Pandas for data exploration

Using Pandas for data exploration

Pandas is a python library that is used for data manipulation and analysis.In data science the key applications of pandas are

• Reading and writing data 
• Handling of missing data
• Reshaping and pivoting of data
• Label based slicing, indexing and sub-setting of large data sets
• Column insertion, deletion and filtering
• Merging and joining

Below are some important syntax that are used in analysis

#Syntax for importing pandas
Import pandas as pd

#Reading a csv file, after writing pd.read_csv, one can use Shift + Tab in jupyter notebook to explore the individual component in  the function

data = pd.read_csv(“data.csv”)

type(data) # To check the data type 

len(data) #To check the length of data 

data.shape # To check the dimension (rows, column)

data.head() # To check the top 5 rows

pd.set_option("display.max.columns", None) # To see all columns

data.tail() # To check the bottom 5 rows

data.info() # To provide information on non null count and data type for a column

data.describe() # To describe the data in terms of count, mean , std,min, 25%,50%,75%, max

data.describe(include=np.object)   # To explore object variables

data["column"].value_counts() # To explore categorical variables

data.loc[data["column1"] == "ABC", "column2"].value_counts() # To explore a column with some condition

data.loc[data["column"] == “A", "date_column"].min() 

data.loc[data["column"] == "A", "date_column"].max()

data.loc[data["column"] == "A", "date_column"].agg(("min", "max"))