Append dataframe to text file python

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Append dataframe to text file python

This is almost a detailed duplicate of the following: Python, Pandas: Writing DataFrame content into the text of the file I report again here is the answer to the SO question listed with some very small changes to fit this case. You can use two methods. np.savetxt() in which case you should have something like: np.savetxt('xgboost.txt', a.values, fmt='%d', delimiter=\t, header=X\tY\tZ\tValue) assuming a dataframe. Of course you can change the delimiter you want (tab, comma, space, etc.). Another option, as mentioned in the answer I joined and mentioned here from @MYGz, is to use the to_csv method, in other words: a.to_csv('xgboost.txt', header=True, index=False, sep='\t', mode='a') I have pandas DataFrame like this X Y Z Value 0 18 55 1 70 1 18 55 2 67 2 18 57 2 75 3 18 58 1 35 4 19 54 2 70 I want to write this data to a text File in this way, 18 55 17 0 18 55 2 67 18 57 2 75 18 58 1 35 19 54 2 70 I've tried something like f = open (writePath, 'a') f.writelines([', str(data['X']),', str(data['Y']) , ', str(data['Z']) , ', str(data['Value'])]) f.close() but its not working. How do we do this? Source: R/data_interface. R Serialize a Spark DataFrame into plain text format. spark_write_text( x, path, mode = NULL, options = list(), partition_by = NULL, ... ) x A Spark DataFrame or dplyr operation path The path to the file. It should be accessible from the cluster. Supports hdfs://, s3a:// and file:// protocols. Mode of a character element. Specifies behavior when the data or table already exists. Supported values include 'error', 'add', 'rewrite' and ignore. Note that 'rewrite' will also change the column structure. For more information, see for your version of Spark. String list options with additional options. partition_by character vector. Output partition by columns given on the file system. ... R optional arguments; Other spark serialization routines: spark_load_table(), spark_read_avro(), spark_read_csv(), spark_read_delta(), spark_read_jdbc(), spark_read_json(), spark_read_libsvm(), spark_read_orc(), spark_read_parquet(), spark_read_source(), spark_read_table(), spark_read_text(), spark_read_text spark_read_text spark_read(), spark_save_table(), spark_write_avro(), spark_write_csv(), spark_write_delta(), spark_write_jdbc(), spark_write_json(), spark_write_orc(), spark_write_parquet(), spark_write_source(), spark_write_table() Way to get Excel data to text in delimited form tab. Need to use pandas as well as xlrd. import pandas as pd import xlrd import os Path=C:\downloads wb = pd. ExcelFile(Path+\\input.xlsx, engine=None) sheet2 = pd.read_excel(wb, sheet_name=Sheet1) Excel_Filter=sheet2[sheet2['Name']='Test'] Excel_Filter.to_excel(C:\downloads\\output.xlsx, index=None) wb2=xlrd.open_workbook(Path+\output.xlsx) df=wb2.sheet_by_name(Sheet1) x=df.nrows y=df.ncols for i in range(0,x): for j in f=open(Path+\\emails.txt, a) f.write(A+\t) f.close() f=open(Path+\\emails.txt, a) f.write() f.close() os.remove(Path+\\output.xlsx) print(Excel_Filter) We need to first generate the xlsx file with filtered data and then convert the information into a text file. Depending on the needs, we can use \t for circles and the type of data we want to use in the text file. Other: DataFrame or series/dictations like object, or list of these ignore_index: boolean, by default false if true, does not use index labels. verify_integrity : boolean , default False If True, raise ValueError on creating index with duplicates. Convert text files to CSV using Python pandas let's see how to