Data Visualization_pandas in hadoop.pptx

Data Visualization_pandas in hadoop.pptx

1. Tutorial Content 1 Overview of Python Libraries for Data Scientists Reading Data; Selecting and Filtering the Data; Data manipulation, sorting, grouping, rearranging Plotting the data Descriptive statistics Inferential statistics

2. Python Libraries for Data Science Many popular Python toolboxes/libraries: NumPy SciPy Pandas SciKit -Learn Visualization libraries matplotlib Seaborn and many more … 2 All these libraries are installed on the SCC

3. Python Libraries for Data Science NumPy : introduces objects for multidimensional arrays and matrices, as well as functions that allow to easily perform advanced mathematical and statistical operations on those objects provides vectorization of mathematical operations on arrays and matrices which significantly improves the performance many other python libraries are built on NumPy 3 Link: http://www.numpy.org/

4. Python Libraries for Data Science SciPy : collection of algorithms for linear algebra, differential equations, numerical integration, optimization, statistics and more part of SciPy Stack built on NumPy 4 Link: https://www.scipy.org/scipylib/

5. Python Libraries for Data Science Pandas: adds data structures and tools designed to work with table-like data (similar to Series and Data Frames in R) provides tools for data manipulation: reshaping, merging, sorting, slicing, aggregation etc. allows handling missing data 5 Link: http://pandas.pydata.org/

6. Link: http://scikit-learn.org/ Python Libraries for Data Science SciKit -Learn: provides machine learning algorithms: classification, regression, clustering, model validation etc. built on NumPy , SciPy and matplotlib 6

7. matplotlib : python 2D plotting library which produces publication quality figures in a variety of hardcopy formats  a set of functionalities similar to those of MATLAB line plots, scatter plots, barcharts , histograms, pie charts etc. relatively low-level; some effort needed to create advanced visualization Link: https://matplotlib.org/ Python Libraries for Data Science 7

8. Seaborn : based on matplotlib   provides high level interface for drawing attractive statistical graphics Similar (in style) to the popular ggplot2 library in R Link: https://seaborn.pydata.org/ Python Libraries for Data Science 8

9. Start Jupyter nootebook # On the Shared Computing Cluster [scc1 ~] jupyter notebook 9

10. In [ ]: Loading Python Libraries 10 #Import Python Libraries import numpy as np import scipy as sp import pandas as pd import matplotlib as mpl import seaborn as sns Press Shift+Enter to execute the jupyter cell

11. In [ ]: Reading data using pandas 11 #Read csv file df = pd.read_csv ( "http://rcs.bu.edu/examples/python/ data_analysis /Salaries.csv" ) There is a number of pandas commands to read other data formats: pd.read_excel ('myfile. xlsx ', sheet_name ='Sheet1', index_col =None, na_values =['NA']) pd.read_stata (' myfile.dta ') pd.read_sas ('myfile.sas7bdat') pd.read_hdf ('myfile.h5','df') Note: The above command has many optional arguments to fine-tune the data import process.

12. In [3]: Exploring data frames 12 #List first 5 records df.head () Out[3]:

13. Hands-on exercises 13 Try to read the first 10, 20, 50 records; Can you guess how to view the last few records; Hint:

14. Data Frame data types Pandas Type Native Python Type Description object string The most general dtype. Will be assigned to your column if column has mixed types (numbers and strings). int64 int Numeric characters. 64 refers to the memory allocated to hold this character. float64 float Numeric characters with decimals. If a column contains numbers and NaNs(see below), pandas will default to float64, in case your missing value has a decimal. datetime64, timedelta[ns] N/A (but see the  datetime  module in Python’s standard library) Values meant to hold time data. Look into these for time series experiments. 14

15. In [4]: Data Frame data types 15 #Check a particular column type df [ 'salary' ] . dtype Out[4]: dtype ('int64') In [5]: #Check types for all the columns df.dtypes Out[4]: rank discipline phd service sex salary dtype : object object object int64 int64 object int64

16. Data Frames attributes 16 Python objects have attributes and methods . df.attribute description dtypes list the types of the columns columns list the column names axes list the row labels and column names ndim number of dimensions size number of elements shape return a tuple representing the dimensionality values numpy representation of the data

17. Hands-on exercises 17 Find how many records this data frame has; How many elements are there? What are the column names? What types of columns we have in this data frame?

