Pandas support¶
Warning: pandas support is currently experimental, don’t expect everything to work.
It is convenient to use the Pandas package when dealing with numerical data, so Pint provides PintArray. A PintArray is a Pandas Extension Array, which allows Pandas to recognise the Quantity and store it in Pandas DataFrames and Series.
Installation¶
Pandas support is provided by the pint-pandas
package. To install it use either:
python -m pip install pint-pandas
Or:
conda install -c conda-forge pint-pandas
Basic example¶
This example will show the simplist way to use pandas with pint and the underlying objects. It’s slightly fiddly as you are not reading from a file. A more normal use case is given in Reading a csv.
First some imports (you don’t need to import pint_pandas
for this to work)
[1]:
import pandas as pd
import pint
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-1-c24a82475971> in <module>
----> 1 import pandas as pd
2 import pint
ModuleNotFoundError: No module named 'pandas'
Next, we create a DataFrame with PintArrays as columns.
[2]:
df = pd.DataFrame({
"torque": pd.Series([1, 2, 2, 3], dtype="pint[lbf ft]"),
"angular_velocity": pd.Series([1, 2, 2, 3], dtype="pint[rpm]"),
})
df
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-2-381c91812326> in <module>
----> 1 df = pd.DataFrame({
2 "torque": pd.Series([1, 2, 2, 3], dtype="pint[lbf ft]"),
3 "angular_velocity": pd.Series([1, 2, 2, 3], dtype="pint[rpm]"),
4 })
5 df
NameError: name 'pd' is not defined
Operations with columns are units aware so behave as we would intuitively expect.
[3]:
df['power'] = df['torque'] * df['angular_velocity']
df
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-3-2cfa3462697c> in <module>
----> 1 df['power'] = df['torque'] * df['angular_velocity']
2 df
NameError: name 'df' is not defined
We can see the columns’ units in the dtypes attribute
[4]:
df.dtypes
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-4-5cc0934cc03c> in <module>
----> 1 df.dtypes
NameError: name 'df' is not defined
Each column can be accessed as a Pandas Series
[5]:
df.power
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-5-63aee292d66f> in <module>
----> 1 df.power
NameError: name 'df' is not defined
Which contains a PintArray
[6]:
df.power.values
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-6-26d8785a35ae> in <module>
----> 1 df.power.values
NameError: name 'df' is not defined
The PintArray contains a Quantity
[7]:
df.power.values.quantity
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-7-ce03413c05f9> in <module>
----> 1 df.power.values.quantity
NameError: name 'df' is not defined
Pandas Series accessors are provided for most Quantity properties and methods, which will convert the result to a Series where possible.
[8]:
df.power.pint.units
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-8-ee88b103d5f4> in <module>
----> 1 df.power.pint.units
NameError: name 'df' is not defined
[9]:
df.power.pint.to("kW").values
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-9-3fafd42e5436> in <module>
----> 1 df.power.pint.to("kW").values
NameError: name 'df' is not defined
Reading from csv¶
Reading from files is the far more standard way to use pandas. To facilitate this, DataFrame accessors are provided to make it easy to get to PintArrays.
[10]:
import pandas as pd
import pint
import pint_pandas
import io
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-10-268c17893346> in <module>
----> 1 import pandas as pd
2 import pint
3 import pint_pandas
4 import io
ModuleNotFoundError: No module named 'pandas'
Here’s the contents of the csv file.
[11]:
test_data = '''speed,mech power,torque,rail pressure,fuel flow rate,fluid power
rpm,kW,N m,bar,l/min,kW
1000.0,,10.0,1000.0,10.0,
1100.0,,10.0,100000000.0,10.0,
1200.0,,10.0,1000.0,10.0,
1200.0,,10.0,1000.0,10.0,'''
Let’s read that into a DataFrame. Here io.StringIO is used in place of reading a file from disk, whereas a csv file path would typically be used and is shown commented.
[12]:
df = pd.read_csv(io.StringIO(test_data), header=[0, 1])
# df = pd.read_csv("/path/to/test_data.csv", header=[0, 1])
df
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-12-d0e741e5ad92> in <module>
----> 1 df = pd.read_csv(io.StringIO(test_data), header=[0, 1])
2 # df = pd.read_csv("/path/to/test_data.csv", header=[0, 1])
3 df
NameError: name 'pd' is not defined
Then use the DataFrame’s pint accessor’s quantify method to convert the columns from np.ndarray
s to PintArrays, with units from the bottom column level.
