Pandas

Pandas

• 1.
  Pandas •Open-source Python Library •Datamanipulation and analysis tool using its powerful data structures. •Wes McKinney -2008 •Pandas will extract the data from that CSV into : •DataFrame •Table •Perform following statistical operations on it like : •What's the average, median, max, or min of each column? •Does column A correlate with column B? •What does the distribution of data in column C look like? •Clean the data by doing things like removing missing values and filtering rows or columns by some criteria •Visualize the data with help from Matplotlib. Plot bars, lines, histograms, bubbles, and more. •Store the cleaned, transformed data back into a CSV, other file or database
• 2.
  Pandas • From commandprompt •pip install pandas • From Jupyter notebook • !pip install pandas’ •Then in notebook to use pandas..need to create instance of it • import pandas as pd
• 3.
  Pandas •Pandas deals withthe following three data structures − •Series •DataFrame •Panel Data Structure Description Series 1D labeled homogeneous array, size immutable. Data Frames 2D - labeled, size-mutable tabular structure with potentially heterogeneously typed columns. Panel 3D labeled, size-mutable array.
• 4.
  Pandas Series Homogeneous data Size Immutable Valuesof Data Mutable DataFrame is a two-dimensional array with heterogeneous data. For example, Name Age Gender Rating Steve 32 Male 3.45 Lia 28 Female 2.40 The data is represented in rows and columns. Each column represents an attribute and each row represents a person. Data Type of Columns The data types of the four columns are as follows − Column Type Name String Age Integer Gender String Rating Float
• 5.
  Pandas Data Frame : •KeyPoints •Heterogeneous data •Size Mutable •Data Mutable •Panel Panel is a three-dimensional data structure with heterogeneous data. It is hard to represent the panel in graphical representation. But a panel can be illustrated as a container of DataFrame. Key Points Heterogeneous data Size Mutable Data Mutable
• 6.
  Pandas •Series is aone-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.). •The axis labels are collectively called index. A pandas Series can be created using the following constructor − pandas.Series( data, index, dtype, copy) The parameters of the constructor are as follows − Sr.No Parameter & Description 1 Data : data takes various forms like ndarray, list, constants 2 Index : Index values must be unique and hashable, same length as data. Default np.arrange(n) if no index is passed. 3 Dtype : dtype is for data type. If None, data type will be inferred 4 Copy : Copy data. Default False
• 7.
  Pandas A series canbe created using various inputs like − Array Dict Scalar value or constant Create a Series from ndarray If data is an ndarray, then index passed must be of the same length. If no index is passed, then by default index will be range(n) where n is array length, i.e., [0,1,2,3…. range(len(array))-1].
• 8.
  Pandas •A Data frameis a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns. •Features of DataFrame •Potentially columns are of different types •Size – Mutable •Labeled axes (rows and columns) •Can Perform Arithmetic operations on rows and columns
• 9.
  Pandas •pandas.DataFrame •A pandas DataFramecan be created using the following constructor − •pandas.DataFrame( data, index, columns, dtype, copy) •Create DataFrame •A pandas DataFrame can be created using various inputs like − •Lists •dict •Series •Numpy ndarrays •Another DataFrame