18. Data Frames methods 18 df.method () description head( [n] ), tail( [n] ) first/last n rows describe() generate descriptive statistics (for numeric columns only) max(), min() return max/min values for all numeric columns mean(), median() return mean/median values for all numeric columns std () standard deviation sample([n]) returns a random sample of the data frame dropna () drop all the records with missing values Unlike attributes, python methods have parenthesis. All attributes and methods can be listed with a dir () function: dir ( df )

19. Hands-on exercises 19 Give the summary for the numeric columns in the dataset Calculate standard deviation for all numeric columns; What are the mean values of the first 50 records in the dataset? Hint: use head() method to subset the first 50 records and then calculate the mean

20. Selecting a column in a Data Frame Method 1: Subset the data frame using column name: df[‘gender'] Method 2 : Use the column name as an attribute: df.gender Note: there is an attribute rank for pandas data frames, so to select a column with a name "rank" we should use method 1. 20

21. Hands-on exercises 21 Calculate the basic statistics for the salary column; Find how many values in the salary column (use count method); Calculate the average salary;

22. Data Frames groupby method 22 Using "group by" method we can: Split the data into groups based on some criteria Calculate statistics (or apply a function) to each group Similar to dplyr () function in R In [ ]: #Group data using rank df_rank = df.groupby ( [ 'rank' ]) In [ ]: #Calculate mean value for each numeric column per each group df_rank.mean ()

23. Data Frames groupby method 23 Once groupby object is create we can calculate various statistics for each group: In [ ]: #Calculate mean salary for each professor rank: df.groupby ( 'rank' )[[ 'salary' ]].mean() Note: If single brackets are used to specify the column (e.g. salary), then the output is Pandas Series object. When double brackets are used the output is a Data Frame

24. Data Frames groupby method 24 groupby performance notes: - no grouping/splitting occurs until it's needed. Creating the groupby object only verifies that you have passed a valid mapping - by default the group keys are sorted during the groupby operation. You may want to pass sort=False for potential speedup: In [ ]: #Calculate mean salary for each professor rank: df.groupby ([ 'rank'] , sort= False )[[ 'salary' ]].mean()

25. Data Frame: filtering 25 To subset the data we can apply Boolean indexing. This indexing is commonly known as a filter. For example if we want to subset the rows in which the salary value is greater than $120K: In [ ]: #Calculate mean salary for each professor rank: df_sub = df [ df [ 'salary'] > 120000 ] In [ ]: #Select only those rows that contain female professors: df_f = df[ df[ ‘gender'] == 'Female' ] Any Boolean operator can be used to subset the data: > greater; >= greater or equal; < less; <= less or equal; == equal; != not equal;

26. Data Frames: Slicing 26 There are a number of ways to subset the Data Frame: one or more columns one or more rows a subset of rows and columns Rows and columns can be selected by their position or label

27. Data Frames: Slicing 27 When selecting one column, it is possible to use single set of brackets, but the resulting object will be a Series (not a DataFrame ): In [ ]: #Select column salary: df [ 'salary' ] When we need to select more than one column and/or make the output to be a DataFrame , we should use double brackets: In [ ]: #Select column salary: df [[ ' rank' , 'salary ' ]]

28. Data Frames: Selecting rows 28 If we need to select a range of rows, we can specify the range using ":" In [ ]: #Select rows by their position: df [ 10:20 ] Notice that the first row has a position 0, and the last value in the range is omitted: So for 0:10 range the first 10 rows are returned with the positions starting with 0 and ending with 9

