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These one-liners provide quick and effective ways to assess the quality and consistency of the data within a Pandas DataFrame.
Code Snippet | Explanation |
---|---|
df.isnull().sum() |
Counts the number of missing values per column. |
df.duplicated().sum() |
Counts the number of duplicate rows in the DataFrame. |
df.describe() |
Provides basic descriptive statistics of numerical columns. |
df.info() |
Displays a concise summary of the DataFrame including data types and presence of null values. |
df.nunique() |
Counts the number of unique values per column. |
df.apply(lambda x: x.nunique() / x.count() * 100) |
Computes the percentage of unique values for each column. |
df.isin( value » ).sum() |
Counts the number of occurrences of a specific value across all columns. |
df.applymap(lambda x: isinstance(x, type_to_check)).sum() |
Counts the number of values of a specific type (e.g., int, str) per column. |
df.dtypes |
Lists the data type for each column in the DataFrame. |
df.sample(n) |
Returns a random sample of n rows from the DataFrame. |
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