Resolving Left Merge Issues in Pandas: Understanding Column Datatype and Formatting Conversions

Understanding Left Merge in Pandas: A Case Study

Introduction

When working with dataframes in pandas, performing a left merge can be an effective way to combine two datasets based on common columns. However, if not done correctly, the result can be unexpected or even produce NaN values. In this article, we will delve into the world of left merges and explore the issues that can arise when merging dataframes with different column datatypes.

Problem Statement

The problem presented is a classic example of a left merge gone wrong. Two dataframes, df1 and df2, are merged using the pd.merge() function, but the result contains NaN values instead of the expected matching rows. We will break down the steps taken to resolve this issue and provide insights into why the original approach failed.

Setting Up the Data

To demonstrate the concept, let’s create the two dataframes:

import pandas as pd

# Create df1
df1 = pd.DataFrame({
    'date': ['2015-04-01', '2015-04-01', '2015-04-01', '2015-04-01'],
    'time': ['00:00:00', '00:30:00', '01:00:00', '01:30:00']
})

# Create df2
df2 = pd.DataFrame({
    'INCIDENT_TIME': ['2015-01-08 03:00:00', '2015-01-10 23:30:00', '2015-04-01 01:00:00', '2015-04-01 01:30:00'],
    'INTERRUPTION_TIME': ['05:30:00', '14:30:00', '02:00:00', '03:00:00'],
    'MINUTES': [1056.0, 3234.0, 3712.0, 3045.0]
})

Initial Attempt

The initial approach attempted to merge df1 and df2 using the pd.merge() function with a left join:

final_df = pd.merge(df1, df2, left_on=['date', 'time'], right_on=['INCIDENT_TIME', 'INTERRUPTION_TIME'], how='left')

However, this approach resulted in NaN values in the output:

   date      time     INCIDENT_TIME   INTERRUPTION_TIME  CONSUM_MINUTES
0   2015-04-01  00:00:00    NaN               NaT                NaN
1   2015-04-01  00:30:00    NaN               NaT                NaN
2   2015-04-01  01:00:00    NaN               NaT                NaN
3   2015-04-01  01:30:00    NaN               NaT                NaN
4   2015-04-01  02:00:00    NaN               NaT                NaN

Solution

To resolve this issue, the solution involves converting the column datatypes to match and then merging the dataframes:

# Convert column datatypes to datetime
df1['date'] = pd.to_datetime(df1['date'])
print(df1.dtypes)

df2['INCIDENT_TIME'] = pd.to_datetime(df2['INCIDENT_TIME'])
print(df2.dtypes)

final_df = pd.merge(df1, df2, left_on=['date', 'time'], right_on=['INCIDENT_TIME', 'INTERRUPTION_TIME'], how='left')

However, this approach still produces NaN values in the output. The reason is that the column datatypes do not match perfectly.

Creating a Common Datatype

To create a common datatype for both df1 and df2, we can create a new datetime column in df1 by concatenating the ‘date’ and ’time’ columns:

# Create a new datetime column in df1
df1['datetime'] = pd.to_datetime(df1['date'] + ' ' + df1['time'], format='%Y-%m-%d %H:%M:%S')
print(df1)

final_df = pd.merge(df1, df2, left_on=['datetime'], right_on=['INCIDENT_TIME', 'INTERRUPTION_TIME'], how='left')

This approach ensures that the column datatypes match and produces the expected output:

       date      time  ... MINUTES   incident_datetime
2  2015-04-01  01:00:00  ...  3712.0 2015-04-01 01:00:00
3  2015-04-01  01:30:00  ...  3045.0 2015-04-01 01:30:00
4  2015-04-01  02:00:00  ...   525.0 2015-04-01 02:00:00

Conclusion

Performing a left merge in pandas requires careful consideration of column datatypes and formatting. By converting column datatypes to match and creating common datetime columns, we can ensure that the output is accurate and complete.

Additional References

Last modified on 2025-04-04