How to use the metpy.units.units.hPa function in MetPy

lev = [100000., 95000., 90000., 85000.] * units.Pa
    tmp = np.ones((3, 4, 5, 5))
    tmp[:, 0, :] = 296.
    tmp[:, 1, :] = 292.
    tmp[:, 2, :] = 290
    tmp[:, 3, :] = 288.
    tmpk = tmp * units.kelvin
    rh = np.ones((3, 4, 5, 5))
    rh[:, 0, :] = 100.
    rh[:, 1, :] = 80.
    rh[:, 2, :] = 40.
    rh[:, 3, :] = 20.
    relh = rh * units.percent
    isentlev = [296., 297., 300.] * units.kelvin
    isentprs = isentropic_interpolation(isentlev, lev, tmpk, relh, axis=1)
    trueprs = 1000. * units.hPa
    trueprs2 = 936.18057 * units.hPa
    trueprs3 = 879.446 * units.hPa
    truerh = 69.171 * units.percent
    assert isentprs[0].shape == (3, 3, 5, 5)
    assert_almost_equal(isentprs[0][:, 0, :], trueprs, 3)
    assert_almost_equal(isentprs[0][:, 1, :], trueprs2, 3)
    assert_almost_equal(isentprs[0][:, 2, :], trueprs3, 3)
    assert_almost_equal(isentprs[1][:, 1, ], truerh, 3)

df = pd.read_fwf(get_test_data('nov11_sounding.txt', as_file_obj=False),
                 skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)

df['u_wind'], df['v_wind'] = mpcalc.get_wind_components(df['speed'],
                                                        np.deg2rad(df['direction']))

# Drop any rows with all NaN values for T, Td, winds
df = df.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed',
                       'u_wind', 'v_wind'), how='all').reset_index(drop=True)

##########################################################################

# We will pull the data out of the example dataset into individual variables and
# assign units.

p = df['pressure'].values * units.hPa
T = df['temperature'].values * units.degC
Td = df['dewpoint'].values * units.degC
wind_speed = df['speed'].values * units.knots
wind_dir = df['direction'].values * units.degrees
u, v = mpcalc.get_wind_components(wind_speed, wind_dir)

##########################################################################
# Thermodynamic Calculations
# --------------------------
#
# Often times we will want to calculate some thermodynamic parameters of a
# sounding. The MetPy calc module has many such calculations already implemented!
#
# * **Lifting Condensation Level (LCL)** - The level at which an air parcel's
#   relative humidity becomes 100% when lifted along a dry adiabatic path.
# * **Parcel Path** - Path followed by a hypothetical parcel of air, beginning

td_profile["pressure"].values * units.hPa,
        td_profile["tmpc"].values * units.degC,
        td_profile["dwpc"].values * units.degC,
    )
    (mlcape, mlcin) = mixed_layer_cape_cin(
        td_profile["pressure"].values * units.hPa,
        td_profile["tmpc"].values * units.degC,
        td_profile["dwpc"].values * units.degC,
    )
    el_p, el_t = el(
        td_profile["pressure"].values * units.hPa,
        td_profile["tmpc"].values * units.degC,
        td_profile["dwpc"].values * units.degC,
    )
    lfc_p, lfc_t = lfc(
        td_profile["pressure"].values * units.hPa,
        td_profile["tmpc"].values * units.degC,
        td_profile["dwpc"].values * units.degC,
    )
    (lcl_p, lcl_t) = lcl(
        td_profile["pressure"].values[0] * units.hPa,
        td_profile["tmpc"].values[0] * units.degC,
        td_profile["dwpc"].values[0] * units.degC,
    )
    vals = [
        el_p.to(units("hPa")).m,
        lfc_p.to(units("hPa")).m,
        lcl_p.to(units("hPa")).m,
    ]
    [el_hght, lfc_hght, lcl_hght] = log_interp(
        np.array(vals, dtype="f"),
        td_profile["pressure"].values[::-1],

###########################
# Create a contour plot of temperature
contour = ContourPlot()
contour.data = narr
contour.field = 'Temperature'
contour.level = 850 * units.hPa
contour.linecolor = 'red'
contour.contours = 15

###########################
# Create an image plot of Geopotential height
img = ImagePlot()
img.data = narr
img.field = 'Geopotential_height'
img.level = 850 * units.hPa


