Data types
- Machine Learning course
- Beginner
- Lesson 21/112
- 5 min
Data types (dtype) are categorized as numeric, categorical, and text.
Check the types
Numbers read from CSV sometimes arrive as text (object), because of values like "1.200", "?" or "35 kg" containing a unit. dtypes shows this:
print(df.dtypes)
df["fiyat"] = pd.to_numeric(df["fiyat"], errors="coerce")errors="coerce" turns values that can't be converted into NaN; they are then handled as missing values.
Type conversion and categories
The type is changed with astype. Text columns with few distinct values save memory when converted to the category type:
df["yas"] = df["yas"].astype(int)
df["sehir"] = df["sehir"].astype("category")
print(df["sehir"].cat.categories)The model still doesn't accept text; categories are encoded as numbers in the next lesson.
Practice Convert to numbers
Convert the ham list to numbers with pd.to_numeric(..., errors="coerce"); print the number of missing values with isna().sum() and the mean of the valid values with mean().
Starter code
import pandas as pd
s = pd.Series(["10", "20", "?", "30", "yok"])
# buraya yazSample solution
import pandas as pd
s = pd.Series(["10", "20", "?", "30", "yok"])
x = pd.to_numeric(s, errors="coerce")
print(x.isna().sum())
print(x.mean())Expected output
2 20.0
Questions and answers
Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.
1 What does <code>pd.to_numeric(["5", "?"], errors="coerce")</code> produce?
- [5, 0]
- ["5", "?"]
- [5.0, NaN] Correct answer
- An error
Explanation: The unconvertible "?" becomes NaN; because of NaN the result is float.
2 Convert text to numbers; let unconvertible ones become NaN.
df["fiyat"] = pd.to_numeric(df["fiyat"], errors=____)df["fiyat"] = pd.to_numeric(df["fiyat"], errors="coerce")Explanation: "raise" errors, "coerce" produces NaN.
3 Match the types.
| Term | Meaning |
|---|---|
int64 |
Integer |
float64 |
Decimal number |
object |
Text |
category |
Limited set of values |