Repository: hgrecco/pint-pandas (language: python)

Issue:
dequantify with dataframes of mixed types
I have a dataframe where I have a couple of columns of PintArray types and a couple of boolean columns.  
I need to be able to dequantify the columns so I can save everything to a csv, but the .pint.dequantify()  
causes an error.

```
File "..\lib\site-packages\pint_pandas\pint_array.py", line 730, in dequantify
    df_columns["units"] = [
File "..\lib\site-packages\pint_pandas\pint_array.py", line 731, in <listcomp>
    formatter_func(df[col].values.units) for col in df.columns
AttributeError: 'numpy.ndarray' object has no attribute 'units'
```
Is there a way to get pint_pandas to skip the non-pintarray columns?

Thanks


Discussion / hints:
Not at the moment.

The quantify/dequantify was created as an easy way of getting to/from a standard dataframe, as there wasn't support for reading/writing to file at the time. This may have changed since.

Yea we can add that. We'd need a flag like "no_unit" or "N/A" to fill in the non-pintarray columns' unit level. Something that survives the roundtrip in/out of csv or other formats, and doesn't imply dimensionless.
I made a small patch in pint_array.py

```
diff --git a/pint_pandas/pint_array.py b/pint_pandas/pint_array.py
index 35f2fad..0b556be 100644
--- a/pint_pandas/pint_array.py
+++ b/pint_pandas/pint_array.py
@@ -747,12 +747,17 @@ PintArray._add_arithmetic_ops()
 PintArray._add_comparison_ops()
 register_extension_dtype(PintType)
 
+# Magic 'unit' flagging columns with no unit support, used in
+# quantify/dequantify
+NO_UNIT="N/U"
+
 
 @register_dataframe_accessor("pint")
 class PintDataFrameAccessor(object):
     def __init__(self, pandas_obj):
         self._obj = pandas_obj
 
+
     def quantify(self, level=-1):
         df = self._obj
         df_columns = df.columns.to_frame()
@@ -761,7 +766,7 @@ class PintDataFrameAccessor(object):
         df_columns = df_columns.drop(columns=unit_col_name)
 
         df_new = DataFrame(
-            {i: PintArray(df.values[:, i], unit) for i, unit in enumerate(units.values)}
+            {i: PintArray(df.values[:, i], unit) if unit != NO_UNIT else df.values[:,i] for i, unit in enumerate(units.values)}
         )
 
         df_new.columns = df_columns.index.droplevel(unit_col_name)
@@ -778,7 +783,7 @@ class PintDataFrameAccessor(object):
 
         df_columns = df.columns.to_frame()
         df_columns["units"] = [
-            formatter_func(df[col].values.units) for col in df.columns
+            formatter_func(df[col].values.units) if hasattr( df[col].values, "units") else NO_UNIT for col in df.columns
         ]
         from collections import OrderedDict
 

```

Testing (wrapped inside emacs-org jargon :(

```
#+BEGIN_SRC python :eval no-export :results output :noweb no :session *Python*
import io
import pandas as pd
import pint
import pint_pandas
#+END_SRC

#+RESULTS:
: Python 3.9.1 | packaged by conda-forge | (default, Jan 10 2021, 02:55:42) 
: [GCC 9.3.0] on linux
: Type "help", "copyright", "credits" or "license" for more information.


#+BEGIN_SRC python :eval no-export :results output :noweb no :session *Python*
df = pd.DataFrame({
     "demo": [ "demo{}".format(i) for i in range(4)],
     "torque": pd.Series([1, 2, 2, 3], dtype="pint[lbf ft]"),
     "angular_velocity": pd.Series([1, 2, 2, 3], dtype="pint[rpm]"),
})
#+END_SRC

#+RESULTS:
: /home/jj/anaconda3/envs/pdata/lib/python3.9/site-packages/pint_pandas/pint_array.py:194: RuntimeWarning: pint-pandas does not support magnitudes of <class 'int'>. Converting magnitudes to float.
:   warnings.warn(



#+BEGIN_SRC python :eval no-export :results output :noweb no :session *Python*
df['power'] = df['torque'] * df['angular_velocity']
print( df.dtypes )
#+END_SRC

#+RESULTS:
: demo                                                           object
: torque                                       pint[foot * force_pound]
: angular_velocity                         pint[revolutions_per_minute]
: power               pint[foot * force_pound * revolutions_per_minute]
: dtype: object


#+BEGIN_SRC python :eval no-export :results output :noweb no :session *Python*
strBuffer = df.pint.dequantify().to_csv(None, index=None)
print(strBuffer)
#+END_SRC

#+RESULTS:
: demo,torque,angular_velocity,power
: N/U,foot * force_pound,revolutions_per_minute,foot * force_pound * revolutions_per_minute
: demo0,1.0,1.0,1.0
: demo1,2.0,2.0,4.0
: demo2,2.0,2.0,4.0
: demo3,3.0,3.0,9.0

#+BEGIN_SRC python :eval no-export :results output :noweb no :session *Python*
df2 = pd.read_csv(io.StringIO(strBuffer), header=[0,1])
print( df2 )
print( df2.dtypes )

#+END_SRC

#+RESULTS:
#+begin_example
demo             torque       angular_velocity                                       power
     N/U foot * force_pound revolutions_per_minute foot * force_pound * revolutions_per_minute
0  demo0                1.0                    1.0                                         1.0
1  demo1                2.0                    2.0                                         4.0
2  demo2                2.0                    2.0                                         4.0
3  demo3                3.0                    3.0                                         9.0
demo              N/U                                             object
torque            foot * force_pound                             float64
angular_velocity  revolutions_per_minute                         float64
power             foot * force_pound * revolutions_per_minute    float64
dtype: object
#+end_example


#+BEGIN_SRC python :eval no-export :results output :noweb no :session *Python*
df2_ = df2.pint.quantify(level=-1)
print( df2_)
#+END_SRC

#+RESULTS:
: /home/jj/anaconda3/envs/pdata/lib/python3.9/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.
:   return np.array(qtys, dtype="object", copy=copy)
:     demo torque angular_velocity power
: 0  demo0    1.0              1.0   1.0
: 1  demo1    2.0              2.0   4.0
: 2  demo2    2.0              2.0   4.0
: 3  demo3    3.0              3.0   9.0



```
Would like to + 1. I could also use this functionality, as I frequently use pint data frames with only some columns having units. 
I came here looking to make my work with pint_pandas  more friendly to people using the pandas.testing harness.  Pandas is very self-centric, consenting only to test assertions about Series, DataFrame, and Index.  I'd like to use the same basic structures/syntax for PintArray and came here for guidnace.

Is the better way to dequantify and test the dequantified results, or is there a way to test equality (or near-equality) of PintArrays using the logical extension of the Pandas test framework?  (I'm not asking to compare Sequences with PintArrays, but rather a PintArray coming back from a program vs. a PintArray constructed within the test suite).
This solves one problem, but creates another.  If a column contains units of differing types, the heterogeneity results in the column being marked N/U, which is not correct.

