python pandas dataframe

How to quickly get started using Python for financial data analysisIntroduction:This series of posts "quantitative small classroom", through practical cases to teach beginners to use Python, pandas for financial data processing, hope to be helpful to the big home." must -read article": "10 400 times-fold strategy sharing-video-line-guided code""All series article

daily statistical analysis of small and medium-sized enterprises, half a bucket of sub-water, limited capacity, other levels can be bypassed: Get data: I plan to capture the investment and loan data of XX financial website from the internet for use as the data source. Basically, data in each dimension and format is available for later operations to read data: here, I will divide the obtained data into xls, csv, SQL, and pandas

[Spark] [Python]spark example of obtaining Dataframe from Avro fileGet the file from the following address:Https://github.com/databricks/spark-avro/raw/master/src/test/resources/episodes.avroImport into the HDFS system:HDFs Dfs-put Episodes.avroRead in:Mydata001=sqlcontext.read.format ("Com.databricks.spark.avro"). Load ("Episodes.avro")Interactive Run Results:In [7]: Mydata001=sqlcontext.read.format ("Com.

[Example of a limited record taken in Spark][python]dataframethe continuationIn [4]: Peopledf.select ("Age")OUT[4]: Dataframe[age:bigint]In [5]: Mydf=people.select ("Age")---------------------------------------------------------------------------Nameerror Traceback (most recent)----> 1 Mydf=people.select ("Age")Nameerror:name ' People ' is not definedIn [6]: Mydf=peopledf.select ("Age")In [7]: Mydf.take (3)

[Spark] [Python] An example of opening a JSON file in a dataframe way:[email protected] ~]$ cat People.json{"Name": "Alice", "Pcode": "94304"}{"Name": "Brayden", "age": +, "Pcode": "94304"}{"Name": "Carla", "age": +, "Pcoe": "10036"}{"Name": "Diana", "Age": 46}{"Name": "Etienne", "Pcode": "94104"}[Email protected] ~]$[Email protected] ~]$ HDFs dfs-put People.json[Email protected] ~]$ HDFs dfs-cat People.jso

NaNB 2001 3500 NaN 1C 2002 4500 NaN 2D 2003 6000 NaN 3Del data1[' outcome ']The result of deleting a column is:Year Income MoneyA 2000 3000 0B 2001 3500 1C 2002 4500 2D 2003 6000 3Primary index objects in pandas and their corresponding indexed methods and propertiesThere's also a reindex function to rebuild the indexdata={' year ': [2000,2001,2002,2003],' Income ': [3000,3500,4500,6000]}DATA1=PD. DataFrame

Dataframe has a property of empty, directly with dataframe.empty judgment on the line.If DF is empty, then Df.empty returns True, and vice versa returns false.Be careful not to add () after empty.Learn tips: Check your own version of the pandas corresponding to the official Web download pandas use PDF manual, directly search "empty", you can find some examples of

1. The most important thing in the pandas library is the variable-length dictionary (series) and the most important function of the series is alignment; that is, an index, a value in the form, as follows:The series uses PD, which automatically adds an index to each value in the list, or you can specify the index yourself as follows:I generated the dictionary in the form of a list, as follows:You can change the format of Dictionary D with series as fol

2 DataFrameA: Dataframe automatically indexed by passing in a list of equal lengths1data={' State':['Ohio','Ohio','Ohio','Nevada','Nevada'],2 ' Year':[ -,2001,2002,2001,2002],3 'Pop':[1.5,1.7,3.6,2.1,2.9]}4Frame=dataframe (data)B: Specify sequential sequence (previously sorted by default)1 DataFrame (data,columns=['year','State',' pop'])C: When the d

Two data structure series and dataframe.SeriesThe series is the same as a list in Python, with data and index values.Here we create a series object. Data values and indexes for series objects:The index of the list starts at 0, and the series is indexed by default, similar to the list starting with 0. However, you can also customize the index:Indexes can be redefined:Operation elements according to index:Series is also used in the form of dictionaries:

