15.6: Chapter Summary
- Page ID
- 117625
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)Highlights from this chapter include:
- Data science is a multidisciplinary field that combines collection, processing, and analysis of large volumes of data to extract insights and drive informed decision-making.
- The data science life cycle is the framework followed by data scientists to complete a data science project.
- The data science life cycle includes 1) data acquisition, 2) data exploration, 3) data analysis, and 4) reporting.
- Google Colaboratory is a cloud-based Jupyter Notebook environment that allows programmers to write, run, and share Python code online.
- NumPy (Numerical Python) is a Python library that provides support for efficient numerical operations on large, multi-dimensional arrays and serves as a fundamental building block for data analysis in Python.
- NumPy implements an
ndarray
object that allows the creation of multi-dimensional arrays of homogeneous data types and efficient data processing. - NumPy provides functionalities for mathematical operations, array manipulation, and linear algebra operations.
- Pandas is an open-source Python library used for data cleaning, processing, and analysis.
- Pandas provides Series and DataFrame data structures, data processing functionality, and integration with other libraries.
- Exploratory Data Analysis (EDA) is the task of analyzing data to gain insights, identify patterns, and understand the underlying structure of the data.
- A feature is an individual variable or attribute that is calculated from raw data in a dataset.
- Data indexing can be used to select and access specific rows and columns.
- Data slicing refers to selecting a subset of rows and/or columns from a DataFrame.
- Data filtering involves selecting rows or columns based on certain conditions.
- Missing values in a dataset can occur when data are not available or were not recorded properly.
- Data visualization has a crucial role in data science for understanding the data.
- Different types of visualizations include bar plot, line plot, scatter plot, histogram plot, and box plot.
- Several Python data visualization libraries exist that offer a range of capabilities and features to create different plot types. These libraries include Matplotlib, Seaborn, and Plotly.
- The conventional aliases for importing NumPy, Pandas, and Matplotlib.pyplot are
np
,pd
, andplt
, respectively.
At this point, you should be able to write programs to create data structures to store different datasets and explore and visualize datasets.
Function | Description |
---|---|
|
Creates an |
|
Creates an array of zeros. |
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Creates an array of ones. |
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Creates an array of random numbers with |
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Creates an array from a CSV file. |
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Creates a DataFrame from a list, dictionary, or an array. |
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Creates a DataFrame from a CSV file. |
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Returns the first few rows of a DataFrame. |
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Returns the last few rows of a DataFrame. |
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Provides a summary of the DataFrame, including the column names, data types, and the number of non-
Nullvalues. |
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Generates the column count, mean, standard deviation, minimum, maximum, and quartiles. |
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Counts the occurrences of unique values in a column and presents them in descending order. |
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Returns an array of unique values in a column. |
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Allows for accessing data in a DataFrame using row/column labels. |
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Allows for accessing data in a DataFrame using row/column integer-based indexes. |
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Selects only the rows that meet the given |
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Slices using label ranges. |
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Slices rows that are in the list |
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Returns a Boolean array with Boolean values representing whether each entry has been |
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Replaces |
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Removes all rows containing a |
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Takes in two inputs, |
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Takes in two inputs, |
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Takes in two inputs, |
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Takes in one input, |
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Takes in one input, |