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# load data import pandas as pddf_old1 = pd.read_excel (r "D:\ Jupyter\ data\ Python.xlsx", sheet_name = "change one dimension") df_old1

# data cleaning, set the first column to index column df_old2 = pd.read_excel (r "D:\ Jupyter\ data\ Python.xlsx", sheet_name = "change one dimension", index_col = 0) df_old2

# reset index df_old3 = df_old2.reset_index () df_old3

Convert column names to column data

Here we need to use the .melt () function of pandas. Melt () is a reverse operation function that converts column names into column data (columns_name → column_values) and reconstructs DataFrame as follows:

The data3=data2.melt (id_vars='index', var_name='year',value_name='stu_num') data3 parameter explains: DataFrame: the dataset to be processed; id_vars: the column name that does not need to be converted; value_vars: the column name to be converted, if all the remaining columns need to be converted; var_name and value_name are the column names corresponding to the custom settings; col_level: use this level if the column is MultiIndex.

The results are as follows:

# convert 2D to 1D DF _ new = df_old3.melt (id_vars = "index", var_name = "year", value_name = "sales") df_new

The last step is in place:

# one step import pandas as pddf_melt = pd.read_excel (r "D:\ Jupyter\ data\ Python.xlsx", sheet_name = "change one dimension", index_col = 0) .reset_index () .melt (id_vars = "index", var_name = "year", value_name = "sales") .rename (columns = {"index": "province"}) df_melt

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