | bash
``````

If permission is denied we can try with `sudo`

``````curl -s https://raw.githubusercontent.com/filipw/dotnet-script/master/install/install.sh | sudo bash
``````

### Docker

A Dockerfile for running dotnet-script in a Linux container is available. Build:

``````cd build
docker build -t dotnet-script -f Dockerfile ..
``````

And run:

``````docker run -it dotnet-script --version
``````

### Github

You can manually download all the releases in `zip` format from the GitHub releases page.

## Usage

Our typical `helloworld.csx` might look like this:

``````Console.WriteLine("Hello world!");
``````

That is all it takes and we can execute the script. Args are accessible via the global Args array.

``````dotnet script helloworld.csx
``````

### Scaffolding

Simply create a folder somewhere on your system and issue the following command.

``````dotnet script init
``````

This will create `main.csx` along with the launch configuration needed to debug the script in VS Code.

``````.
├── .vscode
│   └── launch.json
├── main.csx
└── omnisharp.json
``````

We can also initialize a folder using a custom filename.

``````dotnet script init custom.csx
``````

Instead of `main.csx` which is the default, we now have a file named `custom.csx`.

``````.
├── .vscode
│   └── launch.json
├── custom.csx
└── omnisharp.json
``````

Note: Executing `dotnet script init` inside a folder that already contains one or more script files will not create the `main.csx` file.

### Running scripts

Scripts can be executed directly from the shell as if they were executables.

``````foo.csx arg1 arg2 arg3
``````

OSX/Linux

Just like all scripts, on OSX/Linux you need to have a `#!` and mark the file as executable via chmod +x foo.csx. If you use dotnet script init to create your csx it will automatically have the `#!` directive and be marked as executable.

The OSX/Linux shebang directive should be #!/usr/bin/env dotnet-script

``````#!/usr/bin/env dotnet-script
Console.WriteLine("Hello world");
``````

You can execute your script using dotnet script or dotnet-script, which allows you to pass arguments to control your script execution more.

``````foo.csx arg1 arg2 arg3
dotnet script foo.csx -- arg1 arg2 arg3
dotnet-script foo.csx -- arg1 arg2 arg3
``````

#### Passing arguments to scripts

All arguments after `--` are passed to the script in the following way:

``````dotnet script foo.csx -- arg1 arg2 arg3
``````

Then you can access the arguments in the script context using the global `Args` collection:

``````foreach (var arg in Args)
{
Console.WriteLine(arg);
}
``````

All arguments before `--` are processed by `dotnet script`. For example, the following command-line

``````dotnet script -d foo.csx -- -d
``````

will pass the `-d` before `--` to `dotnet script` and enable the debug mode whereas the `-d` after `--` is passed to script for its own interpretation of the argument.

### NuGet Packages

`dotnet script` has built-in support for referencing NuGet packages directly from within the script.

``````#r "nuget: AutoMapper, 6.1.0"
`````` Note: Omnisharp needs to be restarted after adding a new package reference

#### Package Sources

We can define package sources using a `NuGet.Config` file in the script root folder. In addition to being used during execution of the script, it will also be used by `OmniSharp` that provides language services for packages resolved from these package sources.

As an alternative to maintaining a local `NuGet.Config` file we can define these package sources globally either at the user level or at the computer level as described in Configuring NuGet Behaviour

It is also possible to specify packages sources when executing the script.

``````dotnet script foo.csx -s https://SomePackageSource
``````

Multiple packages sources can be specified like this:

``````dotnet script foo.csx -s https://SomePackageSource -s https://AnotherPackageSource
``````

### Creating DLLs or Exes from a CSX file

Dotnet-Script can create a standalone executable or DLL for your script.

The executable you can run directly independent of dotnet install, while the DLL can be run using the dotnet CLI like this:

