<jupyter_start><jupyter_text>read csv file and print first five samples<jupyter_code>dataset_path = 'https://datahub.io/machine-learning/iris/r/iris.csv'<jupyter_output><empty_output><jupyter_text>save dataframe as tsv file ( tab separated)<jupyter_code><jupyter_output><empty_output><jupyter_text>sort based on petalwidth and petallength<jupyter_code># sort by petalwidth and petal length<jupyter_output><empty_output><jupyter_text>select sepallength of first five samples<jupyter_code><jupyter_output><empty_output><jupyter_text>group by: get mean of each class using groupby function<jupyter_code><jupyter_output><empty_output><jupyter_text>save first ten rows to a csv file<jupyter_code><jupyter_output><empty_output><jupyter_text>describe overview of dataset main details using describe function<jupyter_code><jupyter_output><empty_output><jupyter_text>find memory usage of each column<jupyter_code><jupyter_output><empty_output><jupyter_text>convert type of sepalwidth to int<jupyter_code><jupyter_output><empty_output><jupyter_text>count values of petalwidth<jupyter_code><jupyter_output><empty_output><jupyter_text>merge two dataframes outer join<jupyter_code>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<jupyter_output><empty_output><jupyter_text>select columns 2 and 3 of row 100 to row 150<jupyter_code><jupyter_output><empty_output><jupyter_text>select petalwidth , sepallength where class is Iris-setosa<jupyter_code><jupyter_output><empty_output><jupyter_text>select all rows with petalwidth > 0.1 and sepallength < 5.0<jupyter_code><jupyter_output><empty_output>
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