How can I use pandas create empty dataframe and fill it row by row for time series calculations?

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I’ve worked a lot with time-indexed data, and when it comes to time series, pre-structuring your DataFrame makes life easier and keeps performance steady. I usually go with this setup when I want to pandas create empty dataframe and fill it row by row:

import pandas as pd

dates = pd.date_range(start="2024-01-01", periods=10)
df = pd.DataFrame(index=dates, columns=["A", "B", "C"])
df = df.fillna(0)  # or use np.nan if you prefer

for i in range(1, len(df)):
    today = df.index[i]
    yesterday = df.index[i - 1]
    df.loc[today, 'A'] = df.loc[yesterday, 'A'] + 1

This avoids the cost of dynamically growing the DataFrame inside a loop—something pandas doesn’t handle very efficiently. Pre-indexing keeps your temporal logic tight and predictable.