diff --git a/pandas/plotting/_matplotlib/timeseries.py b/pandas/plotting/_matplotlib/timeseries.py
index d4587a6ccf90a..da395197737b7 100644
--- a/pandas/plotting/_matplotlib/timeseries.py
+++ b/pandas/plotting/_matplotlib/timeseries.py
@@ -310,7 +310,7 @@ def maybe_convert_index(ax: Axes, data: NDFrameT) -> NDFrameT:
             if isinstance(data.index, ABCDatetimeIndex):
                 data = data.tz_localize(None).to_period(freq=freq_str)
             elif isinstance(data.index, ABCPeriodIndex):
-                data.index = data.index.asfreq(freq=freq_str)
+                data.index = data.index.asfreq(freq=freq_str, how = "S") #Lineplot alignes to the start of period  
     return data
 
 
diff --git a/pandas/tests/indexes/period/test_period_range.py b/pandas/tests/indexes/period/test_period_range.py
index 6f8e6d07da8bf..369900afb6dec 100644
--- a/pandas/tests/indexes/period/test_period_range.py
+++ b/pandas/tests/indexes/period/test_period_range.py
@@ -79,10 +79,16 @@ class TestPeriodRange:
     @pytest.mark.parametrize(
         "freq_offset, freq_period",
         [
+            ("h", "h"),
+            ("7h", "7h"),
             ("D", "D"),
-            ("W", "W"),
-            ("QE", "Q"),
+            ("15D", "15D"),
+            ("ME", "M"),
+            ("3ME", "3M"),
             ("YE", "Y"),
+            ("8YE", "8Y"),
+            ("20s", "20s"),
+            ("2QE", "2Q"),
         ],
     )
     def test_construction_from_string(self, freq_offset, freq_period):
@@ -113,34 +119,6 @@ def test_construction_from_string(self, freq_offset, freq_period):
         result = period_range(start=end, end=start, freq=freq_period, name="foo")
         tm.assert_index_equal(result, expected)
 
-    def test_construction_from_string_monthly(self):
-        # non-empty
-        expected = date_range(
-            start="2017-01-01", periods=5, freq="ME", name="foo"
-        ).to_period()
-        start, end = str(expected[0]), str(expected[-1])
-
-        result = period_range(start=start, end=end, freq="M", name="foo")
-        tm.assert_index_equal(result, expected)
-
-        result = period_range(start=start, periods=5, freq="M", name="foo")
-        tm.assert_index_equal(result, expected)
-
-        result = period_range(end=end, periods=5, freq="M", name="foo")
-        tm.assert_index_equal(result, expected)
-
-        # empty
-        expected = PeriodIndex([], freq="M", name="foo")
-
-        result = period_range(start=start, periods=0, freq="M", name="foo")
-        tm.assert_index_equal(result, expected)
-
-        result = period_range(end=end, periods=0, freq="M", name="foo")
-        tm.assert_index_equal(result, expected)
-
-        result = period_range(start=end, end=start, freq="M", name="foo")
-        tm.assert_index_equal(result, expected)
-
     def test_construction_from_period(self):
         # upsampling
         start, end = Period("2017Q1", freq="Q"), Period("2018Q1", freq="Q")
diff --git a/pandas/tests/plotting/common.py b/pandas/tests/plotting/common.py
index 5a46cdcb051b6..de52edab53d62 100644
--- a/pandas/tests/plotting/common.py
+++ b/pandas/tests/plotting/common.py
@@ -541,6 +541,30 @@ def _check_plot_works(f, default_axes=False, **kwargs):
         plt.close(fig)
 
     return ret
+    
+def _check_inputdata_equals_outputdata(idx, ax):
+    from pandas.core.indexes.datetimes import (
+        DatetimeIndex,
+    )
+
+    """
+    Check that the input dates is equal to the plotted dates
+    Parameters
+    ----------
+    idx : PeriodIndex , DatetimeIndex
+        The input data based on freq
+    ax : matplotlib Axes object
+        The plotted data
+    """
+
+    #Convert to periodIndex to use same formatting as the output
+    if isinstance(idx, DatetimeIndex):
+        idx = idx.to_period()
+    input = [str(period) for period in idx]
+    output_unformatted = ax.get_lines()[0].get_xdata()
+    output = [str(period) for period in output_unformatted]
+    # Convert idx and lines to lists of strings representing dates
+    assert input == output
 
 
 def _gen_default_plot(f, fig, **kwargs):
diff --git a/pandas/tests/plotting/test_datetimelike.py b/pandas/tests/plotting/test_datetimelike.py
index d478fbfb46b08..402b5eada7d86 100644
--- a/pandas/tests/plotting/test_datetimelike.py
+++ b/pandas/tests/plotting/test_datetimelike.py
@@ -38,6 +38,7 @@
 )
 from pandas.core.indexes.timedeltas import timedelta_range
 from pandas.tests.plotting.common import _check_ticks_props
+from pandas.tests.plotting.common import _check_inputdata_equals_outputdata
 
 from pandas.tseries.offsets import WeekOfMonth
 
@@ -69,6 +70,31 @@ def test_fontsize_set_correctly(self):
         for label in ax.get_xticklabels() + ax.get_yticklabels():
             assert label.get_fontsize() == 2
 
+    @pytest.mark.parametrize("freq", ["20s","3s","60min","5min","7h","4D","8W","11M","2Q","Y", "3Y", "10Y"])
+    def test_period_range_content(self, freq):
+        # Setup your DataFrame as in your scenario
+        idx = period_range("2000-01-01", freq=freq, periods=4)
+        df = DataFrame(
+            np.array([0, 1, 0, 1]),
+            index=idx,
+            columns=["A"],
+        )
+
+        ax = df.plot()
+        _check_inputdata_equals_outputdata(idx=idx, ax=ax)
+
+    @pytest.mark.parametrize("freq", ["20s","3s","60min","5min","7h","4D","8W","11ME","2QE","YE", "3YE", "10YE"])
+    def test_date_range_content(self, freq):
+        # Setup your DataFrame as in your scenario
+        idx = date_range("2000/01/01", freq=freq, periods=4)
+        df = DataFrame(
+            np.array([0, 1, 0, 1]),
+            index=idx,
+            columns=["A"],
+        )
+        ax = df.plot()
+        _check_inputdata_equals_outputdata(idx=idx, ax=ax)
+
     def test_frame_inferred(self):
         # inferred freq
         idx = date_range("1/1/1987", freq="MS", periods=100)