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

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)

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