Adventures in Machine Learning

Efficient Data Manipulation with usecols: Optimization for Large Datasets

Using the usecols argument in pandas can be highly beneficial, especially when you are dealing with large datasets with numerous columns. To ease this burden, Python provides easy-to-use libraries, like pandas, for efficient data analysis and manipulation.

Method 1: Use usecols with Column Names

One of the most straightforward and simple ways to use the usecols argument in pandas involves referring to columns by name. Doing this ensures you only import the columns that you are interested in.

To demonstrate this, let’s import a CSV file with specific columns by name. Example 1: Use usecols with Column Names

Consider a sample dataset named “sales.csv”, which contains sales data for selected products in different regions:

| Region | Product | Sales | Cost | Profit |

|:————-:|:———–:|:——:|:——:|:——:|

| North America | Product A | 150000 | 100000 | 50000 |

| Europe | Product B | 250000 | 200000 | 50000 |

| South America | Product C | 100000 | 80000 | 20000 |

| Asia | Product D | 200000 | 150000 | 50000 |

To import this CSV file into pandas with selected columns by name, we simply add the usecols argument and a list of required column names to the pandas read_csv() method:

“`

import pandas as pd

sales = pd.read_csv(“sales.csv”, usecols=[“Product”, “Sales”])

“`

This will import only the “Product” and “Sales” columns from the “sales.csv” file, excluding the “Region”, “Cost”, and “Profit” columns. Using this method significantly reduces the memory used and speeds up data processing.

Method 2: Use usecols with Column Positions

Another way to call the usecols argument in pandas is by using column positions. When working with large datasets with many columns, this method is preferable to referencing the columns by name.

Example 2: Use usecols with Column Positions

Suppose we want to exclude the first and last columns from a dataset containing 6 columns, which include “Name”, “Gender”, “Age”, “Email”, “Phone”, and “Address”. To import this dataset with selected columns by position, we can use the following line of command:

“`

import pandas as pd

data = pd.read_csv(“data.csv”, usecols=lambda x: x not in [0, 5])

“`

In this method, we specify the columns to be excluded by their position, interpreted by their position in the list. This statement reads that the first and last column will not be imported into the resulting dataframe.

Hence it is now possible to manipulate the resulting dataset only with the columns and data we need. In conclusion, we have seen how to use the usecols argument in pandas, both with column names and positions.

By limiting the dataset’s columns that pandas imports, we can speed up data manipulation and save memory space. So next time you’re dealing with large datasets with numerous columns, implement this trick.

Remember, larger data requires more efficient handling!

In the era of big data, extraction and manipulation of large datasets requires efficient and effective methodology. In this context, Python’s pandas library provides an easy way to import data from various sources.

Pandas allows us to import data from a CSV file with just one line of code. It’s a great way to get started with data analysis and manipulation.

However, when working with large datasets, there is no point in importing all the columns from a dataset. pandas provides a way to control what gets imported, making tasks much faster and more efficient.

In this article, we will discuss how to use the usecols argument with column positions to import CSV files with specific columns. Example 2: Use usecols with Column Positions

Let us consider an example dataset, which contains 7 columns named: “Name”, “Gender”, “Age”, “Email”, “Phone”, “Address”, and “Salary”.

The “Salary” column is the last column of this particular dataset. Now, if we want to exclude the last column and only import the rest of the columns, we can use the usecols argument with column index to import specific columns.

“`

import pandas as pd

data = pd.read_csv(“data.csv”, usecols=lambda x: x not in [6])

“`

Here, we have used the indexing method instead of using the column names and selected every index position except the last one. In this way, only “Name”, “Gender”, “Age”, “Email”, and “Phone” columns will be imported into the dataframe.

Now, consider that we only want to work with the first three columns of the sample dataset. We can do this by specifying the list of selected column indices to the usecols method as follows:

“`

import pandas as pd

data = pd.read_csv(“data.csv”, usecols=[0, 1, 2])

“`

By specifying the list of column indices in the usecols argument, we only import those columns defined in the user-specified column index range. This is a highly optimal way of importing large datasets consisting of multiple columns.

By defining the column index range, you can easily exclude the most irrelevant columns that are not vital to your task. This practice also saves computation time and helps speed up data manipulation.

The lambda function used in method 2 creates a logical sequence that reads “x is not in the list of required indices.” This effectively eliminates the need to create a long list of column names to exclude from the dataset. Using this approach offers considerable ease and flexibility in column selection.

Furthermore, the usecols function with column positions allows you to extract a combination of columns that match your specific requirements. If you need to include columns “Name,” “Age,” and “Phone” and exclude the rest, you can accomplish this by modifying the syntax as follows:

“`

import pandas as pd

data = pd.read_csv(“data.csv”, usecols=[0, 2, 4])

“`

This will export only columns “Name,” “Age,” and “Phone”. Since you can use this method to select one or many columns, there is no limit to the number of columns that can be added to this parameter, regardless of their index.

In conclusion, pandas’ usecols argument with column positions offers tremendous control when importing datasets to allow for faster, more efficient data manipulation. Covering the usage of column positions to extract the desired columns builds on our previous discussion, emphasizing the value of utilizing the computational power of pandas packages when dealing with vast data sets.

With this addition to the method’s application, we hope you can now work effectively with datasets consisting of multiple columns. In conclusion, using the usecols argument in pandas is a valuable technique in data analysis and manipulation.

By including only the necessary columns, we can save time on computations and reduce memory usage. We have discussed two methods of working with this function – by column names and column positions.

Using the latter by defining column indexes that we need or don’t need to import, gives us greater control over our dataset. Ultimately, when working with large datasets with many columns, it is crucial to optimize our data frames to reduce computational overheads.

Therefore, understanding and effectively implementing the usecols argument in pandas can be of immense value.

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