29. Data Frames: method loc 29 If we need to select a range of rows, using their labels we can use method loc : In [ ]: #Select rows by their labels: df_sub.loc [ 10:20 , [ ' rank’,’gender','salary ' ]]

30. Data Frames: method iloc 30 If we need to select a range of rows and/or columns, using their positions we can use method iloc : In [ ]: #Select rows by their labels: df_sub.iloc [ 10:20 , [ 0, 3, 4, 5 ]]

31. Data Frames: method iloc (summary) 31 df.iloc [ ] # First row of a data frame df.iloc [ i ] #(i+1) th row df.iloc [ -1 ] # Last row df.iloc [:, ] # First column df.iloc [:, -1 ] # Last column df.iloc [ 0:7 ] #First 7 rows df.iloc [ :, 0:2 ] #First 2 columns df.iloc [ 1:3, 0:2 ] #Second through third rows and first 2 columns df.iloc [[ 0,5 ] , [ 1,3 ]] #1 st and 6 th rows and 2 nd and 4 th columns

32. Data Frames: Sorting 32 We can sort the data by a value in the column. By default the sorting will occur in ascending order and a new data frame is return. In [ ]: # Create a new data frame from the original sorted by the column Salary df_sorted = df .sort_values ( by = 'service' ) df_sorted.head () Out[ ]:

33. Data Frames: Sorting 33 We can sort the data using 2 or more columns: In [ ]: df_sorted = df .sort_values ( by =[ 'service' , 'salary '], ascending = [ True , False ]) df_sorted.head ( 10 ) Out[ ]:

34. Missing Values 34 Missing values are marked as NaN In [ ]: # Read a dataset with missing values flights = pd.read_csv ( "http://rcs.bu.edu/examples/python/ data_analysis /flights.csv" ) In [ ]: # Select the rows that have at least one missing value flights[ flights.isnull ().any(axis=1)].head() Out[ ]:

35. Missing Values 35 There are a number of methods to deal with missing values in the data frame: df.method () description dropna () Drop missing observations dropna (how='all') Drop observations where all cells is NA dropna (axis=1, how='all') Drop column if all the values are missing dropna (thresh = 5) Drop rows that contain less than 5 non-missing values fillna (0) Replace missing values with zeros isnull () returns True if the value is missing notnull () Returns True for non-missing values

36. Missing Values 36 When summing the data, missing values will be treated as zero If all values are missing, the sum will be equal to NaN cumsum () and cumprod () methods ignore missing values but preserve them in the resulting arrays Missing values in GroupBy method are excluded (just like in R) Many descriptive statistics methods have skipna option to control if missing data should be excluded . This value is set to True by default (unlike R)

37. Missing Values 37 How to overcome Missing data in our dataset? There are many ways to overcome the missing data, we will see methods, before that we will start with the scratch like importing the libraries, Dataset: https://github.com/JangirSumit/data_science/blob/master/18th%20May%20Assignments/case%20study%201/SalaryGender.csv at the beginning of every code, we need to import the libraries, import pandas as pd import numpy as npdataset = pd.read_csv ("SalaryGender.csv") print( dataset.head ())

38. Missing Values 38 How to overcome Missing data in our dataset? checking for the dimension of the dataset dataset.shape Checking for the missing values print( dataset.isnull ().sum())

39. Missing Values 39 How to overcome Missing data in our dataset? Drop it if it is not in use (mostly Rows) Excluding observations with missing data is the next most easy approach. However, you run the risk of missing some critical data points as a result. You may do this by using the Python pandas package’s dropna () function to remove all the columns with missing values. Rather than eliminating all missing values from all columns, utilize your domain knowledge or seek the help of a domain expert to selectively remove the rows/columns with missing values that aren’t relevant to the machine learning problem. Pros: after removing missed data, the model becomes robus Cons: Loss of data, which may be important too. If you have more missing data then efficiency won’t be good for modelling. #deleting rows - missed vales dataset.dropna ( inplace =True) print( dataset.isnull ().sum())

