Reshape long Stata-style structure to wide column structure

long-to-wide.py

import pandas as pd

# Load the data from the output CSV file
df = pd.read_csv("output.csv")

# Pivot the data frame to wide format for the min variable
min_wide = df.pivot_table(index=["lon", "lat", "orig_id", "year", "month"], columns="variable", values="value")

# Reset the index to obtain a flat data frame
min_wide = min_wide.reset_index()

# Save the resulting data frame to a CSV file
min_wide.to_csv("min.csv", index=False)

# Pivot the data frame to wide format for the mean variable
mean_wide = df.pivot_table(index=["lon", "lat", "orig_id", "year", "month"], columns="variable", values="value")

# Reset the index to obtain a flat data frame
mean_wide = mean_wide.reset_index()

# Save the resulting data frame to a CSV file
mean_wide.to_csv("mean.csv", index=False)

# Pivot the data frame to wide format for the max variable
max_wide = df.pivot_table(index=["lon", "lat", "orig_id", "year", "month"], columns="variable", values="value")

# Reset the index to obtain a flat data frame
max_wide = max_wide.reset_index()

# Save the resulting data frame to a CSV file
max_wide.to_csv("max.csv", index=False)

"""
For standard wide structure all in single csv not separate as above output, use below code.
import pandas as pd

# Load the data from the output CSV file
df = pd.read_csv("output.csv")

# Pivot the data frame to wide format
df_wide = df.pivot_table(index=["lon", "lat", "orig_id", "year", "month"], columns="variable", values="value")

# Reset the index to obtain a flat data frame
df_wide = df_wide.reset_index()

# Save the resulting data frame to a CSV file
df_wide.to_csv("output_wide.csv", index=False)

"""