Isnull in Pandas Workout: 200 exercises to make you a stronger data analyst

Q: How deeply does Pandas Workout: 200 exercises to make you a stronger data analyst cover isnull?

Defines Series and DataFrame results, Boolean summation, and null proportions, but defers broader missing-data strategies.

isnull is a pandas method that returns Boolean values indicating whether each Series or DataFrame value is null or missing.

Think of isnull as a missing-data detector: applied to a Series, it returns True where values are NaN or otherwise null and False elsewhere. Applied to a DataFrame, it preserves the table’s shape while marking each row-column position. Summing the Boolean result counts missing values because True behaves like 1. section 5.0

Use the returned Boolean Series or DataFrame to answer practical cleaning questions:

Count missing values in one column with `s.isnull().sum()`. section 5.0
Count missing values column by column with `df.isnull().sum()`. section 5.0
Inspect the full missing-value pattern with `df.isnull()`. section 5.0
Compare missing values with the Series shape to estimate their proportion. section 5.6

Checking ages before conversion

For a celebrity-deaths dataset, test the `age` column before converting it to integers: `df['age'].isnull().sum() / len(df['age'])`. The resulting proportion is 0.004, or 0.4 percent. After assessing that loss, remove missing ages with `df.dropna(subset=['age'])`; later, `pd.to_numeric(..., errors='coerce')` provides another way to turn unparseable values into missing values. section 5.6

Table 5.1 What you need to know (view table figure)

Concept	What is it?	Example	To learn more
`df.shape`	A two-element tuple indicating the number of rows and columns in a data frame	`df.shape`	http://mng.bz/8rpg
`len(df)` or `len(df.index)`	Gets the number of rows in a data frame	`len(df)` or `len(df.index)`	http://mng.bz/EQdr
`s.isnull`	Returns a boolean series indicating where there are null (typically `NaN`) values in the series `s`	`s.isnull()`	http://mng.bz/N2KX
`s.notnull`	Returns a boolean series indicating where there are non-null values in the series `s`	`s.notnull()`	http://mng.bz/D420
`df.isnull`	Returns a boolean data frame indicating where there are null (typically `NaN`) values in the data frame `df`	`df.isnull()`	http://mng.bz/lWGz
`df.replace`	Replaces values in one or more columns with other values	`df.replace('a':{'b':'c'), 'd')`	http://mng.bz/Bm2q
`s.map`	Applies a function to each element of a series, returning the result of that application on each element	`s.map(lambda x: x**2)`	http://mng.bz/d1yz
`df.fillna`	Replaces `NaN` with other values	`df.fillna(10)`	http://mng.bz/rWrE
`df.dropna`	Removes rows with `NaN` values	`df = df.dropna()`	http://mng.bz/V1gr
`s.str`	Works with textual data	`df['colname'].str`	http://mng.bz/x4Wq
`str.isdigit`	Returns a boolean series, indicating which strings contain only the digits 0–9	`df['colname'].str.isdigit()`	http://mng.bz/AoAE
`pd.to_numeric`	Returns a series of integers or floats based on a series of strings	`pd.to_numeric(df['colname'])`	http://mng.bz/Zq2j
`df.sort_index`	Reorders the rows of a data frame based on the values in its index in ascending order	`df = df.sort_index()`	http://mng.bz/RxAn
`pd.read_excel`	Creates a data frame based on an Excel spreadsheet	`df = pd.read_excel('myfile.xlsx')`	http://mng.bz/2DXN
`pd.read_csv`	Returns a new data frame based on CSV input	`df = pd.read_csv('myfile.csv')`	http://mng.bz/wvl7
`s.value_counts`	Returns a sorted (descending frequency) series counting how many times each value appears in `s`	`s.value_counts()`	http://mng.bz/1qzZ
`s.unique`	Returns a series with the unique (i.e., distinct) values in `s`, including `NaN` (if it occurs in `s`)	`s.unique()`	http://mng.bz/PzA2
`s.mode`	Returns a series with the most commonly found values in `s`	`s.mode()`	http://mng.bz/7vBm

Use the reference table to compare `s.isnull()` and `df.isnull()` with related shape, non-null, replacement, and mapping operations. section 5.0

How Pandas Workout: 200 exercises to make you a stronger data analyst covers it

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This explainer was generated by Manning's knowledge graph from the text of Pandas Workout: 200 exercises to make you a stronger data analyst. The section links point to the parts of the book it draws on.