Expanding Columns and Exploding Strings to Split Rows in Pandas
To split (explode) a Pandas dataframe string entry to separate rows, you can use either of the following approaches:
explode
method
Solution 1: Using the If you have Pandas version 0.25 or newer, you can use the explode
method to explode a column of lists (or in this case, a column of comma-separated strings) into separate rows. First, you can split the comma-separated string values into lists using the str.split
method. Next, you can use the assign
method to replace the original column with the new lists, and then use the explode
method to explode the column into separate rows.
import pandas as pd
df = pd.DataFrame({'var1': ['a,b,c', 'd,e,f'], 'var2': [1, 2]})
print(df)
# Output:
# var1 var2
# 0 a,b,c 1
# 1 d,e,f 2
b = df.assign(var1=df['var1'].str.split(',')).explode('var1')
print(b)
# Output:
# var1 var2
# 0 a 1
# 0 b 1
# 0 c 1
# 1 d 2
# 1 e 2
# 1 f 2
Note that the explode
method only works on a single column for now. If you want to explode multiple columns, you can use a lambda function with the apply
method, like this:
(df.set_index(['var3'])
.apply(lambda col: col.str.split(',').explode())
.reset_index()
.reindex(df.columns, axis=1))
str.split
and stack
methods
Solution 2: Using the In this approach, you can first split the comma-separated string values into a list using the str.split
method. Next, you can convert the list of lists into a stacked Pandas series using the stack
method. Finally, you can reset the index and rename the columns to get the desired output.
import pandas as pd
df = pd.DataFrame({'var1': ['a,b,c', 'd,e,f'], 'var2': [1, 2]})
print(df)
# Output:
# var1 var2
# 0 a,b,c 1
# 1 d,e,f 2
b = pd.DataFrame(df.var1.str.split(',').tolist(), index=df.var2).stack()
b = b.reset_index()[[0, 'var2']]
b.columns = ['var1', 'var2']
print(b)
# Output:
# var1 var2
# 0 a 1
# 1 b 1
# 2 c 1
# 3 d 2
# 4 e 2
# 5 f 2
Conclusion
In conclusion, when dealing with large datasets or messy data, it is essential to know how to manipulate and transform the data effectively. There are several approaches that can be used to achieve this goal, including splitting a row into multiple rows, splitting a row in two, expanding a column based on a delimiter, exploding a string, and separating rows. By using these techniques, analysts and data scientists can better understand the structure of their data and uncover insights that may not have been apparent before. It's important to choose the approach that best suits the specific data and analytical goals, as each method has its advantages and limitations. Whether it's splitting rows to better analyze individual elements or expanding columns to get a clearer picture of the data, these techniques can be powerful tools in the data scientist's arsenal.