Formatting specific rows in a Jupyter Notebook dataframe output - python-3.x

Question

I'm presenting a data frame in Jupyter Notebook. The initial data type of the data frame is float. I want to present rows 1 & 3 of the printed table as integers and rows 2 & 4 as percentage. How do I do that? (I've spent numerous hours looking for a solution with no success)
Here's the code I'm using:
#Creating the table
clms = sales.columns
indx = ['# of Poeple','% of Poeple','# Purchased per Activity','% Purchased per Activity']
basic_stats = pd.DataFrame(data=np.nan,index=indx,columns=clms)
basic_stats.head()
#Calculating the # of people who took part in each activity
for clm in sales.columns:
basic_stats.iloc[0][clm] = int(round(sales[sales[clm]>0][clm].count(),0))
#Calculating the % of people who took part in each activity from the total email list
for clm in sales.columns:
basic_stats.iloc[1][clm] = round((basic_stats.iloc[0][clm] / sales['Sales'].count())*100,2)
#Calculating the # of people who took part in each activity AND that bought the product
for clm in sales.columns:
basic_stats.iloc[2][clm] = int(round(sales[(sales[clm] >0) & (sales['Sales']>0)][clm].count()))
#Calculating the % of people who took part in each activity AND that bought the product
for clm in sales.columns:
basic_stats.iloc[3][clm] = round((basic_stats.iloc[2][clm] / basic_stats.iloc[0][clm])*100,2)
#Present the table
basic_stats
Here's the printed table:
Output table of 'basic_stats' data frame in Jupyter Notebook

Answer 1

Integer representation
You already assign integers to the cells of row 1 and 3 are. The reason why these integers are printed as floats is that all columns have the data type float64. This is caused by the way you initially create the Dataframe. You can view the data types by printing the .dtypes attribute:
basic_stats = pd.DataFrame(data=np.nan,index=indx,columns=clms)
print(basic_stats.dtypes)
# Prints:
# column1 float64
# column2 float64
# ...
# dtype: object
If you don't provide the data keyword argument in the constructor of the Data
frame, the data type of each cell will be object which can be any object:
basic_stats = pd.DataFrame(index=indx,columns=clms)
print(basic_stats.dtypes)
# Prints:
# column1 object
# column2 object
# ...
# dtype: object
When the data type of a cell is object, the content is formatted using it's builtin methods which leads to integers bein formatted properly.
Percentage representation
In order to display percentages, you can use a custom class that prints a float number the way you want:
class PercentRepr(object):
"""Represents a floating point number as percent"""
def __init__(self, float_value):
self.value = float_value
def __str__(self):
return "{:.2f}%".format(self.value*100)
Then just use this class for the values of row 1 and 3:
#Creating the table
clms = sales.columns
indx = ['# of Poeple','% of Poeple','# Purchased per Activity','% Purchased per Activity']
basic_stats = pd.DataFrame(index=indx,columns=clms)
basic_stats.head()
#Calculating the # of people who took part in each activity
for clm in sales.columns:
basic_stats.iloc[0][clm] = int(round(sales[sales[clm]>0][clm].count(),0))
#Calculating the % of people who took part in each activity from the total email list
for clm in sales.columns:
basic_stats.iloc[1][clm] = PercentRepr(basic_stats.iloc[0][clm] / sales['Sales'].count())
#Calculating the # of people who took part in each activity AND that bought the product
for clm in sales.columns:
basic_stats.iloc[2][clm] = int(round(sales[(sales[clm] >0) & (sales['Sales']>0)][clm].count()))
#Calculating the % of people who took part in each activity AND that bought the product
for clm in sales.columns:
basic_stats.iloc[3][clm] = PercentRepr(basic_stats.iloc[2][clm] / basic_stats.iloc[0][clm])
#Present the table
basic_stats
Note: This actually changes the data in your dataframe! If you want to do further processing with the data of rows 1 and 3, you should be aware that these rows don't contain float objects anymore.

Answer 2

Here's one way, kind of a hack, but if its simply for pretty printing, it'll work.
df = pd.DataFrame(np.random.random(20).reshape(4,5))
# first and third rows display as integers
df.loc[0,] = df.loc[0,]*100
df.loc[2,] = df.loc[2,]*100
df.loc[0,:] = df.loc[0,:].astype(int).astype(str)
df.loc[2,:] = df.loc[2,:].astype(int).astype(str)
# second and fourth rows display as percents (with 2 decimals)
df.loc[1,:] = np.round(df.loc[1,:].values.astype(float),4).astype(float)*100
df.loc[3,:] = np.round(df.loc[3,:].values.astype(float),4).astype(float)*100