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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Imputing Missing Dates(not Data) in Python

Ajay
Ajay Last Updated : 24 Feb, 2023
5 min read

Introduction

Imputing missing values is a crucial step when dealing with data. It is one of the steps performed in the Data Analysis. And coming to time-series data, the missing dates play a major role in the overall analysis or when we are trying to visualize the time-series data. If the missing dates are untouched, the performance of many time-series Machine Learning models will be affected. So one must carefully handle the missing dates and ensure the data is rearranged with the appropriate date imputations. This article explains how to impute missing dates in our python project.

Imputing missing dates depends on the type of data we get. The time-series data can be monthly, weekly, or even daily data. In this article, we will walk through all these scenarios where we will identify the missing dates in the data and write respective codes to impute these missing dates. We will be doing this using the Python Pandas library.

Learning Objectives

  1. To identify missing dates
  2. Learning how to impute dates
  3. Dealing with missing dates in daily, weekly, or monthly data

This article was published as a part of the Data Science Blogathon.

Table of Contents

Imputing Missing Dates in Daily Data

This section will discuss how to identify and impute data containing daily dates. The dataset I’m using contains daily temperature data. The first step in this will be to convert the date column to datetime datatype. We have the to_datetime() method from the Pandas library for this. Then we will make the date column the index. So let’s change the data type of the date column and view the dataset.

import pandas as pd

df_daily = pd.read_excel("/content/daily date.xlsx")

df_daily.index= pd.to_datetime(df_daily.index)
Imputing Missing Dates in Daily Data

In the above image, we can see the dataset we have taken. The dates here range from 15th Jan 2020 to 27th Jan 2020. You can view that the data contains missing dates like the 14th, 19th, 20th, etc. dates.

The next step is to store the dataset’s start and end date. With this, we can generate daily dates from the start date to the end date using the date_range() function in pandas. Let’s see its implementation below.

start = df_daily.index[0].date()

end = df_daily.index[len(df_daily)-1].date()

new_dates = pd.date_range(start=start,end=end,freq='D')

The start variable contains the date 2020-01-15, and the end variable contains the date 2020-01-27. These start and end dates are passed like parameters to the date_range() method. As the data we deal with is daily, we specify the frequency as ‘D.’ The resulting dates are stored in the variable new_dates. The output shown is the contents of the variable new_dates.

"

Finally, we have generated the new dates. The next step will be reindexing these new dates with the old dates in the daily temperature data. To achieve this, we use the reindex() method. To do this

df_daily = df_daily.reindex(new_dates)

df_daily = df_daily.rename_axis('date')

print(df_daily)
"

Finally, the dates have been imputed. From the above pic, it is visible that the missing dates have been added. When the missing dates are added, the corresponding values for other columns will be NaN. These can be imputed in different ways. We are not going to talk about that here. We will be only talking about the missing dates.

Imputing Missing Dates in Weekly Data

This section will deal with the weekly data containing missing dates. It is very much similar to the missing daily dates problem. First, we convert the date to datetime type using the pandas to_datetime().  Then we store the first and last dates from our data in the start and end variables. Then we use the data_range() to get the new dates and reindex them to our data frame.

df_weekly = pd.read_excel("/content/weekly.xlsx")

df_weekly.index= pd.to_datetime(df_weekly.index, infer_datetime_format=True)

start = df_weekly.index[0].date()

end = df_weekly.index[len(df_weekly)-1].date()

new_dates = pd.date_range(start=start,end=end,freq='7D')

df_weekly = df_weekly.reindex(new_dates)

df_weekly = df_weekly.rename_axis('date')

In the above code, for the to_datetime() function, we have used infer_datetime_format=True. This function determines which part in 2020-11-14 belongs to the Month, which part belongs to the Day, and which Year. From the output images, we can see that the missing dates are imputed, and at the same time, NaN values are placed in the missing columns.

Imputing Missing Dates in Weekly Data

One thing to note here is that, in the date_range() function, we have used ‘7D’ as frequency instead of ‘w.’ ‘w’ is the frequency you can use to generate weekly dates. ‘7D’ also is the same; it generates the dates with having 7 days gap between two dates.

But the main difference is that when we use ‘w,’ the dates are generated in a different format. For example, in this data, our first day is 14th Nov 2022, which is Monday, and the next day generated must be 21st Nov. But using ‘w’, the first day will be created is 20th Nov. Because ‘w’ generates dates such that they must start from the start of the week that is Sunday. As the 20th Nov is Sunday, the ‘w’ will start generating days from Sunday, so the dates generated will be 20th Nov, 27th Nov, etc. So it is better to go with ‘7D’ instead of ‘w,’ which will generate the dates correctly.

Imputing Missing Dates in Monthly Data

In this section, we will finally work with monthly data containing missing dates. By now, you must have found a way to impute missing dates for monthly data. If not, do not worry.

As usual, first, we convert the date to datetime type using the pandas to_datetime().  Then we store the first and last dates from our data in the start and end variables. Then we use the data_range() to get the new dates and then reindex them to our DataFrame.

df_monthly = pd.read_excel("/content/monthly.xlsx")

df_monthly.index= pd.to_datetime(df_monthly.index, format="%d-%m-%Y")

start = df_monthly.index[0].date()

end = df_monthly.index[len(df_monthly)-1].date()

new_dates = pd.date_range(start=start,end=end,freq='MS')

df_monthly = df_monthly.reindex(new_dates)

df_monthly = df_monthly.rename_axis('date')

Here instead of infer_date_format, we have used format in to_datetime(). It functions a bit differently. We pass the format of our date to the format variable. The frequency used here is ‘MS,’ which refers to the month’s start. The dates are generated with the starting day for all months. If you want the day to be the end date, then you can use just ‘M’ as freq. Below is the output; we can see that the missing monthly dates were indeed added to the data.

Imputing Missing Dates in Monthly Data

Conclusion

While analyzing time-series data, it’s crucial to fill in missing dates. Many methods are available in Python to find the missing dates, and we have used the Pandas package to solve this. The use of Pandas and its functions to fill in missing dates in Python was covered in this article. We learned this by applying these functions to weekly, daily, and monthly missing data. This article’s ideas will show you how to efficiently manage missing dates in your data.

The key takeaways from this article include the following:

  1. Using the to_datetime() method to convert the date column type to pandas datetime type and then further used to impute missing dates.
  2. date_range() method can be used to generate dates for a given start and end dates, and the frequency of generated dates can be modified
  3. reindex() method is used to reindex the old date column with the new date column, which has missing dates.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion. 

Ajay
Ajay

I work as a Developer in the field of Data Science. I constantly spend time learning new things be it related to AI, DataSceine, and CyberSecurity. Deep learning and machine learning are two topics that I find particularly fascinating, and Python is my preferred language for programming. Cyber Security is another field that I'm touching upon recently. I have experience with large-scale data analysis, and I have a solid grasp of a variety of deep learning and machine learning approaches, including neural networks, regression models, and natural language processing. I'm eager to take on new challenges and make a meaningful contribution to the industry, so I'm constantly seeking for ways to enlarge and deepen my knowledge and skills in the subject.

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