This is the basic introduction of the pandas library, you can use it for teaching this library for machine learning introduction. This slide will be able to help to understand the basics of pandas to the students with no coding background.
Abstract: This PDSG workshop introduces the basics of Python libraries used in machine learning. Libraries covered are Numpy, Pandas and MathlibPlot.
Level: Fundamental
Requirements: One should have some knowledge of programming and some statistics.
Abstract: This PDSG workshop introduces the basics of Python libraries used in machine learning. Libraries covered are Numpy, Pandas and MathlibPlot.
Level: Fundamental
Requirements: One should have some knowledge of programming and some statistics.
This slide is used to do an introduction for the matplotlib library and this will be a very basic introduction. As matplotlib is a very used and famous library for machine learning this will be very helpful to teach a student with no coding background and they can start the plotting of maps from the ending of the slide by there own.
Introduction to Pandas and Time Series Analysis [PyCon DE]Alexander Hendorf
Most data is allocated to a period or to some point in time. We can gain a lot of insight by analyzing what happened when. The better the quality and accuracy of our data, the better our predictions can become.
Unfortunately the data we have to deal with is often aggregated for example on a monthly basis, but not all months are the same, they may have 28 days, 31 days, have four or five weekends,…. It’s made fit to our calendar that was made fit to deal with the earth surrounding the sun, not to please Data Scientists.
Dealing with periodical data can be a challenge. This talk will show to how you can deal with it with Pandas.
A class is a code template for creating objects. Objects have member variables and have behaviour associated with them. In python a class is created by the keyword class.
An object is created using the constructor of the class. This object will then be called the instance of the class.
( Python Training: https://www.edureka.co/python )
This Edureka Python Numpy tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) explains what exactly is Numpy and how it is better than Lists. It also explains various Numpy operations with examples.
Check out our Python Training Playlist: https://goo.gl/Na1p9G
This tutorial helps you to learn the following topics:
1. What is Numpy?
2. Numpy v/s Lists
3. Numpy Operations
4. Numpy Special Functions
This slide is used to do an introduction for the matplotlib library and this will be a very basic introduction. As matplotlib is a very used and famous library for machine learning this will be very helpful to teach a student with no coding background and they can start the plotting of maps from the ending of the slide by there own.
Introduction to Pandas and Time Series Analysis [PyCon DE]Alexander Hendorf
Most data is allocated to a period or to some point in time. We can gain a lot of insight by analyzing what happened when. The better the quality and accuracy of our data, the better our predictions can become.
Unfortunately the data we have to deal with is often aggregated for example on a monthly basis, but not all months are the same, they may have 28 days, 31 days, have four or five weekends,…. It’s made fit to our calendar that was made fit to deal with the earth surrounding the sun, not to please Data Scientists.
Dealing with periodical data can be a challenge. This talk will show to how you can deal with it with Pandas.
A class is a code template for creating objects. Objects have member variables and have behaviour associated with them. In python a class is created by the keyword class.
An object is created using the constructor of the class. This object will then be called the instance of the class.
( Python Training: https://www.edureka.co/python )
This Edureka Python Numpy tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) explains what exactly is Numpy and how it is better than Lists. It also explains various Numpy operations with examples.
Check out our Python Training Playlist: https://goo.gl/Na1p9G
This tutorial helps you to learn the following topics:
1. What is Numpy?
2. Numpy v/s Lists
3. Numpy Operations
4. Numpy Special Functions
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Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
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2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
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for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
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Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
1.4 modern child centered education - mahatma gandhi-2.pptx
Introduction to pandas
1. INTRODUCTION TO PANDAS
A LIBRARY THAT IS USED FOR DATA MANIPULATION AND ANALYSIS TOOL
USING POWERFUL DATA STRUCTURES
2. TYPES OF DATA STRUCTUE IN PANDAS
Data Structure Dimensions Description
Series 1 1D labeled homogeneous
array, sizeimmutable.
Data Frames 2 General 2D labeled, size-
mutable tabular structure
with potentially
heterogeneously typed
columns.
Panel 3 General 3D labeled, size-
mutable array.
