pandas combine two data frames

have the following advantages: Faster (once set) Self-explanation (by checking the code, you will know what it has done) Easy to generate reports or emails More flexible, because you can define custom Aggregate functions Read in the data First, let's build the required environment. If you want to continue with me, you can download this Excel file. Import pandas as pd Import numpy as np Vers

official documentsOnce you have completed your first kernel, you can return to the document and read the rest. Here is my suggested reading order: Processing of lost data Group: Split-apply-combine Mode Reshaping and data cross-table Data merging and linking Input/Output tool (Text,csv,hdf5 ..

col_namesIf C.endswith ("(g)"):Gram_columns.append (c)GRAM_DF = food_info[gram_columns] The column name at the end of the #把以 "G" is indexed to the Dataframe object, resulting in the corresponding multi-column data.Print (Gram_df.head (3))7) data types in Pandas#object-for String values#int-for integer values#float-for Float values#datetime-for Time values#bool-for Boolean values#print (Food_info.dtypes)3,

1 concat The Concat function is a method underneath the pandas that allows for a simple fusion of data based on different axes. Pd.concat (Objs, axis=0, join= ' outer ', Join_axes=none, Ignore_index=false, Keys=none, Levels=none, Names=None, Verify_integrity=false)1 2 1 2 1 2 Parameter descriptionObjs:series,dataframe or a sequence of panel compositions lsitAxis: Axis that needs to merge links, 0

']df_obj[' user number '].isin (alist) #将要过滤的数据放入字典中, uses Isin to filter the data, returns the row index and the results of each row filter, and returns if the match is turedf_obj[df_obj[' user number '].isin (alist)] #获取匹配结果为ture的行Filter data using Dataframe blur (like in sql):df_obj[df_obj[' package '].str.contains (R '. * Voice cdma.* ')] #使用正则表达式进行模糊匹配, * match 0 or unlimited, match 0 or 1 timesData c

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

This article mainly introduced the Python pandas in the Dataframe type data operation function method, has certain reference value, now shares to everybody, has the need friend to refer to The Python data analysis tool pandas Dataframe and series as the primary data structu

connection key in the right Dataframe Left_index: Use the row index in the left dataframe as the connection key Right_index: Use the row index in the right dataframe as the connection key Sort: The default is true to sort the merged data. setting to False in most cases can improve performance Suffixes: A tuple of string values that specifies the suffix name appended to the column name when the left and right dataframe exist with the s

Pandas is a very important data processing library in Python, and pandas provides a very rich data processing function, which is helpful to machine learning and data preprocessing before data mining. The following is the recent