40. Missing Values 40 How to overcome Missing data in our dataset? Imputation by Median: Another technique of imputation that addresses the outlier problem in the previous method is to utilize median values. When sorted, it ignores the influence of outliers and updates the middle value that occurred in that column. Cons: Works only with numerical datasets and failed in covariance between the independent variables #Median - missed value dataset["Age"] = dataset["Age"].replace( np.NaN , dataset["Age"].median()) print(dataset["Age"][:10])

41. Missing Values 41 How to overcome Missing data in our dataset? Imputation by Most frequent values (mode): This method may be applied to categorical variables with a finite set of values. To impute, you can use the most common value. For example, whether the available alternatives are nominal category values such as True/False or conditions such as normal/abnormal. This is especially true for ordinal categorical factors such as educational attainment. Pre-primary, primary, secondary, high school, graduation, and so on are all examples of educational levels. Unfortunately, because this method ignores feature connections, there is a danger of data bias. If the category values aren’t balanced, you’re more likely to introduce bias into the data (class imbalance problem). Pros : Works with all formats of data. Cons: Covariance value cannot be predicted between independent features #Mode - missed value import statistics dataset["Age"] = dataset["Age"].replace( np.NaN , statistics.mode (dataset["Age"])) print(dataset["Age"][:10])

42. Aggregation Functions in Pandas 42 Aggregation - computing a summary statistic about each group, i.e. compute group sums or means compute group sizes/counts Common aggregation functions: min, max count, sum, prod mean, median, mode, mad std , var

43. Aggregation Functions in Pandas 43 agg () method are useful when multiple statistics are computed per column: In [ ]: flights[[ 'dep_delay' , ' arr_delay ' ]]. agg ([ ' min' , 'mean' , 'max ' ]) Out[ ]:

44. Basic Descriptive Statistics 44 df.method () description describe Basic statistics (count, mean, std , min, quantiles, max) min, max Minimum and maximum values mean, median, mode Arithmetic average, median and mode var , std Variance and standard deviation sem Standard error of mean skew Sample skewness kurt kurtosis

45. Handling Duplicate Values and Outliers in a dataset 45  While working on a real world dataset, we might come across very messy data which involves a lot of duplicate values. Such records do not add any value or information while using them in a model and would rather slow down the processing.  So, it is better to remove duplicates before feeding the data to the model. The following method can be used to check for duplicate values in pandas — To check for duplicates, we use the “duplicated” function in Pandas. If the df is the DataFrame , then df.duplicated () will check if the entire row has been repeated anywhere in the dataframe .

46. Handling Duplicate Values and Outliers in a dataset 46 # to check for duplicate values in a particular column df.duplicated ( 'column1' ) # to check for duplicate values in some specific columns df.duplicated ([ 'column1' , 'column2' , 'column3' )] # To check the number of duplicate values df.duplicated. sum () Once we have identified duplicates in the dataset, it is time to remove them. To delete duplicates, we use a function drop_duplicates in Pandas. # Dropping duplicates df.drop_duplicates () # to delete duplicates from a particular column df.drop_duplicates ( 'column1' ) # to delete duplicates from some specific columns df.drop_duplicates ([ 'column1' , 'column2' , 'column3' )]

47. Handling Duplicate Values and Outliers in a dataset 47 An argument “keep” can also be used with drop_duplicates . keep = ‘first’ keeps the first record and deletes the other duplicates, keep = ‘last’ keeps the last record and deletes the rest, and keep = False deletes all the records. Note: Also, do not forget to add the argument ‘ inplace ’ as True to save the changes made to the dataframe . Handling Outliers Outliers are values in a dataset that are atypical and different from the majority of the datapoints, but may or may not be false. Outliers may occur due to the variability of data or maybe due to machine or human errors. Detecting and treating outliers is very crucial in any machine learning project. However, it is not always required to delete or remove outliers. It depends on the problem statement and what are we trying to achieve from that model. For example, in problems related to anomaly detection, fraud detection, etc. outliers play a major role. It is basically the outliers that need to be tracked in such scenarios. Also, the type of algorithms used also decide to what extent outliers would affect the model. Weight based algorithms like linear regression, logistic regression, ADABoost and other deep learning techniques get affected by outliers a lot. Whereas tree based algos like decision tree, random forest etc. don’t get affected by outliers as much.