3. SERIES
• Series is a one-dimensional array like structure with homogeneous
data. For example, the following series is a collection of integers 10,
23, 56,
• … 10 23 56 17 52 61 73 90 26 72
4. DataFrame
• DataFrame is a two-dimensional array with heterogeneous data. For
example,
Name Age Gender Rating
Steve 32 Male 3.45
Lia 28 Female 4.6
Vin 45 Male 3.9
Katie 38 Female 2.78
5. Data Type of Columns
Column Type
Name String
Age Integer
Gender String
Rating Float
6. PANEL
• Panel is a three-dimensional data structure with heterogeneous data.
It is hard to represent the panel in graphical representation. But a
panel can be illustrated as a container of DataFrame.
7. DataFrame
• A Data frame is a two-dimensional data structure, i.e., data is aligned
in a tabular fashion in rows and columns.
• Features of DataFrame
• Potentially columns are of different types
• Size – Mutable
• Labeled axes (rows and columns)
• Can Perform Arithmetic operations on rows and columns
10. • data
• data takes various forms like ndarray, series, map, lists, dict, constants and also
another DataFrame.
• index
• For the row labels, the Index to be used for the resulting frame is Optional Default
np.arrange(n) if no index is passed.
• columns
• For column labels, the optional default syntax is - np.arrange(n). This is only true if
no index is passed.
• dtype
• Data type of each column.
• copy
• This command (or whatever it is) is used for copying of data, if the default is False.
11. • Create DataFrame
• A pandas DataFrame can be created using various inputs like −
• Lists
• dict
• Series
• Numpy ndarrays
• Another DataFrame
12. Example
• Example
• import pandas as pd
• Data = [_______]
• Df = pd.DataFrame(data)
• Print df
Example 2
Import pandas as pd
Data = {‘Name’ : [‘__’. ‘__’],’Age’: [___]}
Df = pd.DataFrame(data)
print df
14. • import pandas as pd
• data = [{'a': 1, 'b': 2},{'a': 5, 'b': 10, 'c': 20}]
• #With two column indices, values same as dictionary keys
• df1 = pd.DataFrame(data, index=['first', 'second'], columns=['a', 'b'])
• #With two column indices with one index with other name
• df2 = pd.DataFrame(data, index=['first', 'second'], columns=['a', 'b1'])
• print df1print df2
15. The following example shows how to create a DataFrame with
a list of dictionaries, row indices, and column indices.
• import pandas as pd
• data = [{'a': 1, 'b': 2},{'a': 5, 'b': 10, 'c': 20}]
• #With two column indices, values same as dictionary keys
• df1 = pd.DataFrame(data, index=['first', 'second'], columns=['a', 'b'])
• #With two column indices with one index with other name
• df2 = pd.DataFrame(data, index=['first', 'second'], columns=['a', 'b1'])
• print df1
• print df2
16. Create a DataFrame from Dict of Series
• import pandas as pd
• d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']), 'two' : pd.Series([1,
2, 3, 4], index=['a', 'b', 'c', 'd'])}
• df = pd.DataFrame(d)
• print df
17. Column Addition
• import pandas as pd
• d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']), 'two' : pd.Series([1, 2,
3, 4], index=['a', 'b', 'c', 'd'])}
• df = pd.DataFrame(d)
• # Adding a new column to an existing DataFrame object with column label
by passing new series
• print ("Adding a new column by passing as Series:")
• df['three']=pd.Series([10,20,30],index=['a','b','c'])
• print dfprint ("Adding a new column using the existing columns in
DataFrame:")
• df['four']=df['one']+df['three']
• print df
18. Column Deletion
• # Using the previous DataFrame, we will delete a
column
• # using del function
• import pandas as pd
• d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']),
'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd']),
'three' : pd.Series([10,20,30], index=['a','b','c'])}
• df = pd.DataFrame(d)
19. • print ("Our dataframe is:")
• print df
• # using del function
• print ("Deleting the first column using DEL function:")
• del df['one']
• print df
# using pop function
• print ("Deleting another column using POP function:")
• df.pop('two')
• print df
24. Handling categorical data
• There are many data that are repetitive for example gender , country , and codes are
always repetitive .
• Categorical variables can take on only a limited
• The categorical data type is useful in the following cases −
• A string variable consisting of only a few different values. Converting such a string
variable to a categorical variable will save some memory.
• The lexical order of a variable is not the same as the logical order (“one”, “two”,
“three”). By converting to a categorical and specifying an order on the categories,
sorting and min/max will use the logical order instead of the lexical order.
• As a signal to other python libraries that this column should be treated as a
categorical variable (e.g. to use suitable statistical methods or plot types).