Strings in a DataFrame, but dtype is object
numpy, pandas, python, series, types
Solution
The `dtype` object comes from NumPy, it describes the type of element in a `ndarray`. Every element in an `ndarray` must have the same size in bytes. For `int64` and `float64`, they are 8 bytes. But for strings, the length of the string is not fixed. So instead of saving the bytes of strings in the `ndarray` directly, Pandas uses an object `ndarray`, which saves pointers to objects; because of this the `dtype` of this kind `ndarray` is object.
Here is an example:
- the int64 array contains 4 int64 value.
- the object array contains 4 pointers to 3 string objects.
Problem
Why does Pandas tell me that I have objects, although every item in the selected column is a string — even after explicit conversion. This is my DataFrame: ``` <class 'pandas.core.frame.DataFrame'> Int64Index: 56992 entries, 0 to 56991 Data columns (total 7 columns): id 56992 non-null values attr1 56992 non-null values attr2 56992 non-null values attr3 56992 non-null values attr4 56992 non-null values attr5 56992 non-null values attr6 56992 non-null values dtypes: int64(2), object(5) ``` Five of them are `dtype object`. I explicitly convert those objects to strings: ``` for c in df.columns: if df[c].dtype == object: print "convert ", df[c].name, " to string" df[c] = df[c].astype(str) ``` Then, `df["attr2"]` still has `dtype object`, although `type(df["attr2"].ix[0]` reveals `str`, which is correct. Pandas distinguishes between `int64` and `float64` and `object`. What is the logic behind it when there is no `dtype str`? Why is a `str` covered by `object`?