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Data Analysis & Processing with Pandas

Deal with Missing Value

Explore methods to manage missing values in pandas Series and DataFrames. Learn to detect NaNs with isnull, fill gaps using fillna with static or forward/backward fill methods, and remove missing data with dropna. This lesson equips you to handle incomplete datasets effectively for data analysis.

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The missing values problem is very common in the real world. For example, suppose you are trying to collect information from a company. There is a field for a company address. Many people want to keep their privacy and leave this field empty. If the data is loaded by pandas, those empty fields are listed as missing values. NaN is the default missing value in pandas.

Operations on NaN data

  • When mean/sum/std/median are performed on a Series which contains missing values, these values would be treated as zero.
  • When add/div/sub are performed, the result is NaN.
  • If all
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