It means:
'O' (Python) objects
Source.
The first character specifies the kind of data and the remaining characters specify the number of bytes per item, except for Unicode, where it is interpreted as the number of characters. The item size must correspond to an existing type, or an error will be raised. The supported kinds are to an existing type, or an error will be raised. The supported kinds are:
'b' boolean
'i' (signed) integer
'u' unsigned integer
'f' floating-point
'c' complex-floating point
'O' (Python) objects
'S', 'a' (byte-)string
'U' Unicode
'V' raw data (void)
Another answer helps if need check type
s.
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:
df = 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:
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.
Pandas 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:
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
import 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:
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
df.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:
print(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:
df.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
.
It means "a python object", i.e. not one of the builtin scalar types supported by numpy.
np.array([object()]).dtype
=> dtype('O')
'O' stands for object.
#Loading a csv file as a dataframe
import pandas as pd
train_df = pd.read_csv('train.csv')
col_name = 'Name of Employee'
#Checking the datatype of column name
train_df[col_name].dtype
#Instead try printing the same thing
print train_df[col_name].dtype
The first line returns: dtype('O')
The line with the print statement returns the following: object
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