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Python for Data
Analysis
DataMites
Part 5
Accredited by IABAC™

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Filtering for Data Frame
DATA SCIENCE FOUNDATION 2
The filter called ‘Boolean’ cam be used for subsetting the data.
Any of the Boolean operator can prove useful to subset the data
> greater; > greater or equal;
< less ; <= less or equal
==equal !=not equal

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Data Frame:- Slicing
Ways to subset the Data Frame
➔ One or more rows
➔ One or more columns
➔ Subset of rows and columns
The position or label of the rows and columns can be used to select
them.
DATA SCIENCE FOUNDATION 3

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A single set of brackets can be used to select a column.
However, the resulting object will be a series.
For selecting more than one column and/or making data
frame for the output, we must use double brackets
# Select column salary
df [ [ ‘rank’, ‘salary’] ]
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Pandas Aggregation Functions
DATA SCIENCE FOUNDATION 5
Aggregation: It refers to the computation of statistics summary
for each group
Compute mean or group sums
Compute group counts/sizes
Commonly used aggregation functions
Max, min
Count, sum, prod
Mean, mode, median, mad, std var

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Graphics for Data Exploration
➔ Seaborn packages provides interface which is high level.
➔ It helps in drawing statistical graphics, which is similar to
ggplot library in R.
➔ It helps in statistical data visualisation
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Graphics
Barplot- Finds the central tendency of a variable.
Violinplot- It shows the density of probability of data
Distplot- Histogram
Jointplot - Scatterplot
Pairplot - Pairplot
Boxplot - boxplot
Swarmplot - Categorical scatterplot
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