Fix patch:
diff --git a/CHANGES b/CHANGES
index cabd7c0..f681a9d 100644
--- a/CHANGES
+++ b/CHANGES
@@ -13,6 +13,7 @@ pint-pandas Changelog
 - Fixed a bug where `.astype("string[pyarrow]")` would not create pyarrow-backed arrays. #101
 - Fixed bug preventing adding to empty slices of PintArrays #69
 - Notebook updated to show plotting #116 #9 #43
+- df.pint.quantify and df.pint.dequantify now allow columns without units. #132
 - Tests reorganised #131
 
 0.2 (2021-03-23)
diff --git a/notebooks/pint-pandas.ipynb b/notebooks/pint-pandas.ipynb
index 230ce79..6b0180c 100644
--- a/notebooks/pint-pandas.ipynb
+++ b/notebooks/pint-pandas.ipynb
@@ -58,9 +58,9 @@
      "output_type": "stream",
      "text": [
       "{'numpy': '1.18.5',\n",
-      " 'pandas': '1.4.0',\n",
-      " 'pint': '0.18',\n",
-      " 'pint_pandas': '0.3.dev55+g2f530f4'}\n"
+      " 'pandas': '1.3.5',\n",
+      " 'pint': '0.19.2',\n",
+      " 'pint_pandas': '0.3.dev62+g7cff3c5'}\n"
      ]
     }
    ],
@@ -454,6 +454,7 @@
    "source": [
     "test_data = '''ShaftSpeedIndex,rpm,1200,1200,1200,1600,1600,1600,2300,2300,2300\n",
     "pump,,A,B,C,A,B,C,A,B,C\n",
+    "TestDate,No Unit,01/01,01/01,01/01,01/01,01/01,01/01,01/02,01/02,01/02\n",
     "ShaftSpeed,rpm,1200,1200,1200,1600,1600,1600,2300,2300,2300\n",
     "FlowRate,m^3 h^-1,8.72,9.28,9.31,11.61,12.78,13.51,18.32,17.90,19.23\n",
     "DifferentialPressure,kPa,162.03,144.16,136.47,286.86,241.41,204.21,533.17,526.74,440.76\n",
@@ -500,6 +501,7 @@
        "    <tr>\n",
        "      <th></th>\n",
        "      <th></th>\n",
+       "      <th>TestDate</th>\n",
        "      <th>ShaftSpeed</th>\n",
        "      <th>FlowRate</th>\n",
        "      <th>DifferentialPressure</th>\n",
@@ -509,6 +511,7 @@
        "    <tr>\n",
        "      <th></th>\n",
        "      <th></th>\n",
+       "      <th>No Unit</th>\n",
        "      <th>rpm</th>\n",
        "      <th>m^3 h^-1</th>\n",
        "      <th>kPa</th>\n",
@@ -523,13 +526,15 @@
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
+       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">1200</th>\n",
        "      <th>A</th>\n",
-       "      <td>1200.0</td>\n",
+       "      <td>01/01</td>\n",
+       "      <td>1200</td>\n",
        "      <td>8.72</td>\n",
        "      <td>162.03</td>\n",
        "      <td>1.32</td>\n",
@@ -537,7 +542,8 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
-       "      <td>1200.0</td>\n",
+       "      <td>01/01</td>\n",
+       "      <td>1200</td>\n",
        "      <td>9.28</td>\n",
        "      <td>144.16</td>\n",
        "      <td>1.23</td>\n",
@@ -545,7 +551,8 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
-       "      <td>1200.0</td>\n",
+       "      <td>01/01</td>\n",
+       "      <td>1200</td>\n",
        "      <td>9.31</td>\n",
        "      <td>136.47</td>\n",
        "      <td>1.18</td>\n",
@@ -554,7 +561,8 @@
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">1600</th>\n",
        "      <th>A</th>\n",
-       "      <td>1600.0</td>\n",
+       "      <td>01/01</td>\n",
+       "      <td>1600</td>\n",
        "      <td>11.61</td>\n",
        "      <td>286.86</td>\n",
        "      <td>3.09</td>\n",
@@ -562,7 +570,8 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
-       "      <td>1600.0</td>\n",
+       "      <td>01/01</td>\n",
+       "      <td>1600</td>\n",
        "      <td>12.78</td>\n",
        "      <td>241.41</td>\n",
        "      <td>2.78</td>\n",
@@ -570,7 +579,8 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
-       "      <td>1600.0</td>\n",
+       "      <td>01/01</td>\n",
+       "      <td>1600</td>\n",
        "      <td>13.51</td>\n",
        "      <td>204.21</td>\n",
        "      <td>2.50</td>\n",
@@ -579,7 +589,8 @@
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">2300</th>\n",
        "      <th>A</th>\n",
-       "      <td>2300.0</td>\n",
+       "      <td>01/02</td>\n",
+       "      <td>2300</td>\n",
        "      <td>18.32</td>\n",
        "      <td>533.17</td>\n",
        "      <td>8.59</td>\n",
@@ -587,7 +598,8 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
-       "      <td>2300.0</td>\n",
+       "      <td>01/02</td>\n",
+       "      <td>2300</td>\n",
        "      <td>17.90</td>\n",
        "      <td>526.74</td>\n",
        "      <td>8.51</td>\n",
@@ -595,7 +607,8 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
-       "      <td>2300.0</td>\n",
+       "      <td>01/02</td>\n",
+       "      <td>2300</td>\n",
        "      <td>19.23</td>\n",
        "      <td>440.76</td>\n",
        "      <td>7.61</td>\n",
@@ -606,31 +619,31 @@
        "</div>"
       ],
       "text/plain": [
-       "                     ShaftSpeed FlowRate DifferentialPressure ShaftPower  \\\n",
-       "                            rpm m^3 h^-1                  kPa         kW   \n",
-       "ShaftSpeedIndex pump                                                       \n",
-       "1200            A        1200.0     8.72               162.03       1.32   \n",
-       "                B        1200.0     9.28               144.16       1.23   \n",
-       "                C        1200.0     9.31               136.47       1.18   \n",
-       "1600            A        1600.0    11.61               286.86       3.09   \n",
-       "                B        1600.0    12.78               241.41       2.78   \n",
-       "                C        1600.0    13.51               204.21       2.50   \n",
-       "2300            A        2300.0    18.32               533.17       8.59   \n",
-       "                B        2300.0    17.90               526.74       8.51   \n",
-       "                C        2300.0    19.23               440.76       7.61   \n",
+       "                     TestDate ShaftSpeed FlowRate DifferentialPressure  \\\n",
+       "                      No Unit        rpm m^3 h^-1                  kPa   \n",
+       "ShaftSpeedIndex pump                                                     \n",
+       "1200            A       01/01       1200     8.72               162.03   \n",
+       "                B       01/01       1200     9.28               144.16   \n",
+       "                C       01/01       1200     9.31               136.47   \n",
+       "1600            A       01/01       1600    11.61               286.86   \n",
+       "                B       01/01       1600    12.78               241.41   \n",
+       "                C       01/01       1600    13.51               204.21   \n",
+       "2300            A       01/02       2300    18.32               533.17   \n",
+       "                B       01/02       2300    17.90               526.74   \n",
+       "                C       01/02       2300    19.23               440.76   \n",
        "\n",
-       "                        Efficiency  \n",
-       "                     dimensionless  \n",
-       "ShaftSpeedIndex pump                \n",
-       "1200            A            30.60  \n",
-       "                B            31.16  \n",
-       "                C            30.70  \n",
-       "1600            A            30.72  \n",
-       "                B            31.83  \n",
-       "                C            31.81  \n",
-       "2300            A            32.52  \n",
-       "                B            31.67  \n",
-       "                C            32.05  "
+       "                     ShaftPower    Efficiency  \n",
+       "                             kW dimensionless  \n",
+       "ShaftSpeedIndex pump                           \n",
+       "1200            A          1.32         30.60  \n",
+       "                B          1.23         31.16  \n",
+       "                C          1.18         30.70  \n",
+       "1600            A          3.09         30.72  \n",
+       "                B          2.78         31.83  \n",
+       "                C          2.50         31.81  \n",
+       "2300            A          8.59         32.52  \n",
+       "                B          8.51         31.67  \n",
+       "                C          7.61         32.05  "
       ]
      },
      "execution_count": 12,
