pygaurav

import pandas as pd

column_names = ["sepal length","sepal width","petal length","petal width","Type of flower"]
df = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data",
                 names=column_names)

df[(df['sepal length']>6) & (df['petal length']>5 )]

pygaurav

import pandas as pd

column_names = ["sepal length","sepal width","petal length","petal width","Type of flower"]
df = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data",
                 names=column_names,index_col="sepal length")
#Lets see the dataframe with index as sepal length
print(df.head())
#Lets verify indexes
print(df.index)
#This will return all rows with index -> sepal length 5.0

pygaurav

import pandas as pd

column_names = ["sepal length","sepal width","petal length","petal width","Type of flower"]
df = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data",
                 names=column_names)

#This will return all rows with indexes 2-4
print(df.iloc[2:5,:].head())

pygaurav

import pandas as pd

column_names = ["sepal length","sepal width","petal length","petal width","Type of flower"]
df = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data",
                 names=column_names)

#Creating the selected column list
select_column_names=["sepal width","petal length"]

#Printing selected columns by loc way

pygaurav

import pandas as pd

column_names = ["sepal length","sepal width","petal length","petal width","Type of flower"]
df = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data",
                 names=column_names)
print("The shape of dataframe is {0}".format(df.shape))
print("The dimension of dataframe is {0}".format(df.ndim))
print("The size of dataframe is {0}".format(df.size))
print("The dtype of data is {0}".format(df.dtypes))
print("The values of data is {0}".format(df.values))

pygaurav

import pandas as pd

column_names = ["sepal length","sepal width","petal length","petal width","Type of flower"]
df = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data",
                 names=column_names)
df.shape

pygaurav

import pandas as pd

column_names = ["sepal length","sepal width","petal length","petal width","Type of flower"]
df = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data",
                 names=column_names)
df.describe()

pygaurav

import pandas as pd

column_names = ["sepal length","sepal width","petal length","petal width","Type of flower"]
df = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data",
                 names=column_names)
df.head()

pygaurav

import pandas as pd

list_of_numbers1 = [100,200,300,400,500]
index_number1 = [1,2,3,4,5]

list_of_numbers2 = [200,300,400,500,600]
index_number2 = [10,20,30,40,50]

series1 = pd.Series(data=list_of_numbers1,index=index_number1)
series2 = pd.Series(data=list_of_numbers2,index=index_number2)

pygaurav

import pandas as pd

list_of_numbers1 = [100,200,300,400,500]
index_number1 = [1,2,3,4,5]

list_of_numbers2 = [200,300,400,500,600]
index_number2 = [10,20,30,40,50]

series1 = pd.Series(data=list_of_numbers1,index=index_number1)
series2 = pd.Series(data=list_of_numbers2,index=index_number2)