Dataframe: copy one row into another while keeping different dtypes of columns

Question

I have a dataframe with columns of different datatypes (floats and ints). Two rows are in the wrong order and I need to swap them, but copying a row onto another does not work.

import pandas as pd

df = pd.DataFrame([
        {"a":2.5, "b":10},
        {"a":2.7, "b":12},
        {"a":2.8, "b":16},
        {"a":3.1, "b":18}
    ])

This does copy the values, but afterwards all rows are of type 'float' (Series objects only have a single datatype.):

df.iloc[1] = df.iloc[2].copy()  # changes datatype if b to float

Copying the rows by using slices sets the whole row to NaN:

df.iloc[1:2] = df.iloc[2:3].copy()  # sets row 1 to NaN,NaN

a   b
0   2.5 10.0
1   NaN NaN
2   2.8 16.0
3   3.1 18.0

2 Questions:

• whats happening in the second case, where do the NaN s come from?
• how do I copy a row onto another row while keeping the datatypes?

Answer 1

whats happening in the second case, where do the NaNs come from?

Problem is different index values of sliced DataFrames, pandas cannot align rows, so NaNs are created:

print (df.iloc[1:2])
     a   b
1  2.7  12

print (df.iloc[2:3])
     a   b
2  2.8  16

how do I copy a row onto another row while keeping the datatypes?

One solution is create one row DataFrame and change index name for alignment:

df.iloc[[1]] = df.iloc[[2]].rename(index={2:1}).copy()

More general if need indexing index values:

df.iloc[[1]] = df.iloc[[2]].rename(index={df.index[2]: df.index[1]}).copy()
print (df)

0  2.5  10
1  2.8  16
2  2.8  16
3  3.1  18

Converting to numpy array is posible, but then dtypes are changes.