[13]:
df.dtypes
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-13-5cc0934cc03c> in <module>
----> 1 df.dtypes
NameError: name 'df' is not defined
[14]:
df_ = df.pint.quantify(level=-1)
df_
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-14-25b295e4ca8b> in <module>
----> 1 df_ = df.pint.quantify(level=-1)
2 df_
NameError: name 'df' is not defined
As previously, operations between DataFrame columns are unit aware
[15]:
df_.speed * df_.torque
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-15-412bdf683a33> in <module>
----> 1 df_.speed * df_.torque
NameError: name 'df_' is not defined
[16]:
df_
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-16-2d133f3be267> in <module>
----> 1 df_
NameError: name 'df_' is not defined
[17]:
df_['mech power'] = df_.speed * df_.torque
df_['fluid power'] = df_['fuel flow rate'] * df_['rail pressure']
df_
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-17-5af8013d64c7> in <module>
----> 1 df_['mech power'] = df_.speed * df_.torque
2 df_['fluid power'] = df_['fuel flow rate'] * df_['rail pressure']
3 df_
NameError: name 'df_' is not defined
The DataFrame’s pint.dequantify
method then allows us to retrieve the units information as a header row once again.
[18]:
df_.pint.dequantify()
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-18-a19beeefef66> in <module>
----> 1 df_.pint.dequantify()
NameError: name 'df_' is not defined
This allows for some rather powerful abilities. For example, to change single column units
[19]:
df_['fluid power'] = df_['fluid power'].pint.to("kW")
df_['mech power'] = df_['mech power'].pint.to("kW")
df_.pint.dequantify()
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-19-64c7f8a50f1b> in <module>
----> 1 df_['fluid power'] = df_['fluid power'].pint.to("kW")
2 df_['mech power'] = df_['mech power'].pint.to("kW")
3 df_.pint.dequantify()
NameError: name 'df_' is not defined
The units are harder to read than they need be, so lets change pints default format for displaying units.
[20]:
pint_pandas.PintType.ureg.default_format = "~P"
df_.pint.dequantify()
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-20-2e235076e975> in <module>
----> 1 pint_pandas.PintType.ureg.default_format = "~P"
2 df_.pint.dequantify()
NameError: name 'pint_pandas' is not defined
or the entire table’s units
[21]:
df_.pint.to_base_units().pint.dequantify()
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-21-df4d39be2ebb> in <module>
----> 1 df_.pint.to_base_units().pint.dequantify()
NameError: name 'df_' is not defined
Advanced example¶
This example shows alternative ways to use pint with pandas and other features.
Start with the same imports.
[22]:
import pandas as pd
import pint
import pint_pandas
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-22-d44ee33305c4> in <module>
----> 1 import pandas as pd
2 import pint
3 import pint_pandas
ModuleNotFoundError: No module named 'pandas'
We’ll be use a shorthand for PintArray
[23]:
PA_ = pint_pandas.PintArray
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-23-c1d8ee679ee9> in <module>
----> 1 PA_ = pint_pandas.PintArray
NameError: name 'pint_pandas' is not defined
And set up a unit registry and quantity shorthand.
[24]:
ureg = pint.UnitRegistry()
Q_ = ureg.Quantity
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-24-7725dbb35407> in <module>
----> 1 ureg = pint.UnitRegistry()
2 Q_ = ureg.Quantity
NameError: name 'pint' is not defined
Operations between PintArrays of different unit registry will not work. We can change the unit registry that will be used in creating new PintArrays to prevent this issue.
[25]:
pint_pandas.PintType.ureg = ureg
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-25-6e63c38edba8> in <module>
----> 1 pint_pandas.PintType.ureg = ureg
NameError: name 'ureg' is not defined
These are the possible ways to create a PintArray.
Note that pint[unit] must be used for the Series constuctor, whereas the PintArray constructor allows the unit string or object.
[26]:
df = pd.DataFrame({
"length" : pd.Series([1,2], dtype="pint[m]"),
"width" : PA_([2,3], dtype="pint[m]"),
"distance" : PA_([2,3], dtype="m"),
"height" : PA_([2,3], dtype=ureg.m),
"depth" : PA_.from_1darray_quantity(Q_([2,3],ureg.m)),
})
df
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-26-8bef4f217f9a> in <module>
----> 1 df = pd.DataFrame({
2 "length" : pd.Series([1,2], dtype="pint[m]"),
3 "width" : PA_([2,3], dtype="pint[m]"),
4 "distance" : PA_([2,3], dtype="m"),
5 "height" : PA_([2,3], dtype=ureg.m),
NameError: name 'pd' is not defined
[27]:
df.length.values.units
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-27-c15585b7a0bf> in <module>
----> 1 df.length.values.units
NameError: name 'df' is not defined