What is data Wrangling?
Data Wrangling is the process of converting data from the initial format to a format that may be readable and better for analysis.
Here we use the below data set :
Import pandas
Open Jupyter notebook or any online jupyter notebook editor and import pandas-
import pandas as pd
import matplotlib.pylab as plt
Reading the data and add header
filename = "https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DA0101EN/auto.csv"
headers = ["symboling","normalized-losses","make","fuel-type","aspiration", "num-of-doors","body-style", "drive-wheels","engine-location","wheel-base",
"length","width","height","curb-weight","engine-type", "num-of-cylinders", "engine-size","fuel
-system","bore","stroke","compression-ratio","horsepower", "peak-rpm","city-mpg","highway-mpg","price"]
Read CSV
df = pd.read_csv(filename, names = headers)
Show data in tabular form
df.head()
Data display in tabular form and you will face some challenges like this-
identify missing data
deal with missing data
correct data format
Identify and handle missing values
Identify missing values
Convert "?" to NaN
Missing data comes with the question mark "?". We replace "?" with NaN (Not a Number)
Example:
import numpy as np
# replace "?" to NaN
df.replace("?", np.nan, inplace = True)
df.head(5)
It set NaN at first five index row where "?" is presented.
How to detect missing data:
There are two method used to detect missing data.
.isnull() - Return true at the place of missing data and other place return false.
.notnull() - Return true at the placed data and false at missing data place.
Example:
mis_value = df.isnull()
mis_value.head(5)
Count missing value -In columns
Using for loop:
Example:
Write this for loop and find result
for column in mis_value .columns.values.tolist():
print(column)
print (mis_value [column].value_counts())
print("")
How we will work with missing data
Drop data
drop the whole row- Let suppose any value is necessary like price but it is missing at any row then we remove whole row.
drop the whole column - let we suppose if price is missing at any column then it reason of delete whole column because price is necessary for data science to calculate price.
Replace data
replace it by mean
replace it by frequency - replace as per frequency for example- 84 % is good, and 16% bad, then 16% remove by good.
replace it based on other functions
Calculate the average of any column
Example
avg= df["column name"].astype("float").mean(axis=0)
print("Average of column name:", avg)
Replace "NaN" by mean value - of any column
Example
df["column_name"].replace(np.nan, avg, inplace=True)
Calculate the mean value - of any column
Example
avg=df['column_name'].astype('float').mean(axis=0)
print("Average of column_name:", avg)
Replace NaN by mean value
Example
df["column_name"].replace(np.nan, avg, inplace=True)
How count each column data separately
Use value_counts() function
Example:
df['column_name'].value_counts()
Output like this: let suppose column_name is qualification then count each qualification with name.
mca 78
bca 45
Calculate for us the most common (max) automatically
df['column_name'].value_counts().idxmax()
Output:
mca 78
Replace NaN by most frequent
Example
df["column_name"].replace(np.nan, "four", inplace=True)
All NaN replace by most frequent- by "four"
Drop whole row with NaN in "Column_name" column
Let suppose column_name is "price"
df.dropna(subset=["price"], axis=0, inplace=True)
# reset index, because we dropped two rows
df.reset_index(drop=True, inplace=True)
Correct data format
In Pandas, we use
.dtype() to check the data type
.astype() to change the data type
Show list of data type:
Use this syntax to list data type -
syntax:
df.dtypes
How to convert data type in proper format
There are different type of data format used -
Syntax:
df[["column1", "column2"]] = df[["column1", "column2"]].astype("float")
df[["column3"]] = df[["column3"]].astype("int")
df[["column4"]] = df[["column4"]].astype("float")
df[["column5"]] = df[["column5"]].astype("float")
Again check it by using following -
It show list so that you can verify that data type is change or not
Syntax:
df.dtypes
Data Standardization
What is Standardization?
Standardization is the process of transforming data into a common format which allows the researcher to make the meaningful comparison.
Example
Transform mpg to L/100km
The formula for unit conversion is
L/100km = 235 / mpg
First go through the data to verify it by using this syntax-
Syntax:
df.head()
Example:
Convert mpg to L/100km by mathematical operation
df['city-L/100km'] = 235/df["city-mpg"]
It add new column city-L/100km after change the value of column city-mpg
# check your transformed data
df.head()
Data Normalization
Why normalization?
Normalization is the process of transforming values of several variables into a similar range.
Example:
# replace (original value) by (original value)/(maximum value)
df['length'] = df['length']/df['length'].max()
df['width'] = df['width']/df['width'].max()
Binning
Why binning?
Binning is a process of transforming continuous numerical variables into discrete categorical 'bins', for grouped analysis.
Indicator variable (or dummy variable)
What is an indicator variable?
An indicator variable (or dummy variable) is a numerical variable used to label categories. They are called 'dummies' because the numbers themselves don't have inherent meaning.
Why we use indicator variables?
So we can use categorical variables for regression analysis in the later modules.
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