Best way to move a column in pandas dataframe to last column in large dataframe

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I have a pandas dataframe with more than 100 columns. For example in the following df:


How can I move date to be the last column? assuming the dataframe is large and i cant write all the column names manually.

You can try this:

new_cols = [col for col in df.columns if col != 'date'] + ['date']
df = df[new_cols]

Test data:

cols = ['A','B','C','D','E','date','G','H','F','I']
df = pd.DataFrame([np.arange(len(cols))],

#    A  B  C  D  E  date  G  H  F  I
# 0  0  1  2  3  4     5  6  7  8  9

Output of the code:

   A  B  C  D  E  G  H  F  I  date
0  0  1  2  3  4  6  7  8  9     5

How to change the order of DataFrame columns?, How can I move the column mean to the front, i.e. set it as first column leaving the order of the other columns One easy way would be to reassign the dataframe with a list of the columns, rearranged as needed. This is how I moved the last element to the first position: import numpy as np import pandas as pd df = pd. The solution that reorders columns posted most often is to set the DataFrame equal to itself but with the columns explicitly named in a new desired order: # Reset DataFrame with columns in desired order df = df[['names','Score','Room','Grade','Teacher','Hobby']]

Use pandas.DataFrame.pop and pandas.concat:

   col1  col2  col3
0     1    11   111
1     2    22   222
2     3    33   333

s = df.pop('col1')
new_df = pd.concat([df, s], 1)


   col2  col3  col1
0    11   111     1
1    22   222     2
2    33   333     3

Reordering Pandas DataFrame Columns: Thumbs Down On , Reordering Pandas DataFrame Columns: Thumbs Down On Standard Solutions similar standard methods for moving columns around (changing their order) in a Following the example data, the article conveys two good but not perfect Last, there is a revised solution that avoids some of the limitations  Change the order of columns in Pandas dataframe. import modules. import pandas as pd import numpy as np. create dummy dataframe. raw_data =

This way :


shifting the last column in the dataframe to the first place, cols = list(df.columns) cols = [cols[-1]] + cols[:-1] df = df[cols]. Pandas DataFrame consists of rows and columns so, in order to iterate over dataframe, we have to iterate a dataframe like a dictionary. In a dictionary, we iterate over the keys of the object in the same way we have to iterate in dataframe.

Simple reindexing should do the job:

original = df.columns
new_cols = original.delete(original.get_loc('date'))

shifting specific column to before/after specific column in dataframe , If you have a large number of columns, the problem will arise in how you get the list of columns that are wanted to move or shift if the column are multiples and  Pandas has two ways to rename their Dataframe columns, first using the df.rename() function and second by using df.columns, which is the list representation of all the columns in dataframe. Let’s Start with a simple example of renaming the columns and then we will check the re-ordering and other actions we can perform using these functions

You can use reindex and union:

df.reindex(df.columns[df.columns != 'date'].union(['date']), axis=1) 

Let's only work with the index headers and not the complete dataframe.

Then, use reindex to reorder the columns.

Output using @QuangHoang setup:

   A  B  C  D  E  F  G  H  I  date
0  0  1  2  3  4  8  6  7  9     5

How to change the order of DataFrame columns in pandas, You can rearrange a DataFrame object by declaring a list of columns and using it as a key. [code]import How can I speed up a function that iterates over a large pandas dataframe? How do you move columns in a dataframe in Pandas? Most of the answers did not generalize enough and pandas reindex_axis method is a little tedious, hence I offer a simple function to move an arbitrary number of columns to any position using a dictionary where key = column name and value = position to move to. If your dataframe is large pass True to 'big_data' then the function will return the ordered columns list.

pandas.DataFrame.reindex, DataFrame. reindex (self, labels=None, index=None, columns=None, axis=None, method=None, copy=True, level=None, fill_value=nan, Places NA/NaN in locations having no value in the previous index. Method to use for filling holes in reindexed DataFrame. Remove row labels or move them to new columns. Note also you misunderstand what tail does, it returns the last n rows (default is 5) of a dataframe. Additional. If the columns are varying length then you can just the header to get the columns and then read the csv again properly and drop the last column:

Indexing and selecting data, In this section / chapter, we will focus on the final point: namely, how to slice, dice, Since indexing with [] must handle a lot of cases (single-label access, slicing, boolean You may access a column on a dataframe directly as an attribute: of inputs, it can offer performance that is a good deal faster than fancy indexing. The columns can also be renamed by directly assigning a list containing the new names to the columns attribute of the dataframe object for which we want to rename the columns. The disadvantage with this method is that we need to provide new names for all the columns even if want to rename only some of the columns.

How To Move a Column to First Position in Pandas DataFrame , Let us get a look at the top 5 rows of the dataframe with Pandas head() function. 1​. 2. 3. 4. 5. 6. 7. Pandas DataFrame.transpose () function transpose index and columns of the dataframe. It reflect the DataFrame over its main diagonal by writing rows as columns and vice-versa. Syntax: DataFrame.transpose (*args, **kwargs)