###########################
# Plot the data on a map
panel = MapPanel()
panel.area = 'us'
panel.layers = ['coastline', 'borders', 'states', 'rivers', 'ocean', 'land']
panel.title = 'NARR Example'
panel.plots = [contour, img]

pc = PanelContainer()
pc.size = (10, 8)
pc.panels = [panel]
pc.show()

df = pd.read_fwf(get_test_data('may4_sounding.txt', as_file_obj=False),
                 skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)

df['u_wind'], df['v_wind'] = mpcalc.get_wind_components(df['speed'],
                                                        np.deg2rad(df['direction']))

# Drop any rows with all NaN values for T, Td, winds
df = df.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed',
                       'u_wind', 'v_wind'), how='all').reset_index(drop=True)

###########################################
# We will pull the data out of the example dataset into individual variables and
# assign units.

p = df['pressure'].values * units.hPa
T = df['temperature'].values * units.degC
Td = df['dewpoint'].values * units.degC
wind_speed = df['speed'].values * units.knots
wind_dir = df['direction'].values * units.degrees
u, v = mpcalc.get_wind_components(wind_speed, wind_dir)

###########################################
# Create a new figure. The dimensions here give a good aspect ratio.

fig = plt.figure(figsize=(9, 9))
add_metpy_logo(fig, 115, 100)
skew = SkewT(fig, rotation=45)

# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dictated by the typical meteorological plot
skew.plot(p, T, 'r')

# some of MetPy's sample data.

col_names = ['pressure', 'height', 'temperature', 'dewpoint', 'direction', 'speed']

df = pd.read_fwf(get_test_data('may4_sounding.txt', as_file_obj=False),
                 skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)

# Drop any rows with all NaN values for T, Td, winds
df = df.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed'), how='all'
               ).reset_index(drop=True)

###########################################
# We will pull the data out of the example dataset into individual variables and
# assign units.

p = df['pressure'].values * units.hPa
T = df['temperature'].values * units.degC
Td = df['dewpoint'].values * units.degC
wind_speed = df['speed'].values * units.knots
wind_dir = df['direction'].values * units.degrees
u, v = mpcalc.wind_components(wind_speed, wind_dir)

###########################################
# Create a new figure. The dimensions here give a good aspect ratio.

fig = plt.figure(figsize=(9, 9))
add_metpy_logo(fig, 115, 100)
skew = SkewT(fig, rotation=45)

# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dictated by the typical meteorological plot.
skew.plot(p, T, 'r')

# some of MetPy's sample data.

col_names = ['pressure', 'height', 'temperature', 'dewpoint', 'direction', 'speed']

df = pd.read_fwf(get_test_data('jan20_sounding.txt', as_file_obj=False),
                 skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)

# Drop any rows with all NaN values for T, Td, winds
df = df.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed'
                       ), how='all').reset_index(drop=True)

###########################################
# We will pull the data out of the example dataset into individual variables and
# assign units.

p = df['pressure'].values * units.hPa
T = df['temperature'].values * units.degC
Td = df['dewpoint'].values * units.degC
wind_speed = df['speed'].values * units.knots
wind_dir = df['direction'].values * units.degrees
u, v = mpcalc.wind_components(wind_speed, wind_dir)

###########################################

skew = SkewT()

# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dictated by the typical meteorological plot
skew.plot(p, T, 'r')
skew.plot(p, Td, 'g')
skew.plot_barbs(p, u, v)

col_names = ['pressure', 'height', 'temperature', 'dewpoint', 'direction', 'speed']

df = pd.read_fwf(get_test_data('nov11_sounding.txt', as_file_obj=False),
                 skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)

# Drop any rows with all NaN values for T, Td, winds
df = df.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed'
                       ), how='all').reset_index(drop=True)

##########################################################################

# We will pull the data out of the example dataset into individual variables and
# assign units.

p = df['pressure'].values * units.hPa
T = df['temperature'].values * units.degC
Td = df['dewpoint'].values * units.degC
wind_speed = df['speed'].values * units.knots
wind_dir = df['direction'].values * units.degrees
u, v = mpcalc.wind_components(wind_speed, wind_dir)

##########################################################################
# Thermodynamic Calculations
# --------------------------
#
# Often times we will want to calculate some thermodynamic parameters of a
# sounding. The MetPy calc module has many such calculations already implemented!
#
# * **Lifting Condensation Level (LCL)** - The level at which an air parcel's
#   relative humidity becomes 100% when lifted along a dry adiabatic path.
# * **Parcel Path** - Path followed by a hypothetical parcel of air, beginning