@@ -5690,7 +5690,7 @@ def reindex(
56905690
56915691 * None (default): don't fill gaps
56925692 * pad / ffill: Propagate last valid observation forward to next
5693- valid.
5693+
56945694 * backfill / bfill: Use next valid observation to fill gap.
56955695 * nearest: Use nearest valid observations to fill gap.
56965696
@@ -5757,7 +5757,7 @@ def reindex(
57575757 ... index=index,
57585758 ... )
57595759 >>> df
5760-
5760+ http_status response_time
57615761 Firefox 200 0.04
57625762 Chrome 200 0.02
57635763 Safari 404 0.07
@@ -5770,7 +5770,7 @@ def reindex(
57705770
57715771 >>> new_index = ["Safari", "Iceweasel", "Comodo Dragon", "IE10", "Chrome"]
57725772 >>> df.reindex(new_index)
5773-
5773+ http_status response_time
57745774 Safari 404.0 0.07
57755775 Iceweasel NaN NaN
57765776 Comodo Dragon NaN NaN
@@ -5783,15 +5783,15 @@ def reindex(
57835783 ``method`` to fill the ``NaN`` values.
57845784
57855785 >>> df.reindex(new_index, fill_value=0)
5786-
5786+ http_status response_time
57875787 Safari 404 0.07
57885788 Iceweasel 0 0.00
57895789 Comodo Dragon 0 0.00
57905790 IE10 404 0.08
57915791 Chrome 200 0.02
57925792
57935793 >>> df.reindex(new_index, fill_value="missing")
5794-
5794+ http_status response_time
57955795 Safari 404 0.07
57965796 Iceweasel missing missing
57975797 Comodo Dragon missing missing
@@ -5801,7 +5801,7 @@ def reindex(
58015801 We can also reindex the columns.
58025802
58035803 >>> df.reindex(columns=["http_status", "user_agent"])
5804-
5804+ http_status user_agent
58055805 Firefox 200 NaN
58065806 Chrome 200 NaN
58075807 Safari 404 NaN
@@ -5811,7 +5811,7 @@ def reindex(
58115811 Or we can use "axis-style" keyword arguments
58125812
58135813 >>> df.reindex(["http_status", "user_agent"], axis="columns")
5814-
5814+ http_status user_agent
58155815 Firefox 200 NaN
58165816 Chrome 200 NaN
58175817 Safari 404 NaN