# importing pandas as pd
import pandas as pd
  
# Making data frame from the csv file
df = pd.read_csv("nba.csv")
  
# Printing the first 10 rows of 
# the data frame for visualization
  
df[:10]

# drop all those rows which 
# have any 'nan' value in it.
df.dropna(inplace = True)

# let's find out the data type of Weight column
before = type(df.Weight[0])
  
# Now we will convert it into 'int64' type.
df.Weight = df.Weight.astype('int64')
  
# let's find out the data type after casting
after = type(df.Weight[0])
  
# print the value of before
before
  
# print the value of after
after

# print the data frame and see
# what it looks like after the change
df

# importing pandas as pd
import pandas as pd
  
# Making data frame from the csv file
df = pd.read_csv("nba.csv")
  
# Drop the rows with 'nan' values
df = df.dropna()
  
# print the existing data type of each column
df.info()

# Passed a dictionary to astype() function 
df = df.astype({"Name":'category', "Age":'int64'})
  
# Now print the data type 
# of all columns after change
df.info()

# print the data frame
# too after the change
df