Python Program for Column to Row Transpose using Pandas

Given an Input File, having columns Dept and Name, perform an operation to convert the column values to rows. Name contains pipe separated values that belong to a particular department identified by the column Dept.

Attached Dataset: emp_data

Examples:

Input:
dept, name
10, Vivek|John
20, Ritika|Shubham|Nitin
30, Vishakha|Ankit

Output:
dept, name
10, Vivek
10, John
20, Ritika
20, Shubham
20, Nitin
30, Vishakha
30, Ankit

Method 1: Pythonic Way

Python

# Reading Data From the text
# file
data = pd.read_csv(r'GFG.txt')


# create new data frame with
# split value columns separates
# data into three columns as per
# separator mentioned
new = data["name"].str.split("|",expand = True)

# making separate first name column
# from new data frame assign columnn
# values to dataframe new columns
# named as name*
data["Name1"] = new[0]
data["Name2"] = new[1]
data["Name3"] = new[2]

# Dropping old Name columns
data.drop(columns =["name"], inplace = True)

# create separate dataframes with two
# columns id,name
d_name1 = data[['dept','Name1']]
d_name2 = data[['dept','Name2']]
d_name3 = data[['dept','Name3']]

# perform concat/unions operation for
# vertical merging of dataframes
union_df=pd.concat([d_name1,d_name2,d_name3],ignore_index=True)
union_df.fillna('',inplace=True)

# concatenate values of series into one
# series "name"
union_df['name'] = union_df['Name1'].astype(str)+union_df['Name2'].astype(str)+union_df['Name3'].astype(str)

# drop column names
union_df.drop(['Name1','Name2','Name3'],axis=1,inplace=True)

# drop rows having empty values
final_df=union_df[union_df['name']!='']

# sort the dataframe data by dept values
final_df.sort_values('dept')

Output:

Note: Shortcoming of above method is when there are more than 3 names separated by |

Method 2: Exploring Pandas

Python

emp_df = pd.read_csv(r'GFG.txt')

# split column data on basis of separator
# convert it into list using to_list
# stack method performs transpose operation
# to the data
emp_df1 = pd.DataFrame(emp_df.name.str.split('|').to_list(),
                       index = emp_df.dept).stack()

emp_df1 = emp_df1.reset_index([0, 'dept'])
emp_df1.columns =['Dept', 'Name']

emp_df1

Output:

Method 3: The Pandas way: explode()

Python

df = pd.read_csv(r'GFG.txt')

# separate values using split()
# transpose is performed by explode
# function explode function overcomes
# the method1 shortcomings incase we
# have many columns we explode will do
# the task in no time and with no hassle
df1 = df.assign(name = df['name'].str.split('|')).explode('name')
df1

Output: