How to utilise Pandas dataframe & series for data wrangling?

This recipe helps you utilise Pandas dataframe & series for data wrangling

Recipe Objective

There are various data wrangling methods. Have you tried to use any of them for dataframe or series?

So this is the recipe on how we can utilise a Pandas dataframe & series for data wrangling.

Step 1 - Importing Library

import pandas as pd

We have only imported pandas which is needed.

Step 2 - Creating a series

We have created a series of numbers in the boject floodingReports and then added index to each number. floodingReports = pd.Series([5, 6, 2, 9, 12]) print(floodingReports) floodingReports = pd.Series([5, 6, 2, 9, 12], index=["Cochise County", "Pima County", "Santa Cruz County", "Maricopa County", "Yuma County"]) print(floodingReports)

Step 3 - Data Wrangling on series

First we have printed the number as per the index. Then we have printed the index on a condition that the value should be greater than 6. print(floodingReports["Cochise County"]) print(floodingReports[floodingReports > 6])

Step 4 - Creating a series from dictionary

We have created a series from a dictionary by passing the dictionary through pd.series. fireReports_dict = {"Cochise County": 12, "Pima County": 342, "Santa Cruz County": 13, "Maricopa County": 42, "Yuma County" : 52} fireReports = pd.Series(fireReports_dict) print(fireReports)

Step 5 - Changing the index of series

We can change the index of series by defining new set of index in series.index function. fireReports.index = ["Cochice", "Pima", "Santa Cruz", "Maricopa", "Yuma"]

Step 6 - Creating a dataframe from dictionary

We have created a dataframe from a dictionary by passing the dictionary through pd.DataFrame data = {"county": ["Cochice", "Pima", "Santa Cruz", "Maricopa", "Yuma"], "year": [2012, 2012, 2013, 2014, 2014], "reports": [4, 24, 31, 2, 3]} df = pd.DataFrame(data) print(df)

Step 7 - Performing Wrangling on dataframe

We are peroforming three Wrangling for better understanding.

  • Adding a new Column
  • dfColumnOrdered["newsCoverage"] = pd.Series([42.3, 92.1, 12.2, 39.3, 30.2]) print(dfColumnOrdered)
  • Deleting a column
  • del dfColumnOrdered["newsCoverage"] print(dfColumnOrdered)
  • Making Transpose
  • # Transpose the dataframe print(dfColumnOrdered.T)
So the output comes as:

0     5
1     6
2     2
3     9
4    12
dtype: int64

Cochise County        5
Pima County           6
Santa Cruz County     2
Maricopa County       9
Yuma County          12
dtype: int64

5

Maricopa County     9
Yuma County        12
dtype: int64

Cochise County        12
Pima County          342
Santa Cruz County     13
Maricopa County       42
Yuma County           52
dtype: int64

       county  year  reports
0     Cochice  2012        4
1        Pima  2012       24
2  Santa Cruz  2013       31
3    Maricopa  2014        2
4        Yuma  2014        3

       county  year  reports  newsCoverage
0     Cochice  2012        4          42.3
1        Pima  2012       24          92.1
2  Santa Cruz  2013       31          12.2
3    Maricopa  2014        2          39.3
4        Yuma  2014        3          30.2

       county  year  reports
0     Cochice  2012        4
1        Pima  2012       24
2  Santa Cruz  2013       31
3    Maricopa  2014        2
4        Yuma  2014        3

               0     1           2         3     4
county   Cochice  Pima  Santa Cruz  Maricopa  Yuma
year        2012  2012        2013      2014  2014
reports        4    24          31         2     3

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