@@ -644,30 +657,59 @@
     "df"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TestDate              No Unit          object\n",
+       "ShaftSpeed            rpm              object\n",
+       "FlowRate              m^3 h^-1         object\n",
+       "DifferentialPressure  kPa              object\n",
+       "ShaftPower            kW               object\n",
+       "Efficiency            dimensionless    object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dtypes"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "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."
+    "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.\n",
+    "\n",
+    "Using 'No Unit' as the unit will prevent quantify converting a column to a PintArray. This can be changed by changing pint_pandas.pint_array.NO_UNIT."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "ShaftSpeed            rpm              float64\n",
-       "FlowRate              m^3 h^-1         float64\n",
-       "DifferentialPressure  kPa              float64\n",
-       "ShaftPower            kW               float64\n",
-       "Efficiency            dimensionless    float64\n",
+       "TestDate              No Unit          object\n",
+       "ShaftSpeed            rpm              object\n",
+       "FlowRate              m^3 h^-1         object\n",
+       "DifferentialPressure  kPa              object\n",
+       "ShaftPower            kW               object\n",
+       "Efficiency            dimensionless    object\n",
        "dtype: object"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -678,9 +720,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "F:\\A\\repos\\pint-pandas\\pint_pandas\\pint_array.py:225: RuntimeWarning: pint-pandas does not support magnitudes of object. Converting magnitudes to float.\n",
+      "  warnings.warn(\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -703,6 +753,7 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
+       "      <th>TestDate</th>\n",
        "      <th>ShaftSpeed</th>\n",
        "      <th>FlowRate</th>\n",
        "      <th>DifferentialPressure</th>\n",
@@ -717,12 +768,14 @@
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
+       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">1200</th>\n",
        "      <th>A</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>1200.0</td>\n",
        "      <td>8.72</td>\n",
        "      <td>162.03</td>\n",
@@ -731,6 +784,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>1200.0</td>\n",
        "      <td>9.28</td>\n",
        "      <td>144.16</td>\n",
@@ -739,6 +793,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>1200.0</td>\n",
        "      <td>9.31</td>\n",
        "      <td>136.47</td>\n",
@@ -748,6 +803,7 @@
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">1600</th>\n",
        "      <th>A</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>1600.0</td>\n",
        "      <td>11.61</td>\n",
        "      <td>286.86</td>\n",
@@ -756,6 +812,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>1600.0</td>\n",
        "      <td>12.78</td>\n",
        "      <td>241.41</td>\n",
@@ -764,6 +821,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>1600.0</td>\n",
        "      <td>13.51</td>\n",
        "      <td>204.21</td>\n",
@@ -773,6 +831,7 @@
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">2300</th>\n",
        "      <th>A</th>\n",
+       "      <td>01/02</td>\n",
        "      <td>2300.0</td>\n",
        "      <td>18.32</td>\n",
        "      <td>533.17</td>\n",
@@ -781,6 +840,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
+       "      <td>01/02</td>\n",
        "      <td>2300.0</td>\n",
        "      <td>17.9</td>\n",
        "      <td>526.74</td>\n",
@@ -789,6 +849,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
+       "      <td>01/02</td>\n",
        "      <td>2300.0</td>\n",
        "      <td>19.23</td>\n",
        "      <td>440.76</td>\n",
@@ -800,32 +861,32 @@
        "</div>"
       ],
       "text/plain": [
-       "                     ShaftSpeed FlowRate DifferentialPressure ShaftPower  \\\n",
-       "ShaftSpeedIndex pump                                                       \n",
-       "1200            A        1200.0     8.72               162.03       1.32   \n",
-       "                B        1200.0     9.28               144.16       1.23   \n",
-       "                C        1200.0     9.31               136.47       1.18   \n",
-       "1600            A        1600.0    11.61               286.86       3.09   \n",
-       "                B        1600.0    12.78               241.41       2.78   \n",
-       "                C        1600.0    13.51               204.21        2.5   \n",
-       "2300            A        2300.0    18.32               533.17       8.59   \n",
-       "                B        2300.0     17.9               526.74       8.51   \n",
-       "                C        2300.0    19.23               440.76       7.61   \n",
+       "                     TestDate ShaftSpeed FlowRate DifferentialPressure  \\\n",
+       "ShaftSpeedIndex pump                                                     \n",
+       "1200            A       01/01     1200.0     8.72               162.03   \n",
+       "                B       01/01     1200.0     9.28               144.16   \n",
+       "                C       01/01     1200.0     9.31               136.47   \n",
+       "1600            A       01/01     1600.0    11.61               286.86   \n",
+       "                B       01/01     1600.0    12.78               241.41   \n",
+       "                C       01/01     1600.0    13.51               204.21   \n",
+       "2300            A       01/02     2300.0    18.32               533.17   \n",
+       "                B       01/02     2300.0     17.9               526.74   \n",
+       "                C       01/02     2300.0    19.23               440.76   \n",
        "\n",
-       "                     Efficiency  \n",
-       "ShaftSpeedIndex pump             \n",
-       "1200            A          30.6  \n",
-       "                B         31.16  \n",
-       "                C          30.7  \n",
-       "1600            A         30.72  \n",
-       "                B         31.83  \n",
-       "                C         31.81  \n",
-       "2300            A         32.52  \n",
-       "                B         31.67  \n",
-       "                C         32.05  "
+       "                     ShaftPower Efficiency  \n",
+       "ShaftSpeedIndex pump                        \n",
+       "1200            A          1.32       30.6  \n",
+       "                B          1.23      31.16  \n",
+       "                C          1.18       30.7  \n",
+       "1600            A          3.09      30.72  \n",
+       "                B          2.78      31.83  \n",
+       "                C           2.5      31.81  \n",
+       "2300            A          8.59      32.52  \n",
+       "                B          8.51      31.67  \n",
+       "                C          7.61      32.05  "
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -844,21 +905,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "ShaftSpeed                    pint[revolutions_per_minute]\n",
-       "FlowRate                pint[meter ** 3 / planck_constant]\n",
-       "DifferentialPressure                      pint[kilopascal]\n",
-       "ShaftPower                                  pint[kilowatt]\n",
-       "Efficiency                             pint[dimensionless]\n",
+       "TestDate                                      object\n",
+       "ShaftSpeed              pint[revolutions_per_minute]\n",
+       "FlowRate                     pint[meter ** 3 / hour]\n",
+       "DifferentialPressure                pint[kilopascal]\n",
+       "ShaftPower                            pint[kilowatt]\n",
+       "Efficiency                       pint[dimensionless]\n",
        "dtype: object"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -876,12 +938,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
+       "TestDate                                      object\n",
