Why the type of pd.DataFrame every items is float, but the dtype of pd.DataFrame is object?
results_table is a pd.DataFrame
When I
print(type(results_table.loc[0,'Mean recall score']))
it return
<class 'numpy.float64'>
Every items is float
But when I
print(results_table['Mean recall score'].dtype)
it returns
object
Why is there such behavior?
python pandas
edited Nov 16 '18 at 0:39
RafaelC
28.1k83154
asked Nov 16 '18 at 0:37
SIRIUSSIRIUS
82
add a comment |
results_table is a pd.DataFrame
When I
print(type(results_table.loc[0,'Mean recall score']))
it return
<class 'numpy.float64'>
Every items is float
But when I
print(results_table['Mean recall score'].dtype)
it returns
object
Why is there such behavior?
python pandas
edited Nov 16 '18 at 0:39
RafaelC
28.1k83154
asked Nov 16 '18 at 0:37
SIRIUSSIRIUS
82
1
There are some scenarios where every item in a series is a float but thedtypeisobject. For example, some error when reading from file that was coerced; or when you had mixed types (e.g. floats and strings) and substituted the strings with other floats at a later time; etc. Just usepd.to_numeric(df['score'])or.astype(float)directly
– RafaelC
Nov 16 '18 at 0:41
add a comment |
results_table is a pd.DataFrame
When I
print(type(results_table.loc[0,'Mean recall score']))
it return
<class 'numpy.float64'>
Every items is float
But when I
print(results_table['Mean recall score'].dtype)
it returns
object
Why is there such behavior?
python pandas
edited Nov 16 '18 at 0:39
RafaelC
28.1k83154
asked Nov 16 '18 at 0:37
SIRIUSSIRIUS
82
results_table is a pd.DataFrame
When I
print(type(results_table.loc[0,'Mean recall score']))
it return
<class 'numpy.float64'>
Every items is float
But when I
print(results_table['Mean recall score'].dtype)
it returns
object
Why is there such behavior?
python pandas
python pandas
edited Nov 16 '18 at 0:39
RafaelC
28.1k83154
asked Nov 16 '18 at 0:37
SIRIUSSIRIUS
82
edited Nov 16 '18 at 0:39
RafaelC
28.1k83154
asked Nov 16 '18 at 0:37
SIRIUSSIRIUS
82
edited Nov 16 '18 at 0:39
RafaelC
28.1k83154
edited Nov 16 '18 at 0:39
RafaelC
28.1k83154
edited Nov 16 '18 at 0:39
RafaelC
28.1k83154
28.1k83154
asked Nov 16 '18 at 0:37
SIRIUSSIRIUS
82
asked Nov 16 '18 at 0:37
SIRIUSSIRIUS
82
asked Nov 16 '18 at 0:37
SIRIUSSIRIUS
82
82
1
There are some scenarios where every item in a series is a float but thedtypeisobject. For example, some error when reading from file that was coerced; or when you had mixed types (e.g. floats and strings) and substituted the strings with other floats at a later time; etc. Just usepd.to_numeric(df['score'])or.astype(float)directly
– RafaelC
Nov 16 '18 at 0:41
add a comment |
1
There are some scenarios where every item in a series is a float but thedtypeisobject. For example, some error when reading from file that was coerced; or when you had mixed types (e.g. floats and strings) and substituted the strings with other floats at a later time; etc. Just usepd.to_numeric(df['score'])or.astype(float)directly
– RafaelC
Nov 16 '18 at 0:41
1
1
There are some scenarios where every item in a series is a float but the
dtype is object. For example, some error when reading from file that was coerced; or when you had mixed types (e.g. floats and strings) and substituted the strings with other floats at a later time; etc. Just use pd.to_numeric(df['score']) or .astype(float) directly– RafaelC
Nov 16 '18 at 0:41
There are some scenarios where every item in a series is a float but the
dtype is object. For example, some error when reading from file that was coerced; or when you had mixed types (e.g. floats and strings) and substituted the strings with other floats at a later time; etc. Just use pd.to_numeric(df['score']) or .astype(float) directly– RafaelC
Nov 16 '18 at 0:41
add a comment |
2 Answers
2
active
oldest
votes
First note df.loc[0, x] only considers the value in row label 0 and column label x, not your entire dataframe. Now let's consider an example:
df = pd.DataFrame({'A': [1.5, 'hello', 'test', 2]}, dtype=object)
print(type(df.loc[0, 'A'])) # type of single element in series
<class 'float'>
print(df['A'].dtype) # type of series
object
As you can see, an object dtype series can hold arbitrary Python objects. You can even, if you wish, extract the type of each element of your series:
print(df['A'].map(type))
0 <class 'float'>
1 <class 'str'>
2 <class 'str'>
3 <class 'int'>
Name: A, dtype: object
An object dtype series is simply a collection of pointers to various objects not held in a contiguous memory block, as may be the case with numeric series. This is comparable to Python list and explains why performance is poor when you work with object instead of numeric series.
See also this answer for a visual respresentation of the above.
answered Nov 16 '18 at 0:50
jppjpp
102k2165116
add a comment |
In the first print statement you are slicing out one single element from you dataframe. This single item you are looking at is a float.
In the second print statement you are actually pulling out a pandas series (ie you are pulling out the whole column) and printing the type of that.