Objective Pandas is a numpy built with more advanced data structures and tools than the NumPy core is the Ndarray,pandas is also centered around Series and dataframe two core data structures. Series and Dataframe correspond to one-dimensional sequence and two-dimensional table structure respectively. Pandas's conventi

Pandas is the data analysis processing library for PythonImport Pandas as PD1. read CSV, TXT fileFoodinfo = Pd.read_csv ("pandas_study.csv""utf-8")2, view the first n, after n informationFoodinfo.head (n) foodinfo.tail (n)3, check the format of the data frame, is dataframe or NdarrayPrint (Type (foodinfo)) # results: 4. See what columns are availableFoodinfo.colu

Let me briefly introduce the two commonly used data structures, series and daraframe in Python, which are defined by the Pandas module. The series is similar to dict in Python, but is structured, and dataframe is similar to a table in a database.1.pandas basic data Structure

Time series PV-GMV Double axis line chartImport NumPy as Npimport pandas as Pdimport matplotlib.pyplot as Pltn = 12date_series = Pd.date_range (start= ' 2018-01-01 ', Periods=n, freq= "D") data = { ' PV ': [10000, 12000, 13000, 11000, 9000, 16000, 10000, 12000, 13000, 11000, 9000, 16000], ' GMV ': [+-------------- DataFrame (data, index=date_series) ax = df.plot ( secondary_y=[' GMV '), x_compat

', Index=false) Except Exception as E: print (E.message) Run, OK, can be stored in the index parameter indicates whether the Dataframe index as a column to store, generally not required, so the assignment is False Now it seems that the problem is solved, but there is a small problem.If I have a CSV file that contains Chinese (i window):Name Age classXiao Ming 151 gradeXiao Zhang 183 grade engine = Create_engine (str (r "mysql+mysqldb://%s:" + '%s

Most of the students who Do data analysis start with excel, and Excel is the most highly rated tool in the Microsoft Office Series.But when the amount of data is very large, Excel is powerless, python Third-party package pandas greatly extend the functionality of excel, the entry takes a little time, but really is the necessary artifact of big data!1. Read data from a filePandas supports the reading of mult

dagscheduler.scala:100617/10/03 06:00:34 INFO Scheduler. Dagscheduler:submitting 1 missing tasks from Resultstage 1 (mappartitionsrdd[5) at count at Nativemethodaccessorimpl.java :-2)17/10/03 06:00:34 INFO Scheduler. Taskschedulerimpl:adding Task Set 1.0 with 1 tasks17/10/03 06:00:34 INFO Scheduler. Tasksetmanager:starting task 0.0 in Stage 1.0 (TID 1, localhost, partition 0,node_local, 1999 bytes)17/10/03 06:00:34 INFO executor. Executor:running task 0.0 in Stage 1.0 (TID 1)17/10/03 06:00:34 I

NaNDtype:object----[' A ', ' B ', ' C ']0 A1 12 NaN3 NaNDtype:object0 b1 22 NaN3 NaNDtype:object0 10 A B,c1 1 2,32 Nan Nan3 Nan Nan0 10 A, b C1 32 Nan Nan3 Nan NanDataframe0 a-b-c1 1-2-c2 [,-,-,]Name:key2, Dtype:object0 [A, b, c]1 [1, 2, C]2 NaNName:key2, Dtype:object#String Indexs = PD. Series (['A','b','C','Bbhello','123', Np.nan,'HJ']) DF= PD. DataFrame ({'Key1': List ('abcdef'), 'Key2':['Hee','FV','W','Hjja','123', Np.nan]})Prin

Pandas is the preferred library for subsequent content in this book. The pandas can meet the following requirements: Data structure with automatic or explicit data alignment by axis. This prevents many common errors caused by data misalignment and data from different data sources (indexed differently). Integrated time series capabilities Data structures that can handle time series data as

Pip Install Pandaspip Install XLRDWhen a lot of records, with Excel sorting processing more laborious, Excel program is not responsive , with pands perfect solution.# We'll use data structures and data analysis tools provided in Pandas Libraryimp Ort pandas as pd# Import retail sales data from an Excel Workbook into a data frame# path = '/documents/analysis/python