        "ShaftSpeed              pint[revolutions_per_minute]\n",
        "FlowRate                     pint[meter ** 3 / hour]\n",
        "DifferentialPressure                pint[kilopascal]\n",
@@ -890,7 +953,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -909,7 +972,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -928,7 +991,7 @@
        "dtype: pint[kilowatt / revolutions_per_minute]"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -939,7 +1002,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -964,6 +1027,7 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
+       "      <th>TestDate</th>\n",
        "      <th>ShaftSpeed</th>\n",
        "      <th>FlowRate</th>\n",
        "      <th>DifferentialPressure</th>\n",
@@ -982,12 +1046,14 @@
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
+       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">1200</th>\n",
        "      <th>A</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>1200.0</td>\n",
        "      <td>8.72</td>\n",
        "      <td>162.03</td>\n",
@@ -998,6 +1064,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>1200.0</td>\n",
        "      <td>9.28</td>\n",
        "      <td>144.16</td>\n",
@@ -1008,6 +1075,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>1200.0</td>\n",
        "      <td>9.31</td>\n",
        "      <td>136.47</td>\n",
@@ -1019,6 +1087,7 @@
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">1600</th>\n",
        "      <th>A</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>1600.0</td>\n",
        "      <td>11.61</td>\n",
        "      <td>286.86</td>\n",
@@ -1029,6 +1098,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>1600.0</td>\n",
        "      <td>12.78</td>\n",
        "      <td>241.41</td>\n",
@@ -1039,6 +1109,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>1600.0</td>\n",
        "      <td>13.51</td>\n",
        "      <td>204.21</td>\n",
@@ -1050,6 +1121,7 @@
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">2300</th>\n",
        "      <th>A</th>\n",
+       "      <td>01/02</td>\n",
        "      <td>2300.0</td>\n",
        "      <td>18.32</td>\n",
        "      <td>533.17</td>\n",
@@ -1060,6 +1132,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
+       "      <td>01/02</td>\n",
        "      <td>2300.0</td>\n",
        "      <td>17.9</td>\n",
        "      <td>526.74</td>\n",
@@ -1070,6 +1143,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
+       "      <td>01/02</td>\n",
        "      <td>2300.0</td>\n",
        "      <td>19.23</td>\n",
        "      <td>440.76</td>\n",
@@ -1083,32 +1157,44 @@
        "</div>"
       ],
       "text/plain": [
-       "                     ShaftSpeed FlowRate DifferentialPressure ShaftPower  \\\n",
-       "ShaftSpeedIndex pump                                                       \n",
-       "1200            A        1200.0     8.72               162.03       1.32   \n",
-       "                B        1200.0     9.28               144.16       1.23   \n",
-       "                C        1200.0     9.31               136.47       1.18   \n",
-       "1600            A        1600.0    11.61               286.86       3.09   \n",
-       "                B        1600.0    12.78               241.41       2.78   \n",
-       "                C        1600.0    13.51               204.21        2.5   \n",
-       "2300            A        2300.0    18.32               533.17       8.59   \n",
-       "                B        2300.0     17.9               526.74       8.51   \n",
-       "                C        2300.0    19.23               440.76       7.61   \n",
+       "                     TestDate ShaftSpeed FlowRate DifferentialPressure  \\\n",
+       "ShaftSpeedIndex pump                                                     \n",
+       "1200            A       01/01     1200.0     8.72               162.03   \n",
+       "                B       01/01     1200.0     9.28               144.16   \n",
+       "                C       01/01     1200.0     9.31               136.47   \n",
+       "1600            A       01/01     1600.0    11.61               286.86   \n",
+       "                B       01/01     1600.0    12.78               241.41   \n",
+       "                C       01/01     1600.0    13.51               204.21   \n",
+       "2300            A       01/02     2300.0    18.32               533.17   \n",
+       "                B       01/02     2300.0     17.9               526.74   \n",
+       "                C       01/02     2300.0    19.23               440.76   \n",
        "\n",
-       "                     Efficiency            ShaftTorque          FluidPower  \n",
-       "ShaftSpeedIndex pump                                                        \n",
-       "1200            A          30.6                 0.0011  1412.9016000000001  \n",
-       "                B         31.16               0.001025           1337.8048  \n",
-       "                C          30.7  0.0009833333333333332  1270.5357000000001  \n",
-       "1600            A         30.72  0.0019312499999999998           3330.4446  \n",
-       "                B         31.83              0.0017375           3085.2198  \n",
-       "                C         31.81              0.0015625           2758.8771  \n",
-       "2300            A         32.52   0.003734782608695652           9767.6744  \n",
-       "                B         31.67  0.0036999999999999997   9428.645999999999  \n",
-       "                C         32.05  0.0033086956521739133           8475.8148  "
+       "                     ShaftPower Efficiency            ShaftTorque  \\\n",
+       "ShaftSpeedIndex pump                                                \n",
+       "1200            A          1.32       30.6                 0.0011   \n",
+       "                B          1.23      31.16               0.001025   \n",
+       "                C          1.18       30.7  0.0009833333333333332   \n",
+       "1600            A          3.09      30.72  0.0019312499999999998   \n",
+       "                B          2.78      31.83              0.0017375   \n",
+       "                C           2.5      31.81              0.0015625   \n",
+       "2300            A          8.59      32.52   0.003734782608695652   \n",
+       "                B          8.51      31.67  0.0036999999999999997   \n",
+       "                C          7.61      32.05  0.0033086956521739133   \n",
+       "\n",
+       "                              FluidPower  \n",
+       "ShaftSpeedIndex pump                      \n",
+       "1200            A     1412.9016000000001  \n",
+       "                B              1337.8048  \n",
+       "                C     1270.5357000000001  \n",
+       "1600            A              3330.4446  \n",
+       "                B              3085.2198  \n",
+       "                C              2758.8771  \n",
+       "2300            A              9767.6744  \n",
+       "                B      9428.645999999999  \n",
+       "                C              8475.8148  "
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1128,7 +1214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1157,6 +1243,7 @@
        "    <tr>\n",
        "      <th></th>\n",
        "      <th></th>\n",
+       "      <th>TestDate</th>\n",
        "      <th>ShaftSpeed</th>\n",
        "      <th>FlowRate</th>\n",
        "      <th>DifferentialPressure</th>\n",
@@ -1168,6 +1255,7 @@
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>unit</th>\n",
+       "      <th>No Unit</th>\n",
        "      <th>revolutions_per_minute</th>\n",
        "      <th>meter ** 3 / hour</th>\n",
        "      <th>kilopascal</th>\n",
@@ -1186,12 +1274,14 @@
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
+       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">1200</th>\n",
        "      <th>A</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>1200.0</td>\n",
        "      <td>8.72</td>\n",
        "      <td>162.03</td>\n",
@@ -1202,6 +1292,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>1200.0</td>\n",
        "      <td>9.28</td>\n",