``````dotnet script exec {path_to_dll} -- arg1 arg2
``````

### Caching

We provide two types of caching, the `dependency cache` and the `execution cache` which is explained in detail below. In order for any of these caches to be enabled, it is required that all NuGet package references are specified using an exact version number. The reason for this constraint is that we need to make sure that we don't execute a script with a stale dependency graph.

#### Dependency Cache

In order to resolve the dependencies for a script, a `dotnet restore` is executed under the hood to produce a `project.assets.json` file from which we can figure out all the dependencies we need to add to the compilation. This is an out-of-process operation and represents a significant overhead to the script execution. So this cache works by looking at all the dependencies specified in the script(s) either in the form of NuGet package references or assembly file references. If these dependencies matches the dependencies from the last script execution, we skip the restore and read the dependencies from the already generated `project.assets.json` file. If any of the dependencies has changed, we must restore again to obtain the new dependency graph.

#### Execution cache

In order to execute a script it needs to be compiled first and since that is a CPU and time consuming operation, we make sure that we only compile when the source code has changed. This works by creating a SHA256 hash from all the script files involved in the execution. This hash is written to a temporary location along with the DLL that represents the result of the script compilation. When a script is executed the hash is computed and compared with the hash from the previous compilation. If they match there is no need to recompile and we run from the already compiled DLL. If the hashes don't match, the cache is invalidated and we recompile.

You can override this automatic caching by passing --no-cache flag, which will bypass both caches and cause dependency resolution and script compilation to happen every time we execute the script.

#### Cache Location

The temporary location used for caches is a sub-directory named `dotnet-script` under (in order of priority):

1. The path specified for the value of the environment variable named `DOTNET_SCRIPT_CACHE_LOCATION`, if defined and value is not empty.
2. Linux distributions only: `\$XDG_CACHE_HOME` if defined otherwise `\$HOME/.cache`
3. macOS only: `~/Library/Caches`
4. The value returned by `Path.GetTempPath` for the platform.

### Debugging

The days of debugging scripts using `Console.WriteLine` are over. One major feature of `dotnet script` is the ability to debug scripts directly in VS Code. Just set a breakpoint anywhere in your script file(s) and hit F5(start debugging) ### Script Packages

Script packages are a way of organizing reusable scripts into NuGet packages that can be consumed by other scripts. This means that we now can leverage scripting infrastructure without the need for any kind of bootstrapping.

#### Creating a script package

A script package is just a regular NuGet package that contains script files inside the `content` or `contentFiles` folder.

The following example shows how the scripts are laid out inside the NuGet package according to the standard convention .

``````└── contentFiles
└── csx
└── netstandard2.0
└── main.csx
``````

This example contains just the `main.csx` file in the root folder, but packages may have multiple script files either in the root folder or in subfolders below the root folder.

When loading a script package we will look for an entry point script to be loaded. This entry point script is identified by one of the following.

• A script called `main.csx` in the root folder
• A single script file in the root folder

If the entry point script cannot be determined, we will simply load all the scripts files in the package.

The advantage with using an entry point script is that we can control loading other scripts from the package.

#### Consuming a script package

To consume a script package all we need to do specify the NuGet package in the `#load`directive.

The following example loads the simple-targets package that contains script files to be included in our script.

``````#load "nuget:simple-targets-csx, 6.0.0"

using static SimpleTargets;
var targets = new TargetDictionary();

Run(Args, targets);
``````

Note: Debugging also works for script packages so that we can easily step into the scripts that are brought in using the `#load` directive.

### Remote Scripts

Scripts don't actually have to exist locally on the machine. We can also execute scripts that are made available on an `http(s)` endpoint.

This means that we can create a Gist on Github and execute it just by providing the URL to the Gist.

This Gist contains a script that prints out "Hello World"

We can execute the script like this

``````dotnet script https://gist.githubusercontent.com/seesharper/5d6859509ea8364a1fdf66bbf5b7923d/raw/0a32bac2c3ea807f9379a38e251d93e39c8131cb/HelloWorld.csx
``````

That is a pretty long URL, so why don't make it a TinyURL like this:

``````dotnet script https://tinyurl.com/y8cda9zt
``````

### Script Location

A pretty common scenario is that we have logic that is relative to the script path. We don't want to require the user to be in a certain directory for these paths to resolve correctly so here is how to provide the script path and the script folder regardless of the current working directory.

``````public static string GetScriptPath([CallerFilePath] string path = null) => path;
public static string GetScriptFolder([CallerFilePath] string path = null) => Path.GetDirectoryName(path);
``````

Tip: Put these methods as top level methods in a separate script file and `#load` that file wherever access to the script path and/or folder is needed.

## REPL

This release contains a C# REPL (Read-Evaluate-Print-Loop). The REPL mode ("interactive mode") is started by executing `dotnet-script` without any arguments.

The interactive mode allows you to supply individual C# code blocks and have them executed as soon as you press Enter. The REPL is configured with the same default set of assembly references and using statements as regular CSX script execution.

### Basic usage

Once `dotnet-script` starts you will see a prompt for input. You can start typing C# code there.

``````~\$ dotnet script
> var x = 1;
> x+x
2
``````

If you submit an unterminated expression into the REPL (no `;` at the end), it will be evaluated and the result will be serialized using a formatter and printed in the output. This is a bit more interesting than just calling `ToString()` on the object, because it attempts to capture the actual structure of the object. For example:

``````~\$ dotnet script
> var x = new List<string>();
> x
List<string>(1) { "foo" }
> x
List<string>(2) { "foo", "bar" }
>
``````

### Inline Nuget packages

REPL also supports inline Nuget packages - meaning the Nuget packages can be installed into the REPL from within the REPL. This is done via our `#r` and `#load` from Nuget support and uses identical syntax.

