Pandas Dataframe - Adding new row based on variables

Question

I have a program that sorts through a large series of data and pulls a bunch of necessary values and saves them as numbers or strings.

How do I then place those values into a new dataframe?

For instance:

labels = ['A', 'B', 'C', 'D']
df = pd.DataFrame(columns=labels)

Now I want to add variables x, y, Z and the text "banana" to the DataFrame into A, B, C and D respectively.

Is there a line of code using an append-like method or something similar that will do this quickly and efficiently?

(My actual dataframe will have a multitude of columns, so a quick script is much more useful than creating an empty dataframe and changing each cell value.)

sacuL · Accepted Answer · 2018-03-06 18:28:33Z

3

This should do it for you, if I understand correctly:

df.loc[len(df)] = ['x', 'y', 'Z', 'banana']

>>> df
   A  B  C       D
0  x  y  Z  banana

Or, more conveniently, as a function:

def insert_row(df, my_row):
    df.loc[len(df)] = my_row

insert_row(df, ['x', 'y', 'Z', 'banana'])

answered Mar 6, 2018 at 18:28

sacuL

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2 Comments

Densoto Over a year ago

I believe this is exactly what I am looking for as it will add the next row to the database. If it's empty - it will add row zero, and if it has 6 current entries, it will add the 7th to row 6. Thanks. Also - if I have variables, X and Y as strings, I just place df.loc[len(df)0 = [X,Y, 'Z', 'banana']?

sacuL Over a year ago

That shouldn't be a problem: X = 'string1', Y = 'string2', df.loc[len(df)] = [X, Y, 'Z', 'banana'] works fine

rmarren · Accepted Answer · 2018-03-06 18:32:08Z

0

In [1]: import pandas as pd

In [2]: df = pd.DataFrame(columns=["A", "B", "C", "D"])

In [3]: df.loc[0] = ["x", "y", "Z", "banana"]

In [4]: df
Out[4]: 
   A  B  C       D
0  x  y  Z  banana

answered Mar 6, 2018 at 18:32

rmarren

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