pandas 常用清洗資料（一）

標籤：long   like   class   res   release   ros   first   facebook   mil   

資料來源擷取：

https://www.kaggle.com/datasets

 

1、

Look at the some basic stats for the ‘imdb_score’ column: data.imdb_score.describe()Select a column: data[‘movie_title’]Select the first 10 rows of a column: data[‘duration’][:10]Select multiple columns: data[[‘budget’,’gross’]]Select all movies over two hours long: data[data[‘duration’] > 120]

 

data.country = data.country.fillna(‘’)data.duration = data.duration.fillna(data.duration.mean())data = pd.read_csv(‘movie_metadata.csv’, dtype={title_year: str})data[‘movie_title’].str.upper()Similarly, to get rid of trailing whitespace:data[‘movie_title’].str.strip()data = data.rename(columns = {‘title_year’:’release_date’, ‘movie_facebook_likes’:’facebook_likes’})

 

丟棄帶有NAN的所有項data.dropna()丟棄所有元素都是NAN的行data.dropna(how=‘all‘)丟棄所有元素都是NAN的列data.dropna(axis=1,how=‘all‘)  #axis = 0 行，=1 列只保留至少有3個非NAN值的行data.dropna(thresh=3)

 

pandas 常用清洗資料（一）