pandas datetime

Pandas data analysis (data structure) and pandas Data Analysis This article mainly expands pandas data structures in the following two directions: Series and DataFrame (corresponding to one-dimensional arrays and two-dimensional arrays in Series and numpy) 1. First, we will introduce how to create a Series. 1) A sequence can be created using an array. For example

Data analysis and presentation-Pandas data feature analysis and data analysis pandasSequence of Pandas data feature analysis data The basic statistics (including sorting), distribution/accumulative statistics, and data features (correlation, periodicity, etc.) can be obtained through summarization (lossy process of extracting data features), data mining (Knowledge formation ). The. sort_index () method so

, how to do? For more information please go to other blogs, where more detailed instructions are available .Pandas import time data for format conversion　　Draw multiple graphs on one canvas and add legends1 fromMatplotlib.font_managerImportfontproperties2Font = fontproperties (fname=r"C:\windows\fonts\STKAITI. TTF", size=14)3colors = ["Red","Green"]#the color used to specify the line4Labels = ["Jingdong","12306"]#used to specify the legend5Plt.plot (

The previous Pandas array (Pandas Series)-(3) Vectorization, said that when the two Pandas series were vectorized, if a key index was only in one of the series , the result of the calculation is nan , so what is the way to deal with nan ?1. Dropna () method:This method discards all values that are the result of NaN , which is equivalent to calculating only the va

Sometimes you need to do some work on the values in the Pandas series , but without the built-in functions, you can write a function yourself, using the Pandas series 's apply method, You can call this function on each value inside, and then return a new SeriesImport= PD. Series ([1, 2, 3, 4, 5])def add_one (x): return x + 1print s.apply ( Add_one)# results:0 6dtype:int64A chestnut:Names =PD. Serie

Data conversionDelete duplicate elements　　The duplicated () function of the Dataframe object can be used to detect duplicate rows and return a series object with the Boolean type. Each element pairsshould be a row, if the row repeats with other rows (that is, the row is not the first occurrence), the element is true, and if it is not repeated with the preceding, the metaThe vegetarian is false.A Series object that returns an element as a Boolean is of great use and is particularly useful for fil

-dateutil>=2->pandas) 3, of course, you can also go to Pandas's website download package HTTPS://PYPI.PYTHON.ORG/PYPI/PANDAS/0.17.1/#downloads Verifying Pandas With so many deployments in front, let's see if we can perform a simple pandas code validation. Because pandas