PANDAS

• Pandas


  Python programming library used for working with data sets
• Pandas


  • Has functions for analyzing, cleaning, exploring, and manipulating data
• Panel Data


  Reference to the name "Pandas"
• Python Data Analysis


  Reference to the name "Pandas"
• Pandas was created by Wes McKinney


  2008
• Pandas


  • Allows us to analyze big data and make conclusions based on statistical theories
  • Can clean messy data sets, and make them readable and relevant
• Relevant data is very important in data science
• Pandas


  • Gives you answers about the data like: Is there a correlation between two or more columns? What is average value? Max value? Min value?
• Pip


  The most popular package manager for Python, developed in 2008, the standard tool for installing Python packages and their dependencies in a secure manner
• Alias


  Pandas is usually imported under the pd alias
• Series


  A Pandas Series is like a column in a table, a one-dimensional array holding data of any type
• Labels


  If nothing else is specified, the values are labeled with their index number. This label can be used to access a specified value
• Key/Value Objects as Series

  You can also use a key/value object, like a dictionary, when creating a Series
• DataFrames


  Data sets in Pandas are usually multi-dimensional tables, Series is like a column, a DataFrame is the whole table
• Locate Row


  Pandas use the loc attribute to return one or more specified row(s)
• Named Indexes


  With the index argument, you can name your own indexes
• Load Files Into a DataFrame

  If your data sets are stored in a file, Pandas can load them into a DataFrame
• tail()


  The tail() method returns a specified number of last rows. The tail() method returns the last 5 rows if a number is not specified
• head()


  The head() method returns a specified number of first rows
• max_rows


  The number of rows returned is defined in Pandas option settings. You can check your system's maximum rows with the pd.options.display.max_rows statement
• percentiles


  The describe() method returns description of the data in the DataFrame, including count, mean, std, min, 25%, 50%, 75%, max
• Null Values


  The info() method tells us how many Non-Null values there are present in each column
• Data Cleaning


  • Fixing bad data in your data set, such as empty cells, data in wrong format, wrong data, duplicates
• Remove Rows


  One way to deal with empty cells is to remove rows that contain empty cells
• fillna()


  The fillna() method allows us to replace empty cells with a value
• Replace Empty Values


  Replace empty cells with a new value instead of deleting entire rows
• Replace Only For Specified Columns


  Replace empty values for one column, specify the column name for the DataFrame
• Replace Using Mean, Median, or Mode

  Replace empty cells with the mean, median or mode value of the column
• Mean


  The average value (the sum of all values divided by number of values)
• Median


  The value in the middle, after you have sorted all values ascending
• Mode


  The value that appears most frequently
• Convert Into a Correct Format

  Fix cells with data of wrong format by converting all cells in the columns into the same format
• coerce


  Invalid parsing will be set as NaN
• ignore


  Invalid parsing will return the input