convert text file to CSV using Python pandas. Python will read data from a text file and create a dataframe with rows equal to the number of lines in the text file and columns equal to the number of fields in a single line. See the example below for a better understanding. The original text of the dataframe file created from the above text file is as follows: the CSV file composed of the given text file note: the first column in the dataframe is the default when a text file is read. Once the dataframe is created, we will save this dataframe in CSV file format using Dataframe.to_csv() file. Syntax: Dataframe.to_csv(parameters) Return: None Let's see examples: Example 1: import pandas as pd dataframe1 = pd.read_csv(GeeksforGeeks.txt) dataframe1.to_csv('GeeksforGeeks.csv', index = None) Output: CSV File formed from given text file The text file read is same as above. After successfully running the above code, a file called geeksforGeeks.csv will be created in the same directory. Example 2: Suppose the column headings are not given, and the text file appears: text file without header then while composing the code you can specify the header. import pandas as pd websites = pd.read_csv(GeeksforGeeks.txt ,header = None) websites.columns = ['Name', 'Type', 'Website'] websites.to_csv('GeeksforGeeks.csv', index = None) Output: CSV file with headers We see that headers have been added successfully and file has been converted from '.txt' format to '.csv' format. Example 3: In this example, text file fields are delimiter/separated by the user defined. '/' Delimited Text File import pandas as pd account = pd.read_csv(GeeksforGeeks.txt, delimiter = '/') account.to_csv('GeeksforGeeks.csv', index = None) Output: CSV File While reading data we specifi that data should be tokenized using specified delimiter. In this case '/'. Attention Geek! Strengthen your foundations with the Python Programming Foundation course and learn the basics. For starters, preparing your interview enhances the concepts of your data structures with the Python DS course. Pandas Library offers a wide range of possibilities to save your data to files and load data from files. In this section you will see more information about working with CSV and Excel files. You will also see how to use other types of files, such as Web pages, databases, and Pickle Python files. You've already learned how to read and write CSV files. Now let's dig a little deeper into the details. When you .to_csv() to save your DataFrame, you can provide an argument for the path_or_buff parameter to specify the path, name and extension of the target file. path_or_buff is the first argument .to_csv receive a () argument. It can be any string that indicates a valid file path that contains its file name and extension. You've seen this in a previous example. However, if you delete path_or_buff then .to_csv() does not create any files. Instead, it returns the corresponding string: >>>>> df = pd. DataFrame(data=data). T >>> s = df.to_csv() >>> print(s) ,COUNTRY,POP,AREA,GDP,CONT,IND_DAY CHN,China,1398.72,9596.96,12234.78,Asia, IND,India,1351.16,3287.26,2575.67,Asia,1947-08-15 USA,US,329.74,9833.52,19485.39,N.America,1776-07-04 IDN,Indonesia,2 68.07,1910.93,1015.54,17-08-1945, BRA,Brazil,Brazil,210.32,8515.77,2055.51,S.America,1822-09-07 PAK,Pakistan,205.71,71,0781.91,302.14,Asia,1947-08-14 NGA,Nigeria,200.96,923.77,375.77,Africa,1960-10-01 BGD,Bangladesh,167.09,147.57,245 63,Asia ,1971-03-26 RUS,Russia,146.79,17098.25,1530.75,,1992-06-12 MEX,Mexico,126.58,1964.38,1158.23,N.America ,1810-09-16 JPN,Japan,126.22,377.97,4872.42,Asia, DEU,Germany,83.02,357.11,3693.2,Europe, FRA,France,67.02,640.68,2582.49,Europe,1789-07-14 