Update README.md

Author: krishcy25

README.md

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-# Python-BasicOperations-and-DataAnalysis
+# Python-BasicOperations-and-DataAnalysis
+
+##This repository contains two Notebooks
+
+# Notebook 1: "Python-BasicOperations.ipynb"- Contains Basic Operations with Python Dataframes. This notebook contains the below topics
+
+## Topic 1: Basic Dataframe Reading/Operations
+###### Code Block 1.1: Reading the dataframe
+###### Code Block 1.2: Getting to know the shape of the dataset (Rows and Columns)
+###### Code Block 1.3: Length of dataframe.
+###### Code Block 1.4: Getting to know the data type of the dataset
+###### Code Block 1.5: Extracting one column from the dataframe and getting to know the data type and the size (of Series)
+###### Code Block 1.6: Printing the size of the series
+###### Code Block 1.7: Data types for whole dataframe (variables)
+###### Code Block 1.8: Working on creating specific indexes
+###### Code Block 1.9: Printing the first 5 rows of the dataframe
+###### Code Block 1.10: Printing the last 5 rows of the dataframe
+###### Code Block 1.11: Displaying the information of the dataframe
+###### Code Block 1.12: Extracting all rows from the dataframe with only one column
+###### Code Block 1.13: Understanding difference between Series and Dataframe
+###### Code Block 1.14: Extracting range of columns. For example all columns from country to right end
+###### Code Block 1.15: Selection Based on single index column
+###### Code Block 1.16: Selection Based on multiple index columns values
+###### Code Block 1.17: Selection Based on multiple index columns values
+
+## Topic 2: Conversion of operations/code from SQL to Python
+###### Code Block 2.1: SQL (where clause with single condition)-->Python Code
+###### Code Block 2.2: SQL (where clause with multiple conditions)-->Python Code
+###### Code Block 2.3: SQL (where clause with multiple conditions using NOT IN)-->Python Code
+###### Code Block 2.4: SQL (where clause with order by on single variable)-->Python Code
+###### Code Block 2.5: SQL (where clause with order by on multiple variables)-->Python Code
+
+## Topic 3: Data Exploration
+###### Code Block 3.1:Checking on various statistics for categorical variables with 1 variable (Series)
+###### Code Block 3.2:Checking on various statistics for integer variables with 1 variable (Series)
+###### Code Block 3.3:Working on describe method for the whole dataframe which basically consists a mix of numbers and categorical variables
+###### Code Block 3.4: Data exploration methods for Series vs Dataframe
+###### Code Block 3.5:Checking median of all integer columns in the dataframe
+###### Code Block 3.6: Select distinct values of any column
+
+
+## Topic 4: Creating a new column, calculated columns, cleaning the column names, dropping rows/columns
+###### 4.1 Creating a new column with in dataframe
+###### 4.2 Printing all the columns from the dataset/Getting to know the column names
+###### 4.3 Cleaning the columns
+###### 4.4 Converting the data type of the column 
+###### 4.5 Renaming the column names
+###### 4.6 Counting the missing values for each column in the dataset
+###### 4.7 Dropping rows and columns that has missing values
+###### 4.8 Replacing the missing values with some value (provided with some conditional logic)
+###### 4.9 Map funciton
+###### 4.10 Writing the final dataset (cleaned) one into csv
+
+## Topic 5: Plotting
+###### 5.1 Plotting a horizontal bar/histogram
+
+# Notebook 2: "Python-Data Combining.ipynb"- Contains various techniques on combining/merging the dataframes. This notebook contains the below topics
+
+
+# Topic 1: Data Combine
+###### 1.1 Combining dataframes using concat function
+###### 1.2 Combining dataframes using concat function- with Ignore Index option
+###### 1.4 Combining dataframes using Merge function (Inner Join)
+###### 1.5 Combining dataframes using Merge function (left Join)
+###### 1.6 Combining dataframes using Merge function (right Join)
+###### 1.7 Combining dataframes using Merge function (outer Join)
+###### 1.8 Use of suffixes
+
+# Topic 2: Transforming Data with Pandas- Using map(), apply(), applymap(), apply(), melt()
+###### 2.1 Creation of new column based on cases (this is more like CASE statement in SAS/SQL)
+###### 2.2 Difference between apply() and map()
+###### 2.3 Use of applymap()
+###### 2.4 Using pd.melt(): unpivots a Dataframe from wide format to long format
+
+# Topic 3: Working with Strings in Pandas
+###### 3.1 Renaming one of the column
+###### 3.2 Commonly used String functions
+###### 3.3 Calculating the lenth of a string for one column and store it in a different column
+###### 3.4 Creating a new calculated column by converting the string stored in one column to upper case letters
+###### 3.5 Pattern Searching- Using contains
+###### 3.6 extract() and extractall()
+
+# Topic 4: Working with Missing and Duplicate Data
+###### 4.1 Identifying missing values
+###### 4.2 Indentifying the duplicate values
+###### 4.3 Dropping the duplicates
+###### 4.4 Imputation of missing values with mean or any fixed value- Using fillna()
+###### 4.5 Dropping rows/columns which contains missing values