📌Inner join One to many relationships 📌 Merging multiple DataFrames Great job putting your new skills into action. You merged multiple tables with varying relationships and added suffixes to make your column names clearer. Using your skills, you were able t Left join

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..

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