add a row at top in pandas dataframe [duplicate]

Below is my dataframe

import pandas as pd
df = pd.DataFrame({'name': ['jon','sam','jane','bob'],
           'age': [30,25,18,26],
           'sex':['male','male','female','male']})


   age  name     sex
0   30   jon    male
1   25   sam    male
2   18  jane  female
3   26   bob    male

I want to insert a new row at the first position

name: dean, age: 45, sex: male

   age  name     sex
0   45  dean    male
1   30   jon    male
2   25   sam    male
3   18  jane  female
4   26   bob    male

What is the best way to do this in pandas?

Solution 1:

Probably this is not the most efficient way but:

df.loc[-1] = ['45', 'Dean', 'male']  # adding a row
df.index = df.index + 1  # shifting index
df.sort_index(inplace=True) 

Output:

 age  name     sex
0  45  Dean    male
1  30   jon    male
2  25   sam    male
3  18  jane  female
4  26   bob    male

Solution 2:

If it's going to be a frequent operation, then it makes sense (in terms of performance) to gather the data into a list first and then use pd.concat([], ignore_index=True) (similar to @Serenity's solution):

Demo:

data = []

# always inserting new rows at the first position - last row will be always on top    
data.insert(0, {'name': 'dean', 'age': 45, 'sex': 'male'})
data.insert(0, {'name': 'joe', 'age': 33, 'sex': 'male'})
#...

pd.concat([pd.DataFrame(data), df], ignore_index=True)

In [56]: pd.concat([pd.DataFrame(data), df], ignore_index=True)
Out[56]:
   age  name     sex
0   33   joe    male
1   45  dean    male
2   30   jon    male
3   25   sam    male
4   18  jane  female
5   26   bob    male

PS I wouldn't call .append(), pd.concat(), .sort_index() too frequently (for each single row) as it's pretty expensive. So the idea is to do it in chunks...