dtypes in pandas

What each column is holding, and how to change it on purpose.

Ask before you calculate

dtypes tells you what every column is. A column of numbers read from a file is very often object, which means text, and averaging it will either fail or lie.

import pandas as pd

df = pd.DataFrame({"score": ["92", "88"], "n": [1, 2]})
print(df.dtypes)

Converting a column

astype changes a column's type when every value can make the trip. to_numeric with errors="coerce" is the forgiving version: anything it cannot convert becomes missing rather than raising.

import pandas as pd

df = pd.DataFrame({"score": ["92", "88"]})
df["score"] = df["score"].astype(int)
print(df["score"].sum())

messy = pd.Series(["92", "n/a"])
print(pd.to_numeric(messy, errors="coerce").tolist())

Exercise

Try It Yourself

Write numeric_total(df) that returns the sum of the score column, where the scores arrived as text and anything that is not a number counts as zero.

import pandas as pd

def numeric_total(df):
    # Sum the score column, treating junk as 0.
    return 0

print(numeric_total(pd.DataFrame({"score": ["92", "88"]})))

Press Run to see output

Check Your Understanding

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