Forcing pandas .iloc to return a single-row dataframe?

Question

For programming purpose, I want .iloc to consistently return a data frame, even when the resulting data frame has only one row. How to accomplish this?

Currently, .iloc returns a Series when the result only has one row. Example:

In [1]: df = pd.DataFrame({'a':[1,2], 'b':[3,4]})  In [2]: df Out[2]:    a  b 0  1  3 1  2  4  In [3]: type(df.iloc[0, :]) Out[3]: pandas.core.series.Series 

This behavior is poor for 2 reasons:

• Depending on the number of chosen rows, .iloc can either return a Series or a Data Frame, forcing me to manually check for this in my code

- .loc, on the other hand, always return a Data Frame, making pandas inconsistent within itself (wrong info, as pointed out in the comment)

For the R user, this can be accomplished with drop = FALSE, or by using tidyverse's tibble, which always return a data frame by default.

Answer 1

Use double brackets,

df.iloc[[0]] 

Output:

   a  b 0  1  3  print(type(df.iloc[[0]])  <class 'pandas.core.frame.DataFrame'> 

Short for df.iloc[[0],:]

Answer 2

Accessing row(s) by label: loc

# Setup df = pd.DataFrame({'X': [1, 2, 3], 'Y':[4, 5, 6]}, index=['a', 'b', 'c']) df             X  Y a  1  4 b  2  5 c  3  6 

To get a DataFrame instead of a Series, pass a list of indices of length 1,

df.loc[['a']] # Same as df.loc[['a'], :] # selects all columns     X  Y a  1  4 

To select multiple specific rows, use

df.loc[['a', 'c']]      X  Y a  1  4 c  3  6 

To select a contiguous range of rows, use

df.loc['b':'c']      X  Y b  2  5 c  3  6 

Access row(s) by position: iloc

Specify a list of indices of length 1,

i = 1 df.iloc[[i]]     X  Y b  2  5 

Or, specify a slice of length 1:

df.iloc[i:i+1]      X  Y b  2  5 

To select multiple rows or a contiguous slice you'd use a similar syntax as with loc.