🧹 Drop Missing Values in Pandas (dropna explained in 60s)
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🧹 Drop Missing Values in Pandas (dropna explained in 60s)
2 vizualizăriAcum 3 săptămâni
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Prashant Kumar on Instagram: "In this video, I break down some of the most important Pandas methods every data scientist and machine-learning beginner should know. These functions help you quickly explore your dataset, clean missing values, engineer better features, and aggregate results — all inside Python. You’ll see how methods like df.head(), df.info(), and df.describe() help you understand your data fast. Then we look at essential cleaning tools such as dropna(), fillna(), and drop_duplicat
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Prashant Kumar on Instagram: "In this video, I break down some of t…
7,6mii vizualizăriAcum 1 lună
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Shubhanshi📈📊 on Instagram: "If you work with data, you already know the truth: 👉 Messy data kills insights. 👉 Clean data creates impact. Here are the most-used Python (Pandas) commands for data cleaning that every data analyst / data engineer / data scientist should have at their fingertips 👇 🔍 Data Inspection df.head() df.info() df.describe() 🧩 Missing Data Handling df.isnull().sum() df.dropna() df.fillna() 🧹 Cleaning & Transformation df.drop_duplicates() df.rename() df.astype() df.repl
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df.dropna(axis=1,how='all') 拷贝到研究环境中
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df.dropna() 有5个参数
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df.dropna(axis=0,subset=['high','cl…
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去掉缺失值——df.dropna 对应的行或者列被去除
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df.dropna(axis=0,subset=['high','cl…
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df.dropna() 直接删除避免出现严重错误
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df.dropna(axis=1,thresh=2) 代码正常运行
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