Add empty row between every new market

I want to change .append to .concat.
I got the following error ValueError: Must pass 2-d input. shape=(1, 2, 2).
I looked already at Error "'DataFrame' object has no attribute 'append'" I tried but unfortunately I got the error above. How do I fix this issue?

I want to add an empty row between every new Market.

Dataframe

Market  Values
0      A       1
1      B       2
2      A       3
3      C       4
4      B       5

import pandas as pd

data = {
    'Market': ['A', 'B', 'A', 'C', 'B'],
    'Values': [1, 2, 3, 4, 5]
}
df_sorted = pd.DataFrame(data)
print(df_sorted)

markets = ['A', 'B', 'C']

appended_df = pd.DataFrame()

# Loop through markets and append rows
for market in markets:
    market_rows = df_sorted[df_sorted['Market'] == market]
    #appended_df = appended_df.append(market_rows, ignore_index=True) # <--- old
    df_sorted = pd.concat([df_sorted, pd.DataFrame([market_rows])], ignore_index=True)
    #appended_df = appended_df.append(pd.Series(), ignore_index=True) # <--- old
    df_sorted = pd.concat([df_sorted, pd.DataFrame([pd.Series()])], ignore_index=True)
print(appended_df)

[OUT] ValueError: Must pass 2-d input. shape=(1, 2, 2)

What I want

 Market  Values
0      A     1.0
1      A     3.0
2    NaN     NaN
3      B     2.0
4      B     5.0
5    NaN     NaN
6      C     4.0

>Solution :

Don’t concat in a loop, use groupby to efficiently split the groups (rather than repeated slicing), collect in a list and concat once after the loop. Use a nested loop to add the intermediate (and discard the last one before concat):

df_sorted = pd.DataFrame(data)

out = pd.concat([x for k, g in df_sorted.groupby('Market', sort=False)
                for x in [g, pd.DataFrame(index=[0])]][:-1],
               ignore_index=True
              )

Output:

  Market  Values
0      A     1.0
1      A     3.0
2    NaN     NaN
3      B     2.0
4      B     5.0
5    NaN     NaN
6      C     4.0