Panda and Panda's Efficient Functions

Numpy is an extended library in Python environment, which supports a large number of dimension arrays.

And matrix operation; Pandas is also the data manipulation and analysis in Python environment.

Software package and powerful data analysis library. Both can be found in daily data analysis.

Important, without the support of Numpy and panda, data points.

Analysis will become extremely difficult. But sometimes we need to speed up the data analysis. What happened?

Is there any way to help us?

Let me introduce the functions of Numpy and Pandas to you. These efficient functions will enable

Data analysis is simpler and more convenient.

Six efficient functions of Numpy

Numpy is a Python language extension package for scientific computing, which usually contains powerful

N-dimensional array objects, complex functions, used to integrate C/C++ and Fortran generations.

Code tools and useful functions of linear algebra, Fourier transform and random number generation.

In addition to the above obvious uses, Numpy can also be used for the efficiency of general data.

Define multidimensional containers of any data type. This makes Numpy

It can realize seamless and rapid integration with various databases.

Next, analyze the six Numpy functions one by one.

Panda and Panda's Efficient Functions

Parameter partition ()

With the help of arg partition (), Numpy can find out the n largest cables.

References, these found indexes will also be output. Then we do numerical values as needed.

Sort.

Close all ()

All close () is used to match two arrays and get an output represented by a Boolean value. if

Within the tolerance range, the two arrays are not equal.

Then all close () returns False. This function is useful for checking whether two arrays are similar.

Very useful.

Clip ()

Clip () keeps the values in the array within an interval. Sometimes, we need

Make sure that the value is within the upper and lower limits. To do this, we can use Numpy's

The clip () function achieves this goal. Given an interval, the values outside the interval will be clipped.

To the edge of the interval.

Panda and Panda's Efficient Functions

Extract ()

As the name implies, extract () is to extract a specific element from an array under certain conditions.

Sue. With extract (), we can also use conditions such as and or.

Where ()

Where () is used to return elements that meet specific conditions from an array. For example, it

Returns the index position of a numeric value that meets a specific condition. Where () and making in SQL

Where conditions are similar.

Percentile ()

Percentile () is used to calculate the nth percentile of array elements in a specific axis direction.

Count.

The above are six efficient functions of Numpy expansion package, which I believe will help you.

Six Efficient Functions of Panda Data Statistics Software Package

Pandas is also a Python package, which provides fast, flexible and

A data structure with excellent expressive ability, which aims to make the processing structured (tabular, multidimensional, different

Structure) and time series data become simple and intuitive.

Pandas is suitable for the following types of data:

Panda and Panda's Efficient Functions

Tabular data with heterogeneous columns, such as SQL table or Excel table;

Ordered and disordered (not necessarily fixed frequency) time series data;

Arbitrary matrix data with row/column labels (isomorphic or heterogeneous);

Other arbitrary forms of statistical data sets. In fact, data doesn't need to be tagged at all.

Put it in the panda structure.

Pandas are good at dealing with the following types:

Pandas is also a Python package, which provides fast, flexible and

A data structure with excellent expressive ability, which aims to make the processing structured (tabular, multidimensional, different

Structure) and time series data become simple and intuitive.

Pandas is suitable for the following types of data:

Easy to handle missing data in floating-point data and non-floating-point data (represented by NaN

);

Resizable: It can be inserted from a data frame or an object in a higher dimension.

Or delete the column;

Explicit data can be automatically aligned: objects can be explicitly aligned to a set of labels, or they can be aligned with the.

Users can simply choose to ignore labels, making series, data frames and so on automatic.

Align data;

Panda and Panda's Efficient Functions

Flexible grouping function, split-apply-merge the data set, and input the data.

Row aggregation and transformation;

Simplify the process of converting data into data frame objects, which are basically

Irregular data with different indexes in Python and NumPy data structures;

Intelligent slicing, indexing and subset setting of large data sets based on tags;

Merge and connect data sets more intuitively;

Remolding and perspective data sets more flexibly;

Grading marks of the shaft (may contain multiple marks);

Powerful IO tool for downloading files from flat files (CSV and delimited files)

Excel file, data added to the database, and saved from HDF 5 format.

| Load data;

Specific functions of time series: data range generation, frequency conversion and moving window system.

Design, data movement and lag, etc.

read_csv(nrows=n)

One mistake most people make is that they will still save it. Csv file when they don't need it.

Read it completely. If unknown. The csv file has 10GB, and then read the whole file.

A.csv file will be very unwise, which not only takes up a lot of memory, but also costs a lot.

Time. All we need to do is start with. Csv files, and then import them as needed.

Continue importing.

Panda and Panda's Efficient Functions

Map ()

The map () function maps the value of Series according to the corresponding input. Used for connection

Each value in the sequence is replaced by another value, which may also come from a function.

It may come from a dictionary or series.

Application ()

Apply () allows users to pass functions and apply them to in Pandas sequences.

Every value.

It's in)

Is in () is used to filter data frames. Is in () is helpful to select a specific.

Row of values

Copy ()

The function copies a panda object. When one data frame is allocated to another.

A data frame, if one of the data frames is changed, the other data frame

The value of will also change. To prevent this problem, you can use the copy () function.

Select_d type ()

Select_d types () is used to return data frame columns based on d types columns.

A subset of. The parameters of this function can be set to include all data classes with a specific data class.

Or set to exclude columns with specific data types.

Finally, pivot_table () is also a very useful function in Panda.

Not if you know something about the use of pivot_table () in excel.

It is often easy to get started.