        "      <td>144.16</td>\n",
@@ -1212,6 +1303,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>1200.0</td>\n",
        "      <td>9.31</td>\n",
        "      <td>136.47</td>\n",
@@ -1223,6 +1315,7 @@
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">1600</th>\n",
        "      <th>A</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>1600.0</td>\n",
        "      <td>11.61</td>\n",
        "      <td>286.86</td>\n",
@@ -1233,6 +1326,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>1600.0</td>\n",
        "      <td>12.78</td>\n",
        "      <td>241.41</td>\n",
@@ -1243,6 +1337,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>1600.0</td>\n",
        "      <td>13.51</td>\n",
        "      <td>204.21</td>\n",
@@ -1254,6 +1349,7 @@
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">2300</th>\n",
        "      <th>A</th>\n",
+       "      <td>01/02</td>\n",
        "      <td>2300.0</td>\n",
        "      <td>18.32</td>\n",
        "      <td>533.17</td>\n",
@@ -1264,6 +1360,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
+       "      <td>01/02</td>\n",
        "      <td>2300.0</td>\n",
        "      <td>17.90</td>\n",
        "      <td>526.74</td>\n",
@@ -1274,6 +1371,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
+       "      <td>01/02</td>\n",
        "      <td>2300.0</td>\n",
        "      <td>19.23</td>\n",
        "      <td>440.76</td>\n",
@@ -1287,18 +1385,18 @@
        "</div>"
       ],
       "text/plain": [
-       "                                 ShaftSpeed          FlowRate  \\\n",
-       "unit                 revolutions_per_minute meter ** 3 / hour   \n",
-       "ShaftSpeedIndex pump                                            \n",
-       "1200            A                    1200.0              8.72   \n",
-       "                B                    1200.0              9.28   \n",
-       "                C                    1200.0              9.31   \n",
-       "1600            A                    1600.0             11.61   \n",
-       "                B                    1600.0             12.78   \n",
-       "                C                    1600.0             13.51   \n",
-       "2300            A                    2300.0             18.32   \n",
-       "                B                    2300.0             17.90   \n",
-       "                C                    2300.0             19.23   \n",
+       "                     TestDate             ShaftSpeed          FlowRate  \\\n",
+       "unit                  No Unit revolutions_per_minute meter ** 3 / hour   \n",
+       "ShaftSpeedIndex pump                                                     \n",
+       "1200            A       01/01                 1200.0              8.72   \n",
+       "                B       01/01                 1200.0              9.28   \n",
+       "                C       01/01                 1200.0              9.31   \n",
+       "1600            A       01/01                 1600.0             11.61   \n",
+       "                B       01/01                 1600.0             12.78   \n",
+       "                C       01/01                 1600.0             13.51   \n",
+       "2300            A       01/02                 2300.0             18.32   \n",
+       "                B       01/02                 2300.0             17.90   \n",
+       "                C       01/02                 2300.0             19.23   \n",
        "\n",
        "                     DifferentialPressure ShaftPower    Efficiency  \\\n",
        "unit                           kilopascal   kilowatt dimensionless   \n",
@@ -1340,7 +1438,7 @@
        "                C                         8475.8148  "
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1353,12 +1451,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This allows for some rather powerful abilities. For example, to change single column units"
+    "This allows for some rather powerful abilities. For example, to change a column's units"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1387,6 +1485,7 @@
        "    <tr>\n",
        "      <th></th>\n",
        "      <th></th>\n",
+       "      <th>TestDate</th>\n",
        "      <th>ShaftSpeed</th>\n",
        "      <th>FlowRate</th>\n",
        "      <th>DifferentialPressure</th>\n",
@@ -1398,6 +1497,7 @@
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>unit</th>\n",
+       "      <th>No Unit</th>\n",
        "      <th>revolutions_per_minute</th>\n",
        "      <th>liter / second</th>\n",
        "      <th>kilopascal</th>\n",
@@ -1416,12 +1516,14 @@
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
+       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">1200</th>\n",
        "      <th>A</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>1200.0</td>\n",
        "      <td>2.422222</td>\n",
        "      <td>162.03</td>\n",
@@ -1432,6 +1534,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>1200.0</td>\n",
        "      <td>2.577778</td>\n",
        "      <td>144.16</td>\n",
@@ -1442,6 +1545,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>1200.0</td>\n",
        "      <td>2.586111</td>\n",
        "      <td>136.47</td>\n",
@@ -1453,6 +1557,7 @@
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">1600</th>\n",
        "      <th>A</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>1600.0</td>\n",
        "      <td>3.225000</td>\n",
        "      <td>286.86</td>\n",
@@ -1463,6 +1568,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>1600.0</td>\n",
        "      <td>3.550000</td>\n",
        "      <td>241.41</td>\n",
@@ -1473,6 +1579,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>1600.0</td>\n",
        "      <td>3.752778</td>\n",
        "      <td>204.21</td>\n",
@@ -1484,6 +1591,7 @@
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">2300</th>\n",
        "      <th>A</th>\n",
+       "      <td>01/02</td>\n",
        "      <td>2300.0</td>\n",
        "      <td>5.088889</td>\n",
        "      <td>533.17</td>\n",
@@ -1494,6 +1602,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
+       "      <td>01/02</td>\n",
        "      <td>2300.0</td>\n",
        "      <td>4.972222</td>\n",
        "      <td>526.74</td>\n",
@@ -1504,6 +1613,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
+       "      <td>01/02</td>\n",
        "      <td>2300.0</td>\n",
        "      <td>5.341667</td>\n",
        "      <td>440.76</td>\n",
@@ -1517,18 +1627,18 @@
        "</div>"
       ],
       "text/plain": [
-       "                                 ShaftSpeed       FlowRate  \\\n",
-       "unit                 revolutions_per_minute liter / second   \n",
-       "ShaftSpeedIndex pump                                         \n",
-       "1200            A                    1200.0       2.422222   \n",
-       "                B                    1200.0       2.577778   \n",
-       "                C                    1200.0       2.586111   \n",
-       "1600            A                    1600.0       3.225000   \n",
-       "                B                    1600.0       3.550000   \n",
-       "                C                    1600.0       3.752778   \n",
-       "2300            A                    2300.0       5.088889   \n",
-       "                B                    2300.0       4.972222   \n",
-       "                C                    2300.0       5.341667   \n",
+       "                     TestDate             ShaftSpeed       FlowRate  \\\n",
+       "unit                  No Unit revolutions_per_minute liter / second   \n",
+       "ShaftSpeedIndex pump                                                  \n",
+       "1200            A       01/01                 1200.0       2.422222   \n",
+       "                B       01/01                 1200.0       2.577778   \n",
+       "                C       01/01                 1200.0       2.586111   \n",
+       "1600            A       01/01                 1600.0       3.225000   \n",
+       "                B       01/01                 1600.0       3.550000   \n",
