Py之Pandas：Python的pandas庫簡介、安裝、使用方法詳細攻略

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目錄

pandas庫簡介

pandas庫安裝

pandas庫使用方法

1、函數(shù)使用方法

2、使用經(jīng)驗總結(jié)

3、繪圖相關(guān)操作

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pandas庫簡介

? ? ? 在 Python 自帶的科學計算庫中，Pandas 模塊是最適于數(shù)據(jù)科學相關(guān)操作的工具。它與 Scikit-learn 兩個模塊幾乎提供了數(shù)據(jù)科學家所需的全部工具。Pandas 是一種開源的、易于使用的數(shù)據(jù)結(jié)構(gòu)和Python編程語言的數(shù)據(jù)分析工具。

? ? ? 根據(jù)大多數(shù)一線從事機器學習應(yīng)用的研發(fā)人員的經(jīng)驗，如果問他們究竟在機器學習的哪個環(huán)節(jié)最耗費時間，恐怕多數(shù)人會很無奈地回答您：“數(shù)據(jù)預(yù)處理。”。事實上，多數(shù)在業(yè)界的研發(fā)團隊往往不會投人太多精力從事全新機器學習模型的研究，而是針對具體的項目和特定的數(shù)據(jù)，使用現(xiàn)有的經(jīng)典模型進行分析。這樣一來，時間多數(shù)被花費在處理數(shù)據(jù)，甚至是數(shù)據(jù)清洗的工作上，特別是在數(shù)據(jù)還相對原始的條件下。Pandas便應(yīng)運而生，它是一款針對于數(shù)據(jù)處理和分析的Python工具包，實現(xiàn)了大量便于數(shù)據(jù)讀寫、清洗、填充以及分析的功能。這樣就幫助研發(fā)人員節(jié)省了大量用于數(shù)據(jù)預(yù)處理下作的代碼，同時也使得他們有更多的精力專注于具體的機器學習任務(wù)。

pandas: powerful Python data analysis toolkit
pandas

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pandas庫安裝

pip install pandas

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pandas庫使用方法

1、函數(shù)使用方法

Pickling

read_pickle(path[,?compression])

Load pickled pandas object (or any object) from file.

Flat File

read_table(filepath_or_buffer[,?sep,?…])	(DEPRECATED) Read general delimited file into DataFrame.
read_csv(filepath_or_buffer[,?sep,?…])	Read a comma-separated values (csv) file into DataFrame.
read_fwf(filepath_or_buffer[,?colspecs,?…])	Read a table of fixed-width formatted lines into DataFrame.
read_msgpack(path_or_buf[,?encoding,?iterator])	Load msgpack pandas object from the specified file path

Clipboard

read_clipboard([sep])

Read text from clipboard and pass to read_csv.

Excel

read_excel(io[,?sheet_name,?header,?names,?…])	Read an Excel file into a pandas DataFrame.
ExcelFile.parse([sheet_name,?header,?names,?…])	Parse specified sheet(s) into a DataFrame

ExcelWriter(path[,?engine,?date_format,?…])

Class for writing DataFrame objects into excel sheets, default is to use xlwt for xls, openpyxl for xlsx.

JSON

read_json([path_or_buf,?orient,?typ,?dtype,?…])

Convert a JSON string to pandas object.

json_normalize(data[,?record_path,?meta,?…])	Normalize semi-structured JSON data into a flat table.
build_table_schema(data[,?index,?…])	Create a Table schema from?data.

HTML

read_html(io[,?match,?flavor,?header,?…])

Read HTML tables into a?list?of?DataFrame?objects.

HDFStore: PyTables (HDF5)

read_hdf(path_or_buf[,?key,?mode])	Read from the store, close it if we opened it.
HDFStore.put(key,?value[,?format,?append])	Store object in HDFStore
HDFStore.append(key,?value[,?format,?…])	Append to Table in file.
HDFStore.get(key)	Retrieve pandas object stored in file
HDFStore.select(key[,?where,?start,?stop,?…])	Retrieve pandas object stored in file, optionally based on where criteria
HDFStore.info()	Print detailed information on the store.
HDFStore.keys()	Return a (potentially unordered) list of the keys corresponding to the objects stored in the HDFStore.
HDFStore.groups()	return a list of all the top-level nodes (that are not themselves a pandas storage object)
HDFStore.walk([where])	Walk the pytables group hierarchy for pandas objects

Feather

read_feather(path[,?columns,?use_threads])

Load a feather-format object from the file path

Parquet

read_parquet(path[,?engine,?columns])

Load a parquet object from the file path, returning a DataFrame.

SAS

read_sas(filepath_or_buffer[,?format,?…])

Read SAS files stored as either XPORT or SAS7BDAT format files.

SQL

read_sql_table(table_name,?con[,?schema,?…])	Read SQL database table into a DataFrame.
read_sql_query(sql,?con[,?index_col,?…])	Read SQL query into a DataFrame.
read_sql(sql,?con[,?index_col,?…])	Read SQL query or database table into a DataFrame.

Google BigQuery

read_gbq(query[,?project_id,?index_col,?…])

Load data from Google BigQuery.

STATA

read_stata(filepath_or_buffer[,?…])

Read Stata file into DataFrame.

StataReader.data(**kwargs)	(DEPRECATED) Reads observations from Stata file, converting them into a dataframe
StataReader.data_label()	Returns data label of Stata file
StataReader.value_labels()	Returns a dict, associating each variable name a dict, associating each value its corresponding label
StataReader.variable_labels()	Returns variable labels as a dict, associating each variable name with corresponding label
StataWriter.write_file()

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2、使用經(jīng)驗總結(jié)

Python語言學習之pandas：DataFrame二維表的簡介、常用函數(shù)、常用案例之詳細攻略

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