Convert Float to String in Pandas

Question

I am a little confused with datatype "object" in Pandas. What exactly is "object"?

I would like to change the variable "SpT" (see below) from object to String.

> df_cleaned.dtypes
    Vmag        float64
    RA          float64
    DE          float64
    Plx         float64
    pmRA        float64
    pmDE        float64
    B-V         float64
    SpT          object
    M_V         float64
    distance    float64
    dtype: object

For this I do the following:

df_cleaned['SpT'] = df_cleaned['SpT'].astype(str)

But that has no effect on the dtype of SpT.

The reason for doing is when I do the following:

f = lambda s: (len(s) >= 2)  and (s[0].isalpha()) and (s[1].isdigit())
i  = df_cleaned['SpT'].apply(f)
df_cleaned = df_cleaned[i]

I get:

TypeError: object of type 'float' has no len()

Hence, I believe if I convert "object" to "String", I will get to do what I want.

More info: This is how SpT looks like:

HIP
1                F5
2               K3V
3                B9
4               F0V
5             G8III
6              M0V:
7                G0
8      M6e-M8.5e Tc
9                G5
10              F6V
11               A2
12            K4III
13            K0III
14               K0
15               K2
...
118307    M2III:
118308        K:
118309        A2
118310        K5
118312        G5
118313        F0
118314        K0
118315     K0III
118316        F2
118317        F8
118318        K2
118319       G2V
118320        K0
118321       G5V
118322      B9IV
Name: SpT, Length: 114472, dtype: object

Answer 1

If a column contains string or is treated as string, it will have a dtype of object (but not necessarily true backward -- more below). Here is a simple example:

import pandas as pd
df = pd.DataFrame({'SpT': ['string1', 'string2', 'string3'],
                   'num': ['0.1', '0.2', '0.3'],
                   'strange': ['0.1', '0.2', 0.3]})
print df.dtypes
#SpT        object
#num        object
#strange    object
#dtype: object

If a column contains only strings, we can apply len on it like what you did should work fine:

print df['num'].apply(lambda x: len(x))
#0    3
#1    3
#2    3

However, a dtype of object does not means it only contains strings. For example, the column strange contains objects with mixed types -- and some str and a float. Applying the function len will raise an error similar to what you have seen:

print df['strange'].apply(lambda x: len(x))
# TypeError: object of type 'float' has no len()

Thus, the problem could be that you have not properly converted the column to string, and the column still contains mixed object types.

Continuing the above example, let us convert strange to strings and check if apply works:

df['strange'] = df['strange'].astype(str)
print df['strange'].apply(lambda x: len(x))
#0    3
#1    3
#2    3

(There is a suspicious discrepancy between df_cleaned and df_clean there in your question, is it a typo or a mistake in the code that causes the problem?)