Basic and tricky Pandas functions

Main preprocessing techniques for pandas package

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import pandas as pd
import numpy as np

read csv file and print first five samples

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dataset_path = 'https://datahub.io/machine-learning/iris/r/iris.csv'

save dataframe as tsv file ( tab separated)

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sort based on petalwidth and petallength

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# sort by petalwidth and petal length

select sepallength of first five samples

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group by: get mean of each class using groupby function

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save first ten rows to a csv file

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describe overview of dataset main details using describe function

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find memory usage of each column

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convert type of sepalwidth to int

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count values of petalwidth

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merge two dataframes outer join

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import random
data1 = [[i ,random.randint(0,20)] for i in range(100)]
data2 = [[random.randint(0,20) , i] for i in range(40,140)]

dataframe1 = pd.DataFrame(data1 , columns=['id1','code1'])
dataframe2 = pd.DataFrame(data2 , columns=['code2','id2'])
# your code here

select columns 2 and 3 of row 100 to row 150

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select petalwidth , sepallength where class is Iris-setosa

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select all rows with petalwidth > 0.1 and sepallength < 5.0

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A solution
Main preprocessing techniques for pandas package

Please see this after you have done it yourself.
Authors
Amin Rezaee
Author