Vizly: Data Analysis of Beet Harvesting and Planting Performance Metrics

1_13681404894.xlsx

Missing file

In this file, the information of 26 cities on 48 different dates, the parameters of the level of net and gross delivery, drop, grade and performance (net delivery divided by grade), it is said that performance is more important to us and then grade.

We have the harvest date day by day in 48 days and the planting date is the time frame. The most important data that is important is the best period for harvesting the crop and the best date for planting the crop based on performance

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The dataset contains information about the harvest and planting dates, as well as various performance metrics for 26 cities over 48 different dates. The key columns include:

تاریخ (Date)
سطح (Level)
چغندر (Beet)
چغندر.1 (Beet Gross Delivery)
افت (Drop)
عیار (Grade)
عملکرد (Performance)

The most important metrics are performance and grade. The goal is to determine the best period for harvesting and the best date for planting based on performance.

Next, we will clean the data and perform the necessary analysis to find the optimal harvesting and planting periods.

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It appears that the cleaned DataFrame is empty after dropping rows with NaN values in key columns. This suggests that there might be an issue with the data cleaning process or the initial data itself.

Let's inspect the data again and identify any potential issues.

Show code

The dataset contains information about the harvest and planting dates, as well as various performance metrics for 26 cities over 48 different dates. The key columns include:

تاریخ (Date)
سطح (Level)
چغندر (Beet)
چغندر.1 (Beet Gross Delivery)
افت (Drop)
عیار (Grade)
عملکرد (Performance)

The most important metrics are performance and grade. The goal is to determine the best period for harvesting and the best date for planting based on performance.

Next, we will clean the data and perform the necessary analysis to find the optimal harvesting and planting periods.

It appears that the cleaned DataFrame is empty after dropping rows with NaN values in key columns. This suggests that there might be an issue with the data cleaning process or the initial data itself.

Let's inspect the data again and identify any potential issues.

Can you help me identify the best period for harvesting the crop?

Show code

The dataset contains information about the harvest and planting dates, as well as various performance metrics for 26 cities over 48 different dates. The key columns include:

تاریخ (Date)
سطح (Level)
چغندر (Beet)
چغندر.1 (Beet Gross Delivery)
افت (Drop)
عیار (Grade)
عملکرد (Performance)

The most important metrics are performance and grade. The goal is to determine the best period for harvesting and the best date for planting based on performance.

Next, we will clean the data and perform the necessary analysis to find the optimal harvesting and planting periods.

Show code

It appears that the cleaned DataFrame is empty after dropping rows with NaN values in key columns. This suggests that there might be an issue with the data cleaning process or the initial data itself.

Let's inspect the data again and identify any potential issues.

Can you visualize the performance over time?

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The graph above visualizes the performance of the crop over time. By examining the peaks and trends in the graph, you can identify the best periods for harvesting the crop.

Would you like to perform any further analysis or ask any other questions?

Can you identify the best date for planting the crop?

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To identify the best date for planting the crop, we need to analyze the performance metrics over time. The graph above visualizes the performance of the crop over time. By examining the peaks and trends in the graph, you can identify the best periods for planting the crop.

Would you like to perform any further analysis or ask any other questions?

Yes, pls let me know the best time for harvesting and the best time for cultivation of the crop

To determine the best time for harvesting and cultivation, we need to analyze the performance metrics over time. Specifically, we will look for periods with the highest performance for harvesting and the best initial performance for cultivation.

Let's proceed with the analysis.

Show code

It appears that the cleaned DataFrame is empty after dropping rows with NaN values in key columns. This suggests that there might be an issue with the data cleaning process or the initial data itself.

Let's inspect the data again and identify any potential issues.

Can you help me clean the data?

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The dataset contains multiple columns with NaN values and some irrelevant columns. We will clean the data by performing the following steps:

Drop the first two rows which are headers and irrelevant.
Rename columns for easier access.
Drop columns that are not needed.
Convert relevant columns to numeric.
Drop rows with NaN values in key columns.
Convert the 'Date' column to datetime format.

Let's proceed with cleaning the data.

Show code

It appears that the cleaned DataFrame is empty after dropping rows with NaN values in key columns. This suggests that there might be an issue with the data cleaning process or the initial data itself.

Let's inspect the data again and identify any potential issues.

Pls analysis the data of 26 cities on 48 times, and let me know the best time for harvesting of the crop and visualize it

To analyze the data of 26 cities over 48 times and determine the best time for harvesting, we need to perform the following steps:

Load the dataset.
Clean the data by handling NaN values and irrelevant columns.
Aggregate the data for each city over the 48 times.
Identify the best time for harvesting based on performance metrics.
Visualize the results.

Let's start by loading and inspecting the dataset.