GBR,UK,66.44,242.5,2631.23,Europe, ITA,Italy,60.36,301.34,1943.84,Europe, ARG,Argentina,44.94,2780.4,637.49,S.America,1816-07-09 DZA,Algeria,43.38,2381.74,167.56,Africa,1962-07-05 CAN,Canada,37.59,9984.67,1647.12,N.America,1867-07-01 AUS,Australia,25.47,7692.02,1408.68,Oceania, KAZ,Kazakhstan,18.53,2724.9,159.41,Asia,1991-12-16 Now you have the string s instead of a CSV file. You also have some lost values in your DataFrame object. For example, the continent is not available for Russia and independence days for several countries (China, Japan, etc.). In data science and machine learning, you need to handle lost values carefully. Pandas are superior here! By default, pandas use nan value to replace missing values. Note: Bread, which stands for not a number, is a certain floating-point value in python. You can get a bread quantity with any of the following functions: floating ('nan') math.nan numpy.nan continental that corresponds to Russia in bread df: >>>>>> df.loc[RUS', 'CONT'] bread this example is marked using .loc[loc] to get data with row and column names. When you save your DataFrame in a CSV file, the blank strings ('') will represent the missing data. You can see this both in the .csv and in the s string. If you want to change this behavior, then use the optional na_rep parameter: >>>>> df.to_csv('New Data.csv', na_rep='(Missing)') This file code generates new data.csv where missing values are no longer blank strings. You can expand the following code block to see how this file should look ,COUNTRY,POP,AREA,GDP,CONT,IND_DAY ,COUNTRY,POP,AREA,GDP,CONT,IND_DAY IND,India,1351.16,3287.26,2575.67,Asia,1947-08-15 USA,US,329.74,9833.52,19485.39,N.America,1776-07-04 IDN,Indonesia,268.07,1910.93,1015.54,Asia,1945-08-17 BRA,Brazil,210.32,8515.77,2055.51,S.America,1822-09-07 PAK,Pakistan,205.71,881.91,302.14,Asia,1947-08-14 NGA,Nigeria,200.96,923.77,375.77,Africa,1960-10-01 BGD,Bangladesh,167.09,147.57,245.63,Asia,1971-03-26 RUS,Russia,146.79,17098.25,1530.75,(missing),1992-06-12 MEX,Mexico,126.58,1964.38,1158.23,N.America,1810-09-16 JPN,Japan,126.22,377.97,4872.42,Asia,(missing) DEU,Germany,83.02,357.11,3693.2,Europe,(missing) FRA,France,67.02,640.68,2582.49,Europe,1789-07-14 GBR,UK,66.44,242.5,2631.23,Europe,(missing) ITA,Italy,60.36,301.34,1943.84,Europe,(missing) ARG,Argentina,44.94,2780.4,637.49,S.America,1816-07-09 DZA,Algeria,43.38,2381.74,167.56,Africa,1962-07-05 CAN,Canada,37.59,9984.67,1647.12,N.America,1867-07-01 AUS,Australia,25.47,7692.02,1408.68,Oceania,(missing) KAZ,Kazakhstan,18.53,2724.9,159.41,Asia,1991-12-16 Now, the string '(missing)' in the file corresponds to the nan values from df. ('') : 'nan' '-nan' 'NA' 'N/A' 'NaN' 'null' keep_default_na=False read_csv() . To specify other labels for missing values, use the parameter na_values: >>>>>> pd.read_csv('new-data.csv', index_col=0, na_values='(missing)') COUNTRY POP AREA GDP CONT IND_DAY CHN China 1398.72 9596.96 12234.78 Asia NaN IND India 1351.16 3287.26 2575.67 Asia 1947-08-15 USA US 329.74 9833.52 19485.39 N.America 1776-07-04 IDN Indonesia 268.07 1910.93 1015.54 Asia 1945-08-17 BRA Brazil 210.32 8515.77 2055.51 S.America 1822-09-07 PAK Pakistan 205.71 881.91 302.14 Asia 194708-14 NGA Nigeria 200.96 923.77 375.77 Africa 1960-10-01 BGD Bangladesh 167.09 147.57 245.63 Asia 1971-03-26 RUS Russia 146.79 17098.25 1530.75 NaN 1992-06-12 MEX Mexico 126.58 1964.38 1158.23 N.America 1810-09-16 JPN Japan 126.22 377.97 4872.42 Asia NaN DEU Germany 83.02 357.11 3693.20 Europe NaN FRA France 67.02 640.68 2582.49 Europe 1789-07-14 GBR UK 66.44 242.50 2631.23 Europe NaN ITA Italy 