How do I count the missing values in each column with pandas?

The third step of the method has two halves: how many values are missing and why. This page is the first half. On the previous page, data.info() already left you a clue: duracion_min, estacion_destino and distancia_km have 303 values each, so each one is missing exactly 8. Before counting anything, place a bet.

Check yourself: Three columns have exactly 8 missing values each: duracion_min, estacion_destino and distancia_km. What do you think is going on?

Counting the gaps

data.isna() returns a table the same size as data, with True where a value is missing and False where it isn't. Summing it counts the True values in each column, because Python treats True as 1. And since the average of a set of ones and zeros is the share of ones, isna().mean() gives you the fraction of gaps.

Your turn

Run the cell to count the gaps in each column. Then change it to (data.isna().mean() * 100).round(1) to see them as percentages. You're done when you know which column has the highest share of gaps and how high it is.

Data: viajes-rueda, 311 rows

The exercise's data, "viajes-rueda". Your edits run with your code.
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Your code (Python)

Five columns are complete. Three are missing 2.6% (8 of 311) and anio_nacimiento is missing 23.5% (73 of 311), almost one row in four.

A percentage helps you size things up, but it doesn't decide anything on its own. A 2.6% concentrated on the last day can ruin that day's report; a 23.5% concentrated in one group can wipe out that whole group if you drop the rows. That's why the next question is where the gaps are, and the first way to find out is to see whether several columns go empty together.

First, practice on a small table. Write down what each line prints and why.

Gaps that go together

example.py

import pandas as pdviajes = pd.DataFrame({    "duracion_min": [12.5, None, 30.0, None, 8.4],    "estacion_destino": ["Centro", None, "Estadio", None, "Mercado"],    "anio_nacimiento": [1990, 1985, None, None, 2001],})faltan_ambas = viajes["duracion_min"].isna() & viajes["estacion_destino"].isna()print(viajes.isna().sum().sum())print(faltan_ambas.sum())print(viajes.isna().any(axis=1).sum())

What three numbers does it print?

Why?

A guess is fine. What you write stays in this browser and waits in your course.

Now on the Rueda table. The code saves two masks, one per column, and counts the rows where both are true.

Your turn

Run the cell. Then add a third mask for distancia_km and count the rows where all three are missing at once. Finish by showing those rows with data[faltan_duracion]. You're done when you can see the 8 rows and what they have in common.

Data: viajes-rueda, 311 rows

The exercise's data, "viajes-rueda". Your edits run with your code.
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Your code (Python)

They are the same 8 records: they lack a destination station, distance (computed between stations), and duration (measured until closure). Joint gaps suggest investigating a shared process; they don't prove a single cause. Look for a pattern and confirm the explanation before treating each column separately. Look at the inicio column of those 8 rows: the next page starts there.

Gaps that don't look like gaps

isna() counts only what pandas recognizes as missing (its documentation notes that an empty string, '', doesn't count either). In real-world tables, missing data shows up in many other forms:

text that says it's missing: "N/A", "sin dato" ("no data"), "-", a cell with a single space;
a filler value the system puts in when there's no data: 0, 9999, 1900.

Rueda has one of these. In anio_nacimiento there are three rows with 1900, a year that suggests a filler value and needs confirmation; isna() doesn't count them. You'll see them in module 3. If the filler text comes from the file itself, you can tell pandas about it when you read the file; another free Maestro course, on CSV files that open wrong, covers that.

Your log entry for this page could be:

Observation: 8 records have no destination, distance or duration; 73 are missing the birth year. Evidence: isna().sum() and the combined count of the three masks. Question: why do those 8 trips have no destination? And why is almost one record in four missing a birth year?

If the gaps in your table are written as text, ask your Maestro how to find them.

Try it

Check yourself: In a new table, isna().sum() answers 0 for the column telefono. Can you be sure no phone number is missing?

pandas: DataFrame.isna, in the official documentation