+       "                C       01/01                 1600.0       3.752778   \n",
+       "2300            A       01/02                 2300.0       5.088889   \n",
+       "                B       01/02                 2300.0       4.972222   \n",
+       "                C       01/02                 2300.0       5.341667   \n",
        "\n",
        "                     DifferentialPressure ShaftPower    Efficiency  \\\n",
        "unit                           kilopascal   kilowatt dimensionless   \n",
@@ -1557,7 +1667,7 @@
        "                C         31.595716   2.354393  "
       ]
      },
-     "execution_count": 20,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1578,7 +1688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1607,6 +1717,7 @@
        "    <tr>\n",
        "      <th></th>\n",
        "      <th></th>\n",
+       "      <th>TestDate</th>\n",
        "      <th>ShaftSpeed</th>\n",
        "      <th>FlowRate</th>\n",
        "      <th>DifferentialPressure</th>\n",
@@ -1618,6 +1729,7 @@
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>unit</th>\n",
+       "      <th>No Unit</th>\n",
        "      <th>rpm</th>\n",
        "      <th>l/s</th>\n",
        "      <th>kPa</th>\n",
@@ -1636,12 +1748,14 @@
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
+       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">1200</th>\n",
        "      <th>A</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>1200.0</td>\n",
        "      <td>2.422222</td>\n",
        "      <td>162.03</td>\n",
@@ -1652,6 +1766,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>1200.0</td>\n",
        "      <td>2.577778</td>\n",
        "      <td>144.16</td>\n",
@@ -1662,6 +1777,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>1200.0</td>\n",
        "      <td>2.586111</td>\n",
        "      <td>136.47</td>\n",
@@ -1673,6 +1789,7 @@
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">1600</th>\n",
        "      <th>A</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>1600.0</td>\n",
        "      <td>3.225000</td>\n",
        "      <td>286.86</td>\n",
@@ -1683,6 +1800,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>1600.0</td>\n",
        "      <td>3.550000</td>\n",
        "      <td>241.41</td>\n",
@@ -1693,6 +1811,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>1600.0</td>\n",
        "      <td>3.752778</td>\n",
        "      <td>204.21</td>\n",
@@ -1704,6 +1823,7 @@
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">2300</th>\n",
        "      <th>A</th>\n",
+       "      <td>01/02</td>\n",
        "      <td>2300.0</td>\n",
        "      <td>5.088889</td>\n",
        "      <td>533.17</td>\n",
@@ -1714,6 +1834,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
+       "      <td>01/02</td>\n",
        "      <td>2300.0</td>\n",
        "      <td>4.972222</td>\n",
        "      <td>526.74</td>\n",
@@ -1724,6 +1845,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
+       "      <td>01/02</td>\n",
        "      <td>2300.0</td>\n",
        "      <td>5.341667</td>\n",
        "      <td>440.76</td>\n",
@@ -1737,34 +1859,34 @@
        "</div>"
       ],
       "text/plain": [
-       "                     ShaftSpeed  FlowRate DifferentialPressure ShaftPower  \\\n",
-       "unit                        rpm       l/s                  kPa         kW   \n",
-       "ShaftSpeedIndex pump                                                        \n",
-       "1200            A        1200.0  2.422222               162.03       1.32   \n",
-       "                B        1200.0  2.577778               144.16       1.23   \n",
-       "                C        1200.0  2.586111               136.47       1.18   \n",
-       "1600            A        1600.0  3.225000               286.86       3.09   \n",
-       "                B        1600.0  3.550000               241.41       2.78   \n",
-       "                C        1600.0  3.752778               204.21       2.50   \n",
-       "2300            A        2300.0  5.088889               533.17       8.59   \n",
-       "                B        2300.0  4.972222               526.74       8.51   \n",
-       "                C        2300.0  5.341667               440.76       7.61   \n",
+       "                     TestDate ShaftSpeed  FlowRate DifferentialPressure  \\\n",
+       "unit                  No Unit        rpm       l/s                  kPa   \n",
+       "ShaftSpeedIndex pump                                                      \n",
+       "1200            A       01/01     1200.0  2.422222               162.03   \n",
+       "                B       01/01     1200.0  2.577778               144.16   \n",
+       "                C       01/01     1200.0  2.586111               136.47   \n",
+       "1600            A       01/01     1600.0  3.225000               286.86   \n",
+       "                B       01/01     1600.0  3.550000               241.41   \n",
+       "                C       01/01     1600.0  3.752778               204.21   \n",
+       "2300            A       01/02     2300.0  5.088889               533.17   \n",
+       "                B       01/02     2300.0  4.972222               526.74   \n",
+       "                C       01/02     2300.0  5.341667               440.76   \n",
        "\n",
-       "                     Efficiency ShaftTorque FluidPower  \n",
-       "unit                                    N·m         kW  \n",
-       "ShaftSpeedIndex pump                                    \n",
-       "1200            A         30.60   10.504226   0.392473  \n",
-       "                B         31.16    9.788029   0.371612  \n",
-       "                C         30.70    9.390142   0.352927  \n",
-       "1600            A         30.72   18.442079   0.925123  \n",
-       "                B         31.83   16.591903   0.857005  \n",
-       "                C         31.81   14.920776   0.766355  \n",
-       "2300            A         32.52   35.664547   2.713243  \n",
-       "                B         31.67   35.332397   2.619068  \n",
-       "                C         32.05   31.595716   2.354393  "
+       "                     ShaftPower Efficiency ShaftTorque FluidPower  \n",
+       "unit                         kW                    N·m         kW  \n",
+       "ShaftSpeedIndex pump                                               \n",
+       "1200            A          1.32      30.60   10.504226   0.392473  \n",
+       "                B          1.23      31.16    9.788029   0.371612  \n",
+       "                C          1.18      30.70    9.390142   0.352927  \n",
+       "1600            A          3.09      30.72   18.442079   0.925123  \n",
+       "                B          2.78      31.83   16.591903   0.857005  \n",
+       "                C          2.50      31.81   14.920776   0.766355  \n",
+       "2300            A          8.59      32.52   35.664547   2.713243  \n",
+       "                B          8.51      31.67   35.332397   2.619068  \n",
+       "                C          7.61      32.05   31.595716   2.354393  "
       ]
      },
-     "execution_count": 21,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1783,7 +1905,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1812,6 +1934,7 @@
        "    <tr>\n",
        "      <th></th>\n",
        "      <th></th>\n",
+       "      <th>TestDate</th>\n",
        "      <th>ShaftSpeed</th>\n",
        "      <th>FlowRate</th>\n",
        "      <th>DifferentialPressure</th>\n",
@@ -1823,6 +1946,7 @@
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>unit</th>\n",
+       "      <th>No Unit</th>\n",
        "      <th>rad/s</th>\n",
        "      <th>m³/s</th>\n",
        "      <th>kg/m/s²</th>\n",
@@ -1841,12 +1965,14 @@
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
+       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">1200</th>\n",
        "      <th>A</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>125.663706</td>\n",
        "      <td>0.002422</td>\n",
        "      <td>162030.0</td>\n",
@@ -1857,6 +1983,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>125.663706</td>\n",
        "      <td>0.002578</td>\n",
