熊猫如何转置数据并添加列名

Question

In Pandas I'm transposing the data and want to name the column.

My current data is:

    alpha   bravo    charlie
0   public  private  public
1   prodA   prodB    prodB
2   100     200      300

After transposing and renaming the columns, the output is:

df.transpose()
df.columns = ["category", "product", "price"]

    category    product price
alpha   public  prodA   100
bravo   private prodB   200
charlie public  prodB   300

How can I have the expected output like:

company category product price
alpha   public   prodA   100
bravo   private  prodB   200
charlie public   prodB   300

Answer 1

Just setup the index first, then tanspose the dataframe

df.index = pd.Index(['category','product','price'],name='company')
df.T

company category product price
alpha     public   prodA   100
bravo    private   prodB   200
charlie   public   prodB   300

Answer 2

First set the index name via df.index.set_names , then apply reset_index :

df.index = df.index.set_names('company')
df.columns = ['category', 'product', 'price']
df = df.reset_index()

#    company category product price
# 0    alpha   public   prodA   100
# 1    bravo  private   prodB   200
# 2  charlie   public   prodB   300

Answer 3

You can us rename_axis and reset_index:

(
    df.T
    .set_axis(["category", "product", "price"], axis=1, inplace=False)
    .rename_axis('company',axis=0)
    .reset_index()
)

Out[124]: 
   company category product price
0    alpha   public   prodA   100
1    bravo  private   prodB   200
2  charlie   public   prodB   300

If you would like to keep company as index:

(
    df.T
    .set_axis(["category", "product", "price"], axis=1, inplace=False)
    .rename_axis('company',axis=0)
)
Out[125]: 
        category product price
company                       
alpha     public   prodA   100
bravo    private   prodB   200
charlie   public   prodB   300