import pandas as pd
## Series: (1-dimensional)
series=pd.Series(["Jigyasu","Mayank","Pankaj","Suraj","Hardik","Saurabh"])
series
series_1=pd.Series(["Black","Pink","White","Red","Green","Blue"])
series_1
## DataFrame: (2-dimensional)
fav_color=pd.DataFrame({"Name":series,"Fav. Color":series_1})
fav_color
## Import data
Car_Data=pd.read_csv("car-sales.csv")
Car_Data
## Exporting Dataframe
Car_Data.to_csv("Exported_Car_Data",index=False)
Ex_Car_Data=pd.read_csv("Exported_Car_Data")
Ex_Car_Data
## Describing Data
# Attributes
Car_Data.dtypes
Car_Data.columns
Car_Column=Car_Data.columns
Car_Column
Car_Data.index
Car_Data.describe()
Car_Data.info()
Car_Data.sum()
Car_Data["Odometer (KM)"].mean()
Car_Data["Odometer (KM)"].sum()
len(Car_Data)
## Selection and viewing
Car_Data.head()
Car_Data.head(7)
Car_Data.tail()
Car_Data.tail(3)
Car_Data.loc[3]
iloc: actual index return
Car_Data.iloc[:3]
Car_Data.loc[:3]
Car_Data["Make"]
Car_Data.Make
Car_Data[Car_Data["Make"]=="Toyota"]
Car_Data[Car_Data["Odometer (KM)"]>=50000]
Car_Data
pd.crosstab(Car_Data["Make"],Car_Data["Doors"])
Car_Data["Odometer (KM)"].mean()
Car_Data["Odometer (KM)"].plot()
Car_Data["Odometer (KM)"].hist()
Car_Data["Price"].plot()
Car_Data
Car_Data["Price"]
Car_Data["Price"] = Car_Data["Price"].str.replace('[\$,]', '', regex=True).astype(float)
Car_Data
Car_Data["Price"].plot()
Car_Data["Price"].hist()
## Manipulating Data
Car_Data["Make"].str.lower()
Car_Data["Make"]=Car_Data["Make"].str.upper()
Car_Data
Car_Data["Colour"]=Car_Data["Colour"].str.lower()
Car_Data
Car_Data_Missing=pd.read_csv("car-sales-missing-data.csv")
Car_Data_Missing
Car_Data_Missing["Odometer"].mean()
Car_Data_Missing
Car_Data_Missing.dropna(inplace=True)
Car_Data_Missing
Car_Data
series=pd.Series([5,5,4,5,6,4,5,4])
series
Car_Data["Seats"]=series
Car_Data
Car_Data["Seats"].fillna(Car_Data["Seats"].mean(),inplace=True)
Car_Data
li=[4.2,5.6,4.9,7.5,6.4,8.2]
Car_Data["Milage"]=li
Car_Data
pd series need to have same arguments as data table but python list must have
li=[4.2,5.6,4.9,7.5,6.4,8.2,7.5,6.4,5.0,10.9]
Car_Data["Milage"]=li
Car_Data
Car_Data["Fuel Used"]=Car_Data["Odometer (KM)"]/Car_Data["Milage"]
Car_Data
Car_Data["No. of wheels"]=4
Car_Data
Car_Data["Pollution Reciept"]=True
Car_Data
Car_Data["Sample"]="Has to remove"
Car_Data
Car_Data.drop("Sample",axis=1,inplace=True)
Car_Data
Car_Data_Shuffled=Car_Data.sample(frac=1)
Car_Data_Shuffled
Insurance=pd.read_csv("Student insurance E-card details. (2).csv")
Insurance.head()
Insurance
Insurance_Random=Insurance.sample(frac=1)
Insurance_Random
Insurance_Random.to_csv("Insurance_Random")
Insurance.sample(frac=0.03)
Insurance_Random.reset_index()
Insurance_Random.reset_index(drop=True)
Car_Data
Car_Data["Odometer (KM)"]=Car_Data["Odometer (KM)"].apply(lambda x:x/1.6)
Car_Data
Jigyasu Spacewonderer 