I have a pandas dataframe with multiple columns and I would like to create a new dataframe by flattening all columns into one using the melt function. But I do not want the column names from the original dataframe to be a part of the new dataframe.
Below is the sample dataframe and code. Is there a way to make it more concise?
date Col1 Col2 Col3 Col4
1990-01-02 12:00:00 24 24 24.8 24.8
1990-01-02 01:00:00 59 58 60 60.3
1990-01-02 02:00:00 43.7 43.9 48 49
The output desired:
Rates
0 24
1 59
2 43.7
3 24
4 58
5 43.9
6 24.8
7 60
8 48
9 24.8
10 60.3
11 49
Code :
df = df.melt(var_name='ColumnNames', value_name='Rates') #using melt function to flatten columns
df_main.drop(['ColumnNames'], axis = 1, inplace = True) # dropping 'ColumnNames'
Set value_name and value_vars params for your purpose:
In [137]: pd.melt(df, value_name='Price', value_vars=df.columns[1:]).drop('variable', axis=1)
Out[137]:
Price
0 24.0
1 59.0
2 43.7
3 24.0
4 58.0
5 43.9
6 24.8
7 60.0
8 48.0
9 24.8
10 60.3
11 49.0