Common dtype errors and fixes in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a common cause of dtype errors when reading CSV files in pandas?

A common cause is that pandas guesses the wrong data type for columns, especially when columns have mixed types or missing values.

beginner

How can you convert a pandas column to numeric type safely?

Use pd.to_numeric() with errors='coerce' to convert values to numbers and turn invalid parsing into NaN.

beginner

What does the error 'cannot convert string to float' usually mean in pandas?

It means pandas tried to convert a column with non-numeric strings to float but failed because some values are not numbers.

intermediate

How do you fix dtype errors caused by missing values in integer columns?

Convert the column to a nullable integer type like Int64 instead of int64 to allow NaN values.

beginner

What pandas method helps to convert columns to datetime and handle errors?

Use pd.to_datetime() with errors='coerce' to convert columns to datetime and set invalid parsing to NaT.

Which pandas function converts a column to numeric and replaces invalid parsing with NaN?

A pd.convert_numeric()

B pd.to_numeric(errors='coerce')

C pd.astype('numeric')

D pd.to_number()

The correct function is pd.to_numeric() with errors='coerce' to safely convert values.

What dtype should you use for integer columns that have missing values?

A Int64

B float64

C int64

D object

The nullable integer type Int64 supports missing values (NaN) unlike int64.

If pandas raises 'cannot convert string to float', what is likely the problem?

A The column has non-numeric strings

B The column is already float

C The column has only integers

D The column is empty

Non-numeric strings cannot be converted to float, causing this error.

Which method converts a column to datetime and sets invalid dates to NaT?

A pd.convert_datetime()

B pd.to_date()

C pd.astype('datetime')

D pd.to_datetime(errors='coerce')

pd.to_datetime() with errors='coerce' converts dates and sets invalid ones to NaT.

What is a quick way to check the data types of all columns in a pandas DataFrame?

A df.get_dtypes()

B df.types()

C df.dtypes

D df.type()

df.dtypes shows the data type of each column in the DataFrame.

Explain common dtype errors in pandas and how to fix them when reading data.

Think about how pandas guesses types and what to do when it guesses wrong.

Describe how to handle non-numeric strings in numeric columns in pandas.

Focus on safe conversion methods and handling invalid data.