pandas drop rows with condition
import pandas as pd
a = ['2015-01-01' , '2015-02-01']
df = pd.DataFrame(data={'date':['2015-01-01' , '2015-02-01', '2015-03-01' , '2015-04-01', '2015-05-01' , '2015-06-01']})
print(df)
# date
#0 2015-01-01
#1 2015-02-01
#2 2015-03-01
#3 2015-04-01
#4 2015-05-01
#5 2015-06-01
df = df[~df['date'].isin(a)]
print(df)
# date
#2 2015-03-01
#3 2015-04-01
#4 2015-05-01
#5 2015-06-01
# importing pandas as pd
import pandas as pd
# Read the csv file and construct the
# dataframe
df = pd.read_csv('nba.csv')
# First filter out those rows which
# does not contain any data
df = df.dropna(how = 'all')
# Filter all rows for which the player's
# age is greater than or equal to 25
df.drop(df[df['Age'] < 25].index, inplace = True)
# Print the modified dataframe
print(df.head(15))
# Print the shape of the dataframe
print(df.shape)
# import pandas library
import pandas as pd
# dictionary with list object in values
details = {
'Name' : ['Ankit', 'Aishwarya', 'Shaurya',
'Shivangi', 'Priya', 'Swapnil'],
'Age' : [23, 21, 22, 21, 24, 25],
'University' : ['BHU', 'JNU', 'DU', 'BHU',
'Geu', 'Geu'],
}
# creating a Dataframe object
df = pd.DataFrame(details, columns = ['Name', 'Age',
'University'],
index = ['a', 'b', 'c', 'd', 'e', 'f'])
# get names of indexes for which
# column Age has value >= 21
# and column University is BHU
index_names = df[ (df['Age'] >= 21) & (df['University'] == 'BHU')].index
# drop these given row
# indexes from dataFrame
df.drop(index_names, inplace = True)
df