48. Handling Duplicate Values and Outliers in a dataset 48 Detecting outliers Outliers can be detected using the following methods - 1- Boxplots  — Creating a boxplot is a smart way to detect if the dataset has outliers. The following picture shows a boxplot – Here, it clearly shows that the data points lying outside the whiskers are outliers. The lower whisker is at  Q1–1.5*IQR   and the higher whisker is at  Q3 + 1.5*IQR , where Q1, Q3, and IQR are 1st quartile(25th percentile), 3rd quartile(75th percentile), and Interquartile Range (Q3-Q1) respectively. This is also known as  IQR Proximity Rule.

49. Handling Duplicate Values and Outliers in a dataset 49 Detecting outliers 2- Using Z-Scores — According to 66–95–99.7 rule, for a normally distributed data 99.7% of the data is within 3 standard deviations of the mean. So, if a point lies outside 3 standard deviations from the mean, it is considered as an outlier. For this we can calculate the z-scores of the data points and keep the threshold as 3. If the z-score of any point is greater than 3 or less than -3, it is an outlier. But this rule is only valid for normal distributions.

50. Graphics to explore the data 50 To show graphs within Python notebook include inline directive: In [ ]: % matplotlib inline Seaborn package is built on matplotlib but provides high level interface for drawing attractive statistical graphics, similar to ggplot2 library in R. It specifically targets statistical data visualization

51. Graphics 51 description distplot histogram barplot estimate of central tendency for a numeric variable violinplot  similar to boxplot, also shows the probability density of the data jointplot Scatterplot regplot Regression plot pairplot Pairplot boxplot boxplot swarmplot categorical scatterplot factorplot General categorical plot

52. Plotting with pandas : 52 Line Plots Bar Plots Histograms and Density Plots Scatter or Point Plots

53. Plotting with pandas 53 Pandas uses the  plot()  method to create diagrams. We can use Pyplot , a submodule of the Matplotlib library to visualize the diagram on the screen. Example: import  pandas as pd import   matplotlib.pyplot   as   plt df = pd.read_csv ( 'data.csv' ) df.plot () plt.show () The examples in this page uses a CSV file called: 'data.csv'. Download data.csv

54. Plotting with pandas 54 Scatter Plot- Specify that you want a scatter plot with the kind argument: kind = 'scatter' A scatter plot needs an x- and a y-axis. In the example below we will use "Duration" for the x-axis and "Calories" for the y-axis. Include the x and y arguments like this: x = 'Duration', y = 'Calories’ import  pandas as pd import   matplotlib.pyplot   as   plt df = pd.read_csv ( 'data.csv' ) df.plot (kind =  'scatter' , x =  'Duration' , y =  'Calories' ) plt.show ()

55. Plotting with pandas 55 Histogram Use the kind argument to specify that you want a histogram: kind = 'hist' A histogram needs only one column. A histogram shows us the frequency of each interval, e.g. how many workouts lasted between 50 and 60 minutes? In the example below we will use the "Duration" column to create the histogram: import pandas as pd import matplotlib.pyplot as plt df = pd.read_csv ('data.csv') df['Duration'].plot(kind='hist') plt.show ()

56. Plotting with pandas 56 Line Plots- shows the relationship between the variables, here we represent the relationship between the duration and calories import pandas as pd import matplotlib.pyplot as plt df = pd.read_csv ('data.csv’) Df.plot () plt.show ()

57. Plotting with pandas 57 #bar plot import pandas as pd import matplotlib.pyplot as plt df = pd.read_csv ('data.csv') df.plot (kind='bar')