PYTHON PANDAS.pptx

PYTHON PANDAS.pptx

• 1.
  PYTHON PANDAS
• 2.
  PANDAS STANDS FOR “PYTHONDATA ANALYSIS LIBRARY”.
• 3.
  Pandas is ahigh-level data manipulation tool developed by Wes Mckinney. It is built on the numpy package and its key data structure is called the dataframe. Dataframes allow you to store and manipulate tabular data in rows of observations and columns of variables.
• 4.
  Using pandas, wecan accomplish five typical steps in the processing and analysis of data , regardless of the origin of data – LOAD PREPARE MANIPULATE MODEL ANALYZE
• 5.
  Python with pandasis used in a wide range of fields including academic and commercial domains including finance, economics, statistics, analytics, etc.
• 6.
  Key features ofpandas Fast and efficient dataframe object with default and customized indexing. Tools for loading data into in-memory data objects from different file formats. Data alignment and integrated handling of missing data. Label-based slicing, indexing and subsetting of large data sets. Columns from a data structure can be deleted or inserted. Group by data for aggregation and transformations. High performance merging and joining of data.
• 7.
  What’s cool aboutpandas is that it takes data (like a CSV or TSV file, or a SQL database) and creates a python object with rows and columns called data frame that looks very similar to table in a statistical software (think excel or SPSS for example. This is so much easier to work with in comparison to working with lists and/or dictionaries through for loops or list comprehension
• 8.
  INSTALLATION AND GETTINGSTARTED In order to “get” Pandas you would need to install it. You would also need to have Python 2.7 and above as a pre-requirement for installation. It is also dependent on other libraries (like Numpy) and has optional dependancies (like Matplotlib for plotting). Therefore, I think that the easiest way to get Pandas set up is to install it through a package like the Anaconda distribution , “a cross platform distribution for data analysis and scientific computing.” There you can download the Windows, OS X and Linux versions.
• 9.
  In order touse pandas in your python IDE (integrated development environment) like jupyter notebook or spyder , you need to import the pandas library first. Importing a library means loading it into the memory and then it’s there for you to work with. In order to import pandas all you have to do is run the following code: import pandas as pd import numpy as np
• 10.
  Usually you wouldadd the second part (‘as pd’) so you can access pandas with ‘pd.Command’ instead of needing to write ‘pandas.Command’ every time you need to use it. Also, you would import numpy as well, because it is very useful library for scientific computing with python. Now pandas is ready for use! Remember, you would need to do it every time you start a new jupyter notebook, spyder file etc.
• 11.
  import pandas aspd data = [ [ ‘Alex’,10], [‘Bob’,12],[‘Clarke’,13] ] df = pd.DataFrame (data, columns = [‘Name’ , ‘Age ‘]) print ( df )
• 12.
  OUTPUT NAME AGE 0 ALEX10 1 BOB 12 2 CLARKE 13
• 13.
  # Create aDataFrame from Dictionary import pandas as pd data = { ‘Name ‘ : [ ‘Tom’,’Jack’,’Steve’,’Ricky’], ‘Age’ : [28,34,29,42] } df = pd.DataFrame (data) print ( df )
• 14.
  OUTPUT AGE NAME 0 28TOM 1 34 JACK 2 29 STEVE 3 42 RICKY
• 15.
  # Create anindexed DataFrame import pandas as pd data = { ‘Name ‘ : [ ‘Tom’,’Jack’,’Steve’,’Ricky’], ‘Age’ : [28,34,29,42] } df = pd.DataFrame (data, index = [‘rank 1’, ‘rank 2’, ‘rank 3’, ‘rank 4’ ]) print ( df )
• 16.
  OUTPUT AGE NAME RANK 128 TOM RANK 2 34 JACK RANK 3 29 STEVE RANK 4 42 RICKY
• 17.
  # Create aDataFrame from List of Dictionaries import pandas as pd data = [ { ‘a’ :1 , ‘b’ : 2 } , { ‘a’ : 5 , ‘b’ : 10 , ‘c’ : 20 } ] df = pd.DataFrame (data) print ( df )
• 18.
  OUTPUT a b c 12 NaN 5 10 20
• 19.
  It is alsopossible to get statistics on the entire data frame or a series (a column etc):  df.mean() Returns the mean of all columns  df.corr() Returns the correlation between columns in a data frame  df.count() Returns the number of non-null values in each data frame column  df.max() Returns the highest value in each column  df.min() Returns the lowest value in each column  df.median() Returns the median of each column  df.std() Returns the standard deviation of each column
• 20.
  THANKS