60.36 301.34 1943.84 Europe NaN ARG Argentina 44.94 2780.40 637.49 S.America 1816-07-09 DZA Algeria 43.38 2381.74 167.56 Africa 1962-07-05 CAN Canada 37.59 9984.67 1647.12 N.America 1867-07-01 AUS 1408.68 7692.02 25.47 NaN KAZ . . '( )' 16-12-1991 159.41 2724.90 18.53 .dtypes : >>>>>> df = pd.read_csv('data.csv', index_col=0) >>> df.dtypes COUNTRY object POP float64 AREA float64 CONT object IND_DAY object dtype: The columns with strings and dates ('COUNTRY', 'CONT', and 'IND_DAY') have the data type object. object. Numeric columns contain 64-bit float64 numbers. You can use the parameter dtype to specify the desired data types parse_dates by force using the date: >>>>>> dtypes = {'POP': 'float32', 'AREA': 'float32', 'GDP': 'float32'} >>> df = pd.read_csv('data.csv', index_col=0, dtype=dtypes, ... parse_dates=['IND_DAY']) >>> df.dtypes COUNTRY object POP float32 AREA float32 GDP float32 CONT object IND_DAY datetime64[ns] dtype: object >>> df['IND_DAY'] CHN NaT IND 1947-08-15 USA 1776-07-04 IDN 1945-08-17 BRA 1822-04 09-07 PAK 1947-08-14 NGA 196 0-10-01 BGD 1971-03-26 RUS 1992-06-12 MEX 1810-09-16 JPN NaT DEU NaT FRA 1789-07-16 JPN NaT DEU NaT FRA 1789-0707 14 GBR Na ITA NaT ARG 1816-07-09 DZA 1962-07-05 CAN 1867-07-01 AUS NaT KAZ 1991-12-16 Name : IND_DAY, dtype: datetime64[ns] Now, you have 32-bit floating-point numbers ()float32) as specified with dtype. These are slightly different from the original 64-bit numbers due to their less accurateness. The values in the last column are considered as dates and have datetime64 data type. That's why the NaN values in this column are replaced with NaT. Now that you have a real date, you can save them in the format you like: >>>>> df = pd.read_csv('data.csv', index_col=0, parse_dates=['IND_DAY']) >>> df.to_csv('formatted-data.csv', date_format='%B%d, %Y') here you have specified the date_format parameter to '%B%d,%Y'. You can expand the following code block to see the resulting file: ,COUNTRY,POP,AREA,GDP,CONT,IND_DAY CHN,China,2019.72,9596.96,12234.78,Asia, IND,India,1351.16,3287.26,2575.67,Asia,August 15, 1947 USA,US,US,US,329.74,9833.52,19485.39,N.America,July 04, 1776 IDN,Indonesia,268.07,1910.93,1015.54,Asia,August 17, 1945 BRA,Brazil,210.32,851 5.77,2055.51,S.America,September 07, 1822 PAK,Pakistan,205.71,881.91,302.14,Asia,August 14 , 1947 NGA ,Nigeria,200.96,923.77,375.77,Africa,October 01, 1960 BGD,Bangladesh,167.09,147.57,245.63,Asia,March 26, 1971 RUS,Russia,146.79,17098.25,1530.75,,June 12, 1992 MEX,Mexico,126.58,1964.38,1158.23,N.America,September 16, 1810 JPN,Japan,126.22,377.97,4872.42,Asia, DEU,Germany,83.02,357.11,3693.2,Europe, FRA,France,67.02,640.68,2582.49,Europe,July 14, 1789 GBR,UK,66.44,242.5,2631.23,Europe, ITA,Italy,60.36,301.34,1943.84,Europe, ARG,Argentina,44.94,2780.4,637.49,S.America,July 09, 1816 DZA ,Algeria,43.38,2381.74,167.56 ,Africa,July 05, 1962 CAN,Canada,37.59,9984.67,1647.12,N.America,July 01, 1867 AUS,Australia,25.47,7692.02,1408.68,Oceania, KAZ,Kazakhstan,18.53,2724.9,159.41,Asia,December 16, 1991 Date format now varies. The format '%B%d,%Y' means that the date will first display the full name of the moon, then the day after that comma, and finally the full year. There are a few other optional parameters that you can .to_csv(): sep shows a separator of values. Dehing represents a dehing separator. Encoding a collection of encoding files. Header specifies if Whether you want to write column labels in the file or not. Here's how you passed the argument for September header: >>>>>> s = df.to_csv(sep=';', header=False) >>> print(s) CHN; China;1398.72;9596.96;12234.78; Asia; IND;India;1351.16;3287.26;2575.67