Checking missing null values in pandas dataframe python
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okay, let's dive deep into handling missing values (often represented as `nan` or `none`) in pandas dataframes. this is a crucial step in data analysis and preparation because missing data can significantly impact your results and model performance.
*1. understanding missing data*
*what are missing values?*
missing values signify that a data point is absent or unknown. this can occur for various reasons, such as:
data entry errors
data corruption
information not available during data collection
data privacy restrictions leading to intentional omission
*representation in pandas:*
`nan` (not a number): a special floating-point value often used to represent missing numerical data. it's part of the ieee 754 floating-point standard. pandas automatically converts missing numerical values to `nan`.
`none`: a python built-in constant representing the absence of a value. pandas can also use `none` for missing data, especially in object (string) columns.
`nat` (not a time): represents missing datetime values.
empty strings (""): sometimes, empty strings are used, particularly in string columns, to represent a lack of data. while not technically `nan` or `none`, you might want to consider them as missing based on your data's context.
*2. identifying missing values*
the core pandas functions for detecting missing values are `isnull()` and `notnull()`.
*`isnull()`:* returns a dataframe or series of booleans. `true` indicates a missing value, `false` indicates a non-missing value.
*`notnull()`:* the opposite of `isnull()`. `true` indicates a non-missing value, `false` indicates a missing value.
here's a demonstration:
*3. summarizing missing values*
instead of just viewing boolean dataframes, we often want to know how many missing values exist in each column. we can chain `isnull()` (or `notnull()`) with `sum()`.
*explanation:*
1. `df.isnull().sum()`: `isnu ...
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