Transposing Data Frames

Pandas: After using transpose old index name does not vanish when using dataFrame.index.set_names

First question. Why is country now the index for 1800, 1801 etc. and is there a better tranpose option to avoid this?

'country' is the name/ label of the column axis since it was the name of the index of the DataFrame before transposing.

df_2.index.set_names(["year"],inplace=True)

Question two. Why is country still there and how to remove it?

Because you are only changing the index name of the transposed DataFrame. The name of the column axis ('country') is left unchanged.

How to remove it:

You can use DataFrame.rename_axis to change the name of both axes (index and column) at the same time. If you want to remove the name of a given axis, just pass None.

For instance,

# or df_2 = df1.T.rename_axis(index='year', columns=None) if you prefer
>>> df_2 = df_1.rename_axis(index=None, columns='year').T
>>> df_2

      Germany
year         
1800     38.4
1801     38.4
1802     38.4

Transposing a data frame

data <- as.data.frame(t(data))

This ensures that the numeric values are not converted to string values.

Transpose dataframe at certain number of rows

You could use pivot to reshape. Then use rename_axis + reset_index to get it in the desired form:

out = df.pivot(['Col1', 'Col2'], 'Col3', 'Col4').add_prefix('Col4_').rename_axis(columns=[None]).reset_index()

Output:

  Col1 Col2     Col4_1     Col4_2    Col4_3
0  A01  CY5  -1.091990   7.402931  0.089915
1  A01  TEX -10.525215 -38.686164  1.337128
2  A02  CY5  -1.526202   2.229630 -0.604621
3  A06  CY5   8.216859   6.890880  0.466027

Transpose columns to rows in R dataframe

We may use pivot_longer

library(tidyr)
pivot_longer(df, cols = starts_with('cheese'), 
   names_to = c("product", ".value"), names_sep = "\\.", 
     values_transform = list(kg = as.integer))

-output

# A tibble: 6 × 4
  level1 column.other product    kg
  <chr>  <chr>        <chr>   <int>
1 A      yes          cheese1    58
2 A      yes          cheese2    11
3 B      yes          cheese1    63
4 B      yes          cheese2    22
5 C      yes          cheese1    33
6 C      yes          cheese2    20

Transpose columns pandas dataframe

use pd.melt to flatten the dataframe and then sort and remove the unwanted columns

df.drop(columns='ID').melt(id_vars='Name', value_name='Locator').sort_values('Name').drop(columns='variable')

                  Name  Locator
0   DASS ARGENTINA SRL  info@iricresanluis.com.ar
2   DASS ARGENTINA SRL  2664941642
4   DASS ARGENTINA SRL  2664941644
6   DASS ARGENTINA SRL  info@iricresanluis.com.ar
8   DASS ARGENTINA SRL  info_tec@iricresanluis.com.ar
10  DASS ARGENTINA SRL  115456789
12  DASS ARGENTINA SRL  contact_mail@gmail.com
1   PEPSI               mail_general@pepsi.com
3   PEPSI               456535365
5   PEPSI               7766554399
7   PEPSI               mail1@pepsi.com
9   PEPSI               mail2@pepsi.com
11  PEPSI               8864545332
13  PEPSI               last_mail@pepsi.com

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