How to Convert a Datetime Index into a Regular Column in a Pandas DataFrame?

Adding New Time Column to DataFrame

In order to plot your data, you need to transform the index column, 'YYYY-MO-DD HH-MI-SS_SSS', into a regular column. To achieve this, you can utilize the 'reset_index' function to convert the index to a new column.

df3 = df3.reset_index()

This will create a new column named 'index' in your DataFrame df3.

Alternatively, you can use the 'copy and assignment' method to create a new column based on the existing index:

df3['Time'] = df3.index

This will add a new column named 'Time' that contains the values from the index.

Optimized Code

Here's an optimized version of your code that addresses the issue:

# Import CSV file
df = pd.read_csv('university2.csv', sep=";", skiprows=1, index_col='YYYY-MO-DD HH-MI-SS_SSS', parse_dates='YYYY-MO-DD HH-MI-SS_SSS')

# Extract interesting columns
df2 = df[[ 'ATMOSPHERIC PRESSURE (hPa)', 'TEMPERATURE (C)', 'magnetic_mag']].copy()

# Resample and aggregate interesting columns
df3 = df2.resample('H').agg(['mean','std'])
df3.columns = [' '.join(col) for col in df3.columns]

# Reset index to create Time column
df3.reset_index(inplace=True)

# Plot the data
plt.plot(df3['magnetic_mag mean'], df3['Time'], label='FDI')  

This optimized code uses 'read_csv' with proper options to set the index column and parse dates correctly. Additionally, it leverages 'inplace' functionality to avoid creating unnecessary copies of data.

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