The pandas series is an object, but each entry in the series is a float. So this is why you get the results you did.
answered Nov 16 '18 at 0:55
James FultonJames Fulton
1825
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
First note df.loc[0, x] only considers the value in row label 0 and column label x, not your entire dataframe. Now let's consider an example:
df = pd.DataFrame({'A': [1.5, 'hello', 'test', 2]}, dtype=object)
print(type(df.loc[0, 'A'])) # type of single element in series
<class 'float'>
print(df['A'].dtype) # type of series
object
As you can see, an object dtype series can hold arbitrary Python objects. You can even, if you wish, extract the type of each element of your series:
print(df['A'].map(type))
0 <class 'float'>
1 <class 'str'>
2 <class 'str'>
3 <class 'int'>
Name: A, dtype: object
An object dtype series is simply a collection of pointers to various objects not held in a contiguous memory block, as may be the case with numeric series. This is comparable to Python list and explains why performance is poor when you work with object instead of numeric series.
See also this answer for a visual respresentation of the above.
answered Nov 16 '18 at 0:50
jppjpp
102k2165116
add a comment |
First note df.loc[0, x] only considers the value in row label 0 and column label x, not your entire dataframe. Now let's consider an example:
df = pd.DataFrame({'A': [1.5, 'hello', 'test', 2]}, dtype=object)
print(type(df.loc[0, 'A'])) # type of single element in series
<class 'float'>
print(df['A'].dtype) # type of series
object
As you can see, an object dtype series can hold arbitrary Python objects. You can even, if you wish, extract the type of each element of your series:
print(df['A'].map(type))
0 <class 'float'>
1 <class 'str'>
2 <class 'str'>
3 <class 'int'>
Name: A, dtype: object
An object dtype series is simply a collection of pointers to various objects not held in a contiguous memory block, as may be the case with numeric series. This is comparable to Python list and explains why performance is poor when you work with object instead of numeric series.
See also this answer for a visual respresentation of the above.
answered Nov 16 '18 at 0:50
jppjpp
102k2165116
add a comment |
First note df.loc[0, x] only considers the value in row label 0 and column label x, not your entire dataframe. Now let's consider an example:
df = pd.DataFrame({'A': [1.5, 'hello', 'test', 2]}, dtype=object)
print(type(df.loc[0, 'A'])) # type of single element in series
<class 'float'>
print(df['A'].dtype) # type of series
object
As you can see, an object dtype series can hold arbitrary Python objects. You can even, if you wish, extract the type of each element of your series:
print(df['A'].map(type))
0 <class 'float'>
1 <class 'str'>
2 <class 'str'>
3 <class 'int'>
Name: A, dtype: object
An object dtype series is simply a collection of pointers to various objects not held in a contiguous memory block, as may be the case with numeric series. This is comparable to Python list and explains why performance is poor when you work with object instead of numeric series.
See also this answer for a visual respresentation of the above.
answered Nov 16 '18 at 0:50
jppjpp
102k2165116
First note df.loc[0, x] only considers the value in row label 0 and column label x, not your entire dataframe. Now let's consider an example:
df = pd.DataFrame({'A': [1.5, 'hello', 'test', 2]}, dtype=object)
print(type(df.loc[0, 'A'])) # type of single element in series
<class 'float'>
print(df['A'].dtype) # type of series
object
As you can see, an object dtype series can hold arbitrary Python objects. You can even, if you wish, extract the type of each element of your series:
print(df['A'].map(type))
0 <class 'float'>
1 <class 'str'>
2 <class 'str'>
3 <class 'int'>
Name: A, dtype: object
An object dtype series is simply a collection of pointers to various objects not held in a contiguous memory block, as may be the case with numeric series. This is comparable to Python list and explains why performance is poor when you work with object instead of numeric series.
See also this answer for a visual respresentation of the above.
answered Nov 16 '18 at 0:50
jppjpp
102k2165116
edited Nov 16 '18 at 0:55
answered Nov 16 '18 at 0:50
jppjpp
102k2165116
answered Nov 16 '18 at 0:50
jppjpp
102k2165116
answered Nov 16 '18 at 0:50
jppjpp
102k2165116
102k2165116
add a comment |
add a comment |
In the first print statement you are slicing out one single element from you dataframe. This single item you are looking at is a float.
In the second print statement you are actually pulling out a pandas series (ie you are pulling out the whole column) and printing the type of that.
The pandas series is an object, but each entry in the series is a float. So this is why you get the results you did.
answered Nov 16 '18 at 0:55
James FultonJames Fulton
1825
add a comment |
In the first print statement you are slicing out one single element from you dataframe. This single item you are looking at is a float.
In the second print statement you are actually pulling out a pandas series (ie you are pulling out the whole column) and printing the type of that.
The pandas series is an object, but each entry in the series is a float. So this is why you get the results you did.
answered Nov 16 '18 at 0:55
James FultonJames Fulton
1825
add a comment |
In the first print statement you are slicing out one single element from you dataframe. This single item you are looking at is a float.
In the second print statement you are actually pulling out a pandas series (ie you are pulling out the whole column) and printing the type of that.
The pandas series is an object, but each entry in the series is a float. So this is why you get the results you did.
answered Nov 16 '18 at 0:55
James FultonJames Fulton
1825
In the first print statement you are slicing out one single element from you dataframe. This single item you are looking at is a float.
In the second print statement you are actually pulling out a pandas series (ie you are pulling out the whole column) and printing the type of that.
The pandas series is an object, but each entry in the series is a float. So this is why you get the results you did.
answered Nov 16 '18 at 0:55
James FultonJames Fulton
1825
answered Nov 16 '18 at 0:55
James FultonJames Fulton
1825
answered Nov 16 '18 at 0:55
James FultonJames Fulton
1825
answered Nov 16 '18 at 0:55
James FultonJames Fulton
1825
1825
add a comment |
add a comment |
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1
There are some scenarios where every item in a series is a float but the
dtypeisobject. For example, some error when reading from file that was coerced; or when you had mixed types (e.g. floats and strings) and substituted the strings with other floats at a later time; etc. Just usepd.to_numeric(df['score'])or.astype(float)directly– RafaelC
Nov 16 '18 at 0:41