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Pandas for Everyone: Python Data Analysis Labs

(PYTHON-PANDAS.AP1.0L0)
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Skills You’ll Get

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1

Pandas DataFrame Basics

  • Performing Grouped and Aggregated Calculations Using the .groupby() Method
2

Pandas Data Structures Basics

  • Creating a DataFrame and Making Changes to it
3

Plotting Basics

  • Creating a Scatter Plot Using Multivariate Data
  • Creating a Density Plot Using Bivariate Data
4

Tidy Data

  • Using Functions and Methods to Process and Tidy Data
5

Apply Functions

  • Performing Calculations Across DataFrames
  • Vectorizing Functions
6

Data Assembly

  • Performing Concatenation Using the concat() Function
  • Merging Multiple Data Sets Using the .merge() Function
7

Data Normalization

  • Understanding Multiple Observational Units in a Data Set
8

Groupby Operations: Split-Apply-Combine

  • Performing Data Summarization Using Group-by Operations
  • Performing Boolean Subsetting on the Data
  • Performing Operations on Grouped Objects
9

Missing Data

  • Finding and Cleaning Missing Data
10

Data Types

  • Performing Data Type Conversion
11

Strings and Text Data

  • Finding and Substituting a Pattern
12

Dates and Times

  • Converting an Object Type into a datetime Type
  • Extracting Date Components from the Data
  • Getting Stock Data and Subsetting it Based on Dates
  • Resampling Dates Using the .resample() Method
13

Linear Regression (Continuous Outcome Variable)

  • Performing Linear Regression
  • Performing Multiple Regression
14

Generalized Linear Models

  • Performing Logistic Regression
  • Performing Poisson Regression Using the poisson() Function
15

Survival Analysis

  • Performing Survival Analysis Using the KaplanMeierFitter() Function
16

Model Diagnostics

  • Comparing Models Using Cross-Validation
17

Regularization

  • Performing L1 Regularization Using the Lasso() Function
  • Performing L2 Regularization Using the Ridge() Function
18

Clustering

  • Performing k-Means Clustering
  • Using Hierarchical Clustering Algorithms

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