<jupyter_start><jupyter_text>A short example of how to balance an unbalanced panel data using numpy.Numpy is important for this because it is very fast. It took less than a few seconds Here's a sample data you can save as a csv file named unbalanced_panel_sample.csv to run the examples below:```client_id,month1,2020-011,2020-022,2020-012,2020-022,2020-033,2020-02```<jupyter_code>import pandas as pd
import numpy as np

# pd.options.display.float_format = '{:,.2f}'.format    # display format for dataframes
df = pd.read_csv('unbalanced_panel_sample.csv')
df
df['month'] = pd.to_datetime(df.month, format='%Y-%m')
df
unique_client_ids = df.client_id.unique()
unique_client_ids
unique_months = df.month.unique()
unique_months
arr = np.array(np.meshgrid(unique_client_ids, unique_months)).reshape(2, -1).T
df_bal = pd.DataFrame(arr, columns=df.columns)
df_bal = df_bal.sort_values(['client_id', 'month'])
df_bal['month'] = pd.to_datetime(df_bal['month'])
df_bal<jupyter_output><empty_output><jupyter_text>Benchmarking np.meshgridHere we benchmark meshgrid under the scenario where you have a million units that you'd like balanced across a hundred time periods. After applying np.meshgrid, the balanced dataset will have a billion records. This took around 30 seconds to complete. Doing this in pandas would be orders of magnitude longer.<jupyter_code>a = np.random.choice(range(10**7), 10**7, replace=False)
b = np.random.choice(range(100), 100, replace=False)
%%time
c = np.array(np.meshgrid(a, b)).reshape(2,-1).T
c.shape<jupyter_output>CPU times: user 19 s, sys: 1min 11s, total: 1min 30s
Wall time: 28 s
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