Reorder Pandas Columns

Question

How can I reorder using pandas, 40,41,42,43,44 next to 2 ??
Is there any fastest and simple way to do it?
I dont want using type pd["sku_id","primary_category_code","primary_category_1","primary_category_2"...] to reorder...
take so much effort to do that.

For example:

     #   Column                     Non-Null Count  Dtype  
---  ------                     --------------  -----  
 0   sku_id                     274 non-null    object 
 1   primary_category_code      274 non-null    object 
 2   primary_category_name_chi  274 non-null    object 
 3   cat_codes                  274 non-null    object 
 4   cat_names_chi              274 non-null    object 
 5   name_chi                   274 non-null    object 
 6   brand_name_chi             274 non-null    object 
 7   summary_chi                274 non-null    object 
 8   description_en             86 non-null     object 
 9   description_chi            154 non-null    object 
 10  image_urls                 274 non-null    object 
 11  creation_time              274 non-null    object 
 12  store_code                 274 non-null    object 
 13  original_price             274 non-null    float64
 14  discount_price             271 non-null    float64
 15  stock_available            274 non-null    int64  
 16  out_of_stock_since         2 non-null      object 
 17  max_order_quantity         0 non-null      float64
 18  total_shipped_quantity     232 non-null    float64
 19  manu_country_chi           274 non-null    object 
 20  height                     274 non-null    float64
 21  length                     274 non-null    float64
 22  width                      274 non-null    float64
 23  dimension_unit             274 non-null    object 
 24  weight                     274 non-null    float64
 25  weight_unit                274 non-null    object 
 26  colors                     140 non-null    object 
 27  delivery_mode              274 non-null    object 
 28  pickup_days                274 non-null    object 
 29  num_days_to_be_ready       274 non-null    int64  
 30  online_date                0 non-null      float64
 31  offline_date               155 non-null    object 
 32  warranty_period            273 non-null    float64
 33  warranty_period_unit       270 non-null    object 
 34  warranty_supplier_en       270 non-null    object 
 35  warranty_supplier_chi      270 non-null    object 
 36  virtual_store_code         0 non-null      float64
 37  virtual_store_name_en      0 non-null      float64
 38  virtual_store_name_chi     0 non-null      float64
 39  primary_store              274 non-null    object 
 40  primary_category_1         274 non-null    object 
 41  primary_category_2         274 non-null    object 
 42  primary_category_3         274 non-null    object 
 43  primary_category_4         274 non-null    object 
 44  primary_category_5         274 non-null    object

cs95 · Accepted Answer · 2022-02-10 04:55:55Z

4

Given some DataFrame that looks like this:

df = pd.DataFrame(columns=range(1, 11))
df

Empty DataFrame
Columns: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
Index: []

You can reorder the DataFrame's columns by index using iloc. I use np.r_ to make selection easy:

df.iloc[:, np.r_[0:2, 7:10, 2:8]]

Empty DataFrame
Columns: [1, 2, 8, 9, 10, 3, 4, 5, 6, 7, 8]
Index: []

answered Feb 10, 2022 at 4:55

cs95

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3 Comments

John Lee Over a year ago

Actually, my columns are in wording not in numbers. Still can use it?

Olsgaard Over a year ago

@JohnLee yes, .iloc looks at the numerical index of the columns. e.g., df.iloc[:, [2,3]] looks at the 3rd and 4th column, regardless of the column name (columns are counted 0, 1,2,3...).

cs95 Over a year ago

@JohnLee yes, this solution is agnostic of column names as it is positional/index based solution. Thanks!

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