        "      <td>144160.0</td>\n",
@@ -1867,6 +1994,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>125.663706</td>\n",
        "      <td>0.002586</td>\n",
        "      <td>136470.0</td>\n",
@@ -1878,6 +2006,7 @@
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">1600</th>\n",
        "      <th>A</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>167.551608</td>\n",
        "      <td>0.003225</td>\n",
        "      <td>286860.0</td>\n",
@@ -1888,6 +2017,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>167.551608</td>\n",
        "      <td>0.003550</td>\n",
        "      <td>241410.0</td>\n",
@@ -1898,6 +2028,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
+       "      <td>01/01</td>\n",
        "      <td>167.551608</td>\n",
        "      <td>0.003753</td>\n",
        "      <td>204210.0</td>\n",
@@ -1909,6 +2040,7 @@
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">2300</th>\n",
        "      <th>A</th>\n",
+       "      <td>01/02</td>\n",
        "      <td>240.855437</td>\n",
        "      <td>0.005089</td>\n",
        "      <td>533170.0</td>\n",
@@ -1919,6 +2051,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
+       "      <td>01/02</td>\n",
        "      <td>240.855437</td>\n",
        "      <td>0.004972</td>\n",
        "      <td>526740.0</td>\n",
@@ -1929,6 +2062,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
+       "      <td>01/02</td>\n",
        "      <td>240.855437</td>\n",
        "      <td>0.005342</td>\n",
        "      <td>440760.0</td>\n",
@@ -1942,34 +2076,34 @@
        "</div>"
       ],
       "text/plain": [
-       "                      ShaftSpeed  FlowRate DifferentialPressure ShaftPower  \\\n",
-       "unit                       rad/s      m³/s              kg/m/s²   kg·m²/s³   \n",
-       "ShaftSpeedIndex pump                                                         \n",
-       "1200            A     125.663706  0.002422             162030.0     1320.0   \n",
-       "                B     125.663706  0.002578             144160.0     1230.0   \n",
-       "                C     125.663706  0.002586             136470.0     1180.0   \n",
-       "1600            A     167.551608  0.003225             286860.0     3090.0   \n",
-       "                B     167.551608  0.003550             241410.0     2780.0   \n",
-       "                C     167.551608  0.003753             204210.0     2500.0   \n",
-       "2300            A     240.855437  0.005089             533170.0     8590.0   \n",
-       "                B     240.855437  0.004972             526740.0     8510.0   \n",
-       "                C     240.855437  0.005342             440760.0     7610.0   \n",
+       "                     TestDate  ShaftSpeed  FlowRate DifferentialPressure  \\\n",
+       "unit                  No Unit       rad/s      m³/s              kg/m/s²   \n",
+       "ShaftSpeedIndex pump                                                       \n",
+       "1200            A       01/01  125.663706  0.002422             162030.0   \n",
+       "                B       01/01  125.663706  0.002578             144160.0   \n",
+       "                C       01/01  125.663706  0.002586             136470.0   \n",
+       "1600            A       01/01  167.551608  0.003225             286860.0   \n",
+       "                B       01/01  167.551608  0.003550             241410.0   \n",
+       "                C       01/01  167.551608  0.003753             204210.0   \n",
+       "2300            A       01/02  240.855437  0.005089             533170.0   \n",
+       "                B       01/02  240.855437  0.004972             526740.0   \n",
+       "                C       01/02  240.855437  0.005342             440760.0   \n",
        "\n",
-       "                     Efficiency ShaftTorque   FluidPower  \n",
-       "unit                               kg·m²/s²     kg·m²/s³  \n",
-       "ShaftSpeedIndex pump                                      \n",
-       "1200            A         30.60   10.504226   392.472667  \n",
-       "                B         31.16    9.788029   371.612444  \n",
-       "                C         30.70    9.390142   352.926583  \n",
-       "1600            A         30.72   18.442079   925.123500  \n",
-       "                B         31.83   16.591903   857.005500  \n",
-       "                C         31.81   14.920776   766.354750  \n",
-       "2300            A         32.52   35.664547  2713.242889  \n",
-       "                B         31.67   35.332397  2619.068333  \n",
-       "                C         32.05   31.595716  2354.393000  "
+       "                     ShaftPower Efficiency ShaftTorque   FluidPower  \n",
+       "unit                   kg·m²/s³               kg·m²/s²     kg·m²/s³  \n",
+       "ShaftSpeedIndex pump                                                 \n",
+       "1200            A        1320.0      30.60   10.504226   392.472667  \n",
+       "                B        1230.0      31.16    9.788029   371.612444  \n",
+       "                C        1180.0      30.70    9.390142   352.926583  \n",
+       "1600            A        3090.0      30.72   18.442079   925.123500  \n",
+       "                B        2780.0      31.83   16.591903   857.005500  \n",
+       "                C        2500.0      31.81   14.920776   766.354750  \n",
+       "2300            A        8590.0      32.52   35.664547  2713.242889  \n",
+       "                B        8510.0      31.67   35.332397  2619.068333  \n",
+       "                C        7610.0      32.05   31.595716  2354.393000  "
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1988,19 +2122,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -2011,22 +2143,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "kilowatt"
+       "kW"
       ],
       "text/latex": [
-       "$\\mathrm{kilowatt}$"
+       "$\\mathrm{kW}$"
       ],
       "text/plain": [
        "<Unit('kilowatt')>"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2044,7 +2176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -2071,7 +2203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2089,7 +2221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2105,7 +2237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2122,7 +2254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2140,14 +2272,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "c:\\users\\a\\repos\\pint-pandas\\pint_pandas\\pint_array.py:220: RuntimeWarning: pint-pandas does not support magnitudes of int32. Converting magnitudes to float.\n",
+      "F:\\A\\repos\\pint-pandas\\pint_pandas\\pint_array.py:225: RuntimeWarning: pint-pandas does not support magnitudes of int32. Converting magnitudes to float.\n",
       "  warnings.warn(\n"
      ]
     },
@@ -2209,7 +2341,7 @@
        "1    2.0   3.0      3.0    3.0   3.0          3.0"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2221,14 +2353,14 @@
     "        \"distance\" : PA_([2.,3.], dtype=\"m\"),\n",
     "        \"height\" : PA_([2.,3.], dtype=ureg.m),\n",
     "        \"depth\" : PA_.from_1darray_quantity(Q_([2,3],ureg.m)),\n",
-    "        \"displacement\" : PA_(Q_([2,3],ureg.m)),\n",
+    "        \"displacement\" : PA_(Q_([2.,3.],ureg.m)),\n",
     "    })\n",
     "df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -2243,7 +2375,7 @@
        "<Unit('meter')>"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2251,21 +2383,14 @@
    "source": [
     "df.length.values.units"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [conda env:pint-pandas-feb22]",
+   "display_name": "Python [conda env:pint-pandas-dev]",
    "language": "python",
-   "name": "conda-env-pint-pandas-feb22-py"
+   "name": "conda-env-pint-pandas-dev-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -2277,7 +2402,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.8.13"
   }
  },
  "nbformat": 4,
diff --git a/pint_pandas/pint_array.py b/pint_pandas/pint_array.py
index bf14a74..f7d118a 100644
--- a/pint_pandas/pint_array.py
+++ b/pint_pandas/pint_array.py
@@ -1,6 +1,7 @@
 import copy
 import re
 import warnings
+from collections import OrderedDict
 
