How do I replace a slice of a dataframe column with values from another dataframe column slice?

Question

I have a two dataframes with several columns including a timestamp column. I would like to copy the first 1000 timestamps from the second dataframe to the first one.

df1 = pd.read_csv(file1.csv)
df2 = pd.read_csv(file2.csv)
df1.timestamp.iloc[:1000] = df2.timestamp.iloc[:1000]  

I tried various things like adding .copy() to the right hand side, using .loc[:1000, 'timestamp'] instead of the columnname.iloc syntax, converting the column series into a numpy array first, but I keep getting errors ranging from "too many indexers", to a directive to use .loc[rowindexing, columnindexing] (which doesn't fix the issue), and other error messages.

Answer 1

Given df1, df2:

df1 = pd.DataFrame({'timestamp': range(0,2000)})
df2 = -df1

using .loc:

df1.loc[:999,'timestamp'] = df2.loc[:999,'timestamp']
df1.loc[997:1002,'timestamp']

997     -997
998     -998
999     -999
1000    1000
1001    1001
1002    1002
Name: timestamp, dtype: int64

or using iloc (optionally converting loc -> iloc using get_loc)

df1.iloc[:1000,0] = df2.iloc[:1000,0]
df1.loc[997:1002,'timestamp']

997     -997
998     -998
999     -999
1000    1000
1001    1001
1002    1002
Name: timestamp, dtype: int64

note that the slicing behavior on iloc and loc is differrent.
.loc includes the right value, .iloc doesn't include it (like in range)