When you see dtype('O') inside dataframe this means Pandas string.

What is dtype?

Something that belongs to pandas or numpy, or both, or something else? If we examine pandas code:

Copydf = pd.DataFrame({'float': [1.0],
                    'int': [1],
                    'datetime': [pd.Timestamp('20180310')],
                    'string': ['foo']})
print(df)
print(df['float'].dtype,df['int'].dtype,df['datetime'].dtype,df['string'].dtype)
df['string'].dtype

It will output like this:

Copy   float  int   datetime string    
0    1.0    1 2018-03-10    foo
---
float64 int64 datetime64[ns] object
---
dtype('O')

You can interpret the last as Pandas dtype('O') or Pandas object which is Python type string, and this corresponds to Numpy string_, or unicode_ types.

CopyPandas dtype    Python type     NumPy type          Usage
object          str             string_, unicode_   Text

Like Don Quixote is on ass, Pandas is on Numpy and Numpy understand the underlying architecture of your system and uses the class numpy.dtype for that.

Data type object is an instance of numpy.dtype class that understand the data type more precise including:

• Type of the data (integer, float, Python object, etc.)
• Size of the data (how many bytes is in e.g. the integer)
• Byte order of the data (little-endian or big-endian)
• If the data type is structured, an aggregate of other data types, (e.g., describing an array item consisting of an integer and a float)
• What are the names of the "fields" of the structure
• What is the data-type of each field
• Which part of the memory block each field takes
• If the data type is a sub-array, what is its shape and data type

In the context of this question dtype belongs to both pands and numpy and in particular dtype('O') means we expect the string.

Here is some code for testing with explanation: If we have the dataset as dictionary

Copyimport pandas as pd
import numpy as np
from pandas import Timestamp

data={'id': {0: 1, 1: 2, 2: 3, 3: 4, 4: 5}, 'date': {0: Timestamp('2018-12-12 00:00:00'), 1: Timestamp('2018-12-12 00:00:00'), 2: Timestamp('2018-12-12 00:00:00'), 3: Timestamp('2018-12-12 00:00:00'), 4: Timestamp('2018-12-12 00:00:00')}, 'role': {0: 'Support', 1: 'Marketing', 2: 'Business Development', 3: 'Sales', 4: 'Engineering'}, 'num': {0: 123, 1: 234, 2: 345, 3: 456, 4: 567}, 'fnum': {0: 3.14, 1: 2.14, 2: -0.14, 3: 41.3, 4: 3.14}}
df = pd.DataFrame.from_dict(data) #now we have a dataframe

print(df)
print(df.dtypes)

The last lines will examine the dataframe and note the output:

Copy   id       date                  role  num   fnum
0   1 2018-12-12               Support  123   3.14
1   2 2018-12-12             Marketing  234   2.14
2   3 2018-12-12  Business Development  345  -0.14
3   4 2018-12-12                 Sales  456  41.30
4   5 2018-12-12           Engineering  567   3.14
id               int64
date    datetime64[ns]
role            object
num              int64
fnum           float64
dtype: object

All kind of different dtypes

Copydf.iloc[1,:] = np.nan
df.iloc[2,:] = None

But if we try to set np.nan or None this will not affect the original column dtype. The output will be like this:

Copyprint(df)
print(df.dtypes)

    id       date         role    num   fnum
0  1.0 2018-12-12      Support  123.0   3.14
1  NaN        NaT          NaN    NaN    NaN
2  NaN        NaT         None    NaN    NaN
3  4.0 2018-12-12        Sales  456.0  41.30
4  5.0 2018-12-12  Engineering  567.0   3.14
id             float64
date    datetime64[ns]
role            object
num            float64
fnum           float64
dtype: object

So np.nan or None will not change the columns dtype, unless we set the all column rows to np.nan or None. In that case column will become float64 or object respectively.

You may try also setting single rows:

Copydf.iloc[3,:] = 0 # will convert datetime to object only
df.iloc[4,:] = '' # will convert all columns to object

And to note here, if we set string inside a non string column it will become string or object dtype.

@HYRY's answer is great. I just want to provide a little more context..

Arrays store data as contiguous, fixed-size memory blocks. The combination of these properties together is what makes arrays lightning fast for data access. For example, consider how your computer might store an array of 32-bit integers, [3,0,1].

If you ask your computer to fetch the 3rd element in the array, it'll start at the beginning and then jump across 64 bits to get to the 3rd element. Knowing exactly how many bits to jump across is what makes arrays fast.

Now consider the sequence of strings ['hello', 'i', 'am', 'a', 'banana']. Strings are objects that vary in size, so if you tried to store them in contiguous memory blocks, it'd end up looking like this.

Now your computer doesn't have a fast way to access a randomly requested element. The key to overcoming this is to use pointers. Basically, store each string in some random memory location, and fill the array with the memory address of each string. (Memory addresses are just integers.) So now, things look like this

Now, if you ask your computer to fetch the 3rd element, just as before, it can jump across 64 bits (assuming the memory addresses are 32-bit integers) and then make one extra step to go fetch the string.

The challenge for NumPy is that there's no guarantee the pointers are actually pointing to strings. That's why it reports the dtype as 'object'.

Shamelessly gonna plug my own course on NumPy where I originally discussed this.