@@ -6324,7 +6324,7 @@ def fillna(
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... [3, 4, np.nan, 1],
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... [np.nan, np.nan, np.nan, 5],
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... [np.nan, 3, np.nan, 4]],
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- ... columns=list(' ABCD' ))
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+ ... columns=list(" ABCD" ))
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>>> df
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A B C D
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0 NaN 2.0 NaN 0
@@ -6343,7 +6343,7 @@ def fillna(
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We can also propagate non-null values forward or backward.
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- >>> df.fillna(method=' ffill' )
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+ >>> df.fillna(method=" ffill" )
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A B C D
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0 NaN 2.0 NaN 0
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1 3.0 4.0 NaN 1
@@ -6353,7 +6353,7 @@ def fillna(
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Replace all NaN elements in column 'A', 'B', 'C', and 'D', with 0, 1,
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2, and 3 respectively.
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- >>> values = {{'A' : 0, 'B' : 1, 'C' : 2, 'D' : 3}}
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+ >>> values = {{"A" : 0, "B" : 1, "C" : 2, "D" : 3}}
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>>> df.fillna(value=values)
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A B C D
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0 0.0 2.0 2.0 0
@@ -6369,6 +6369,17 @@ def fillna(
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1 3.0 4.0 NaN 1
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2 NaN 1.0 NaN 5
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3 NaN 3.0 NaN 4
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+
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+ When filling using a DataFrame, replacement happens along
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+ the same column names and same indices
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+
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+ >>> df2 = pd.DataFrame(np.zeros((4, 4)), columns=list("ABCE"))
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+ >>> df.fillna(df2)
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+ A B C D
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+ 0 0.0 2.0 0.0 0
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+ 1 3.0 4.0 0.0 1
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+ 2 0.0 0.0 0.0 5
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+ 3 0.0 3.0 0.0 4
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"""
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inplace = validate_bool_kwarg (inplace , "inplace" )
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value , method = validate_fillna_kwargs (value , method )
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