concat pandas

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

attach data rows. We'll use the Dataframe in the previous section to practice concatenation and additional operations on data rowsfunction concat () is a concatenation dataframe, such as a dataframe consisting of 3 rows of data can be concatenated with other data rows in order to reconstruct the original dataframe:Print ("Concat back together\n", Pd.concat ([df[:3],df[3:]]))To append data rows, you can use

The pandas Series is much more powerful than the numpy array , in many waysFirst, the pandas Series has some methods, such as:The describe method can give some analysis data of Series :Import= PD. Series ([1,2,3,4]) d = s.describe ()Print (d)Count 4.000000mean 2.500000std 1.290994min 1.00000025% 1.75000050% 2.50000075% 3.250000max 4.000000dtype:float64Second, the bigges

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

This article describes how the pandas series with the index index is vectorized:1. Index indexed arrays are the same:S1 = PD. Series ([1, 2, 3, 4], index=['a','b','C','D']) S2= PD. Series ([ten, +, +], index=['a','b','C','D'])PrintS1 +s2a11b22C33D44Dtype:int64Add the values corresponding to each index directly2. Index indexed array values are the same, in different order:S1 = PD. Series ([1, 2, 3, 4], index=['a','b','C','D']) S2= PD. Series ([ten, +,

This article describes how to use the pandas library in Python to analyze cdn logs. It also describes the complete sample code of pandas for cdn log analysis, then we will introduce in detail the relevant content of the pandas library. if you need it, you can refer to it for reference. let's take a look at it. Preface A requirement encountered in recent work is