``````~\$ dotnet script
> #r "nuget: Automapper, 6.1.1"
> using AutoMapper;
> typeof(MapperConfiguration)
[AutoMapper.MapperConfiguration]
> using static SimpleTargets;
> typeof(TargetDictionary)
[Submission#0+SimpleTargets+TargetDictionary]
``````

### Multiline mode

Using Roslyn syntax parsing, we also support multiline REPL mode. This means that if you have an uncompleted code block and press Enter, we will automatically enter the multiline mode. The mode is indicated by the `*` character. This is particularly useful for declaring classes and other more complex constructs.

``````~\$ dotnet script
> class Foo {
* public string Bar {get; set;}
* }
> var foo = new Foo();
``````

### REPL commands

Aside from the regular C# script code, you can invoke the following commands (directives) from within the REPL:

### Seeding REPL with a script

You can execute a CSX script and, at the end of it, drop yourself into the context of the REPL. This way, the REPL becomes "seeded" with your code - all the classes, methods or variables are available in the REPL context. This is achieved by running a script with an `-i` flag.

For example, given the following CSX script:

``````var msg = "Hello World";
Console.WriteLine(msg);
``````

When you run this with the `-i` flag, `Hello World` is printed, REPL starts and `msg` variable is available in the REPL context.

``````~\$ dotnet script foo.csx -i
Hello World
>
``````

You can also seed the REPL from inside the REPL - at any point - by invoking a `#load` directive pointed at a specific file. For example:

``````~\$ dotnet script
Hello World
>
``````

## Piping

The following example shows how we can pipe data in and out of a script.

The `UpperCase.csx` script simply converts the standard input to upper case and writes it back out to standard output.

``````using (var streamReader = new StreamReader(Console.OpenStandardInput()))
{
}
``````

We can now simply pipe the output from one command into our script like this.

``````echo "This is some text" | dotnet script UpperCase.csx
THIS IS SOME TEXT
``````

### Debugging

The first thing we need to do add the following to the `launch.config` file that allows VS Code to debug a running process.

``````{
"name": ".NET Core Attach",
"type": "coreclr",
"request": "attach",
"processId": "\${command:pickProcess}"
}
``````

To debug this script we need a way to attach the debugger in VS Code and the simplest thing we can do here is to wait for the debugger to attach by adding this method somewhere.

``````public static void WaitForDebugger()
{
Console.WriteLine("Attach Debugger (VS Code)");
while(!Debugger.IsAttached)
{
}
}
``````

To debug the script when executing it from the command line we can do something like

``````WaitForDebugger();
{
}
``````

Now when we run the script from the command line we will get

``````\$ echo "This is some text" | dotnet script UpperCase.csx
Attach Debugger (VS Code)
``````

This now gives us a chance to attach the debugger before stepping into the script and from VS Code, select the `.NET Core Attach` debugger and pick the process that represents the executing script.

Once that is done we should see our breakpoint being hit.

## Configuration(Debug/Release)

By default, scripts will be compiled using the `debug` configuration. This is to ensure that we can debug a script in VS Code as well as attaching a debugger for long running scripts.

There are however situations where we might need to execute a script that is compiled with the `release` configuration. For instance, running benchmarks using BenchmarkDotNet is not possible unless the script is compiled with the `release` configuration.

We can specify this when executing the script.

``````dotnet script foo.csx -c release
``````

## Nullable reference types

Starting from version 0.50.0, `dotnet-script` supports .Net Core 3.0 and all the C# 8 features. The way we deal with nullable references types in `dotnet-script` is that we turn every warning related to nullable reference types into compiler errors. This means every warning between `CS8600` and `CS8655` are treated as an error when compiling the script.

Nullable references types are turned off by default and the way we enable it is using the `#nullable enable` compiler directive. This means that existing scripts will continue to work, but we can now opt-in on this new feature.

``````#!/usr/bin/env dotnet-script

#nullable enable

string name = null;
``````

Trying to execute the script will result in the following error

``````main.csx(5,15): error CS8625: Cannot convert null literal to non-nullable reference type.
``````

We will also see this when working with scripts in VS Code under the problems panel. Author: filipw
Source Code: https://github.com/filipw/dotnet-script 1604374500

## Python: Real-time Automated Long Short Term Memory (LSTM) Short-term Load Forecasting & Plotting

Python: Real-time Automated Long Short Term Memory (LSTM) Short-term Load Forecasting & Plotting

TABLE OF CONTENT

Introduction 00:00:00

• Introduction of LSTM 00:00:52
• Introduction of RNN 00:13:03

From RNN to LSTM 00:22:56

How to build a LSTM 00:31:41

Programming Exercise 00:42:59

• Details of short-term load forecasting problem 00:43:02

Python

• Data Preparation 00:44:00
• Developing LSTM 01:03:57
• Real-time Model Prediction 01:18:19
• Real-time Plotting 1:28:10

#python #machine-learning #data-science #artificial-intelligence #developer 1619510796

## Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map