 import numpy as np
 import pandas as pd
@@ -22,6 +23,10 @@
 from pint.quantity import _Quantity
 from pint.unit import _Unit
 
+# Magic 'unit' flagging columns with no unit support, used in
+# quantify/dequantify
+NO_UNIT = "No Unit"
+
 
 class PintType(ExtensionDtype):
     """
@@ -807,7 +812,12 @@ def quantify(self, level=-1):
         df_columns = df_columns.drop(columns=unit_col_name)
 
         df_new = DataFrame(
-            {i: PintArray(df.values[:, i], unit) for i, unit in enumerate(units.values)}
+            {
+                i: PintArray(df.values[:, i], unit)
+                if unit != NO_UNIT
+                else df.values[:, i]
+                for i, unit in enumerate(units.values)
+            }
         )
 
         df_new.columns = df_columns.index.droplevel(unit_col_name)
@@ -823,12 +833,20 @@ def formatter_func(dtype):
         df = self._obj
 
         df_columns = df.columns.to_frame()
-        df_columns["units"] = [formatter_func(df[col].dtype) for col in df.columns]
-        from collections import OrderedDict
+        df_columns["units"] = [
+            formatter_func(df[col].dtype)
+            if isinstance(df[col].dtype, PintType)
+            else NO_UNIT
+            for col in df.columns
+        ]
 
         data_for_df = OrderedDict()
         for i, col in enumerate(df.columns):
-            data_for_df[tuple(df_columns.iloc[i])] = df[col].values.data
+            if isinstance(df[col].dtype, PintType):
+                data_for_df[tuple(df_columns.iloc[i])] = df[col].values.data
+            else:
+                data_for_df[tuple(df_columns.iloc[i])] = df[col].values
+
         df_new = DataFrame(data_for_df, columns=data_for_df.keys())
 
         df_new.columns.names = df.columns.names + ["unit"]
@@ -842,7 +860,15 @@ def to_base_units(self):
         index = object.__getattribute__(obj, "index")
         # name = object.__getattribute__(obj, '_name')
         return DataFrame(
-            {col: df[col].pint.to_base_units() for col in df.columns}, index=index
+            {
+                col: (
+                    df[col].pint.to_base_units()
+                    if isinstance(df[col].dtype, PintType)
+                    else df[col]
+                )
+                for col in df.columns
+            },
+            index=index,
         )
 
 


Test patch:
diff --git a/pint_pandas/testsuite/test_pandas_interface.py b/pint_pandas/testsuite/test_pandas_interface.py
index 2cd5004..9c2268a 100644
--- a/pint_pandas/testsuite/test_pandas_interface.py
+++ b/pint_pandas/testsuite/test_pandas_interface.py
@@ -89,6 +89,83 @@ def test_df_operations(self):
 
         df_.pint.to_base_units().pint.dequantify()
 
+    def test_dequantify(self):
+        df = pd.DataFrame(
+            {
+                "no_unit_column": pd.Series([i for i in range(4)], dtype=float),
+                "pintarray_column": pd.Series(
+                    [1.0, 2.0, 2.0, 3.0], dtype="pint[lbf ft]"
+                ),
+            }
+        )
+        expected = pd.DataFrame(
+            {
+                ("no_unit_column", "No Unit"): {0: 0.0, 1: 1.0, 2: 2.0, 3: 3.0},
+                ("pintarray_column", "foot * force_pound"): {
+                    0: 1.0,
+                    1: 2.0,
+                    2: 2.0,
+                    3: 3.0,
+                },
+            }
+        )
+        expected.columns.names = [None, "unit"]
+
+        result = df.pint.dequantify()
+        pd.testing.assert_frame_equal(result, expected)
+
+    def test_quantify(self):
+        df = pd.DataFrame(
+            {
+                ("no_unit_column", "No Unit"): {0: 0.0, 1: 1.0, 2: 2.0, 3: 3.0},
+                ("pintarray_column", "foot * force_pound"): {
+                    0: 1.0,
+                    1: 2.0,
+                    2: 2.0,
+                    3: 3.0,
+                },
+            }
+        )
+        df.columns.names = [None, "unit"]
+        expected = pd.DataFrame(
+            {
+                "no_unit_column": pd.Series([i for i in range(4)], dtype=float),
+                "pintarray_column": pd.Series(
+                    [1.0, 2.0, 2.0, 3.0], dtype="pint[lbf ft]"
+                ),
+            }
+        )
+
+        result = df.pint.quantify()
+        pd.testing.assert_frame_equal(result, expected)
+
+        def test_to_base_units(self):
+            df = pd.DataFrame(
+                {
+                    "no_unit_column": pd.Series([i for i in range(4)], dtype=float),
+                    "pintarray_column": pd.Series(
+                        [1.0, 2.0, 2.0, 3.0], dtype="pint[lbf ft]"
+                    ),
+                }
+            )
+            result = df.pint.to_base_units()
+
+            expected = pd.DataFrame(
+                {
+                    "no_unit_column": pd.Series([i for i in range(4)], dtype=float),
+                    "pintarray_column": pd.Series(
+                        [
+                            1.3558179483314006,
+                            2.711635896662801,
+                            2.711635896662801,
+                            4.067453844994201,
+                        ],
+                        dtype="pint[kilogram * meter ** 2 / second ** 2]",
+                    ),
+                }
+            )
+            pd.testing.assert_frame_equal(result, expected)
+
 
 class TestDataFrameAccessor(object):
     def test_index_maintained(self):