pandas add a column with only one row

append column to dataframe pandas
append list to dataframe pandas
pandas insert single row
pandas add value to column
how to add rows and columns in python
add row to pandas series
pandas append
pd dataframe add one row

This sounds a bit weird, but I think that's exactly what I needed now:

I got several pandas dataframes that contains columns with float numbers, for example:

   a  b  c
0  0  1  2
1  3  4  5
2  6  7  8

Now I want to add a column, with only one row, and the value is equal to the average of column 'a', in this case, is 3.0. So the new dataframe will looks like this:

   a  b  c  average
0  0  1  2  3.0
1  3  4  5
2  6  7  8

And all the rows below are empty.

I've tried things like df['average'] = np.mean(df['a']) but that give me a whole column of 3.0. Any help will be appreciated.

Can do something like:

df['average'] = [np.mean(df['a'])]+['']*(len(df)-1)

Python| Pandas dataframe.append(), append list to dataframe pandas pandas add column insert list into dataframe pandas how to add one row to a dataframe add empty column to dataframe  Pandas : count rows in a dataframe | all or those only that satisfy a condition Pandas : Loop or Iterate over all or certain columns of a dataframe Pandas : 4 Ways to check if a DataFrame is empty in Python

Here is a full example:

import pandas as pd
import numpy as np

df = pd.DataFrame(
    [(0,1,2), (3,4,5), (6,7,8)],
    columns=['a', 'b', 'c'])
print(df)

   a  b  c
0  0  1  2
1  3  4  5
2  6  7  8

df['average'] = ''
df['average'][0] = df['a'].mean()
print(df)

   a  b  c average
0  0  1  2       3
1  3  4  5        
2  6  7  8  

Python Pandas : How to add new columns in a dataFrame using [] or , Access a single value for a row/column label pair. Similar to loc , in that both provide label-based lookups. Use at if you only need to get or set a single value in a  Apply a lambda function to all the columns in dataframe using Dataframe.apply () and inside this lambda function check if column name is ‘z’ then square all the values in it i.e. # Apply function numpy.square () to square the value one column only i.e. with column name 'z' modDfObj = dfObj.apply (lambda x: np.square (x) if x.name == 'z

Assign a series, this is cleaner.

df['average'] = pd.Series(df['a'].mean(), index=df.index[[0]])

Or, even better, assign with loc:

df.loc[df.index[0], 'average'] = df['a'].mean().item()

Filling NaNs is straightforward, you can do

df['average'] = df['average'].fillna('')
df
   a  b  c average
0  0  1  2       3
1  3  4  5        
2  6  7  8        

Append rows of other to the end of caller, returning a new object. DataFrame([[​5, 6], [7, 8]], columns=list('AB')) >>> df.append(df2) A B 0 1 2 1 3 4 0 5 6 1 7 8. Dealing with Rows and Columns in Pandas DataFrame A Data frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns. We can perform basic operations on rows/columns like selecting, deleting, adding, and renaming.

axis{0 or 'index', 1 or 'columns'}, default 0. Take difference over rows (0) or columns (1). Returns. DataFrame. See also. Series.diff. First discrete difference for a  That would only columns 2005, 2008, and 2009 with all their rows. Extracting specific rows of a pandas dataframe ¶ df2[1:3] That would return the row with index 1, and 2. The row with index 3 is not included in the extract because that’s how the slicing syntax works. Note also that row with index 1 is the second row.

How do I add a row to a DataFrame in pandas? Answers: You can also build up a list of lists and convert it to a dataframe –. import pandas as pd rows = [] columns = ['i','double','square'] for i in range (6): row = [i, i*2, i*i] rows.append (row) df = pd.DataFrame (rows, columns=columns) giving.

are added as new columns and the new cells are populated with NaN value. ignore_index : If True, do not use the index labels. Pandas .join(): Combining Data on a Column or Index. While merge() is a module function, .join() is an object function that lives on your DataFrame. This enables you to specify only one DataFrame, which will join the DataFrame you call .join() on.

Comments
  • you cant bro. this is a dataframe. what you can do, is to set up all the values behind the first row as NaNs
  • @ℕʘʘḆḽḘ Can do the fake way to add empty strings
  • great Minimal, Complete, and Verifiable example.
  • What I want for empty is just like something does not print out, as you did