Creating and Visualizing DataFrames : Learn to visualize the contents of your DataFrames, handle missing data values, and import data from and export data to CSV files. 1. Visualizing your Data 2. Missing Values 3. Creating DataFrames 4. Reading and writing CSVs 📌Histograms 📌groupby breed, select the weight col, take the mean Missing Values 📌 Reading and writing CSVs : how to pull data from csv ..

Slicing lets you select consecutive elements of an object using first:last syntax. DataFrames can be sliced by index values, or by row/column number; we'll start with the first case. This involves slicing inside the .loc[] method. 📌 Data Frame은 Slice 전에 Sort the index! 📌 Outer index level .loc["" : ""] 📌 inner index levels : 튜플로 Slice하기 📌Columns 슬라이싱 📌 행과 열을 모두 slice하기 📌 Practice : Subsetting ro..

📌Explicit indexes 📌 Review : Subsetting with .loc[] 출처 : www.datacamp.com/

📌 drop_duplicate method Removing duplicates is an essential skill to get accurate counts, because often you don't want to count the same thing multiple times. //중복 데이터 처리를 위한 메서드 📌Counting categorical variables Counting is a great way to get an overview of your data and to spot curiosities that you might not notice otherwise. .value_counts() .value_counts(normalize=True) // To calculate proporti..

📌data["newly_adding_col"]=calculation 📌Summarizing numerical data 📌Cumulative Sum cumsum() Efficient Summaries

.head() load first few rows .info() displays names of coulmns, data types, whether there are missing values Let's practice Sorting and subsetting zooming in specific columns subsetting rows .isin()

📌Distribution Practice #1

📌 Overview 📌 if more customized behavior is necessary, use elif 📌 Filtering pandas DataFrames #1. Get column #2. #3. 📌 Boolean operators Practice Cars per capita(1) cars per capita (2) 📌 while loop : execute statement repeatedly 📌 for loop practice dictionary needs METHOD, numpy array needs Function 📌Loop data structures part2 📌Random Walk 임의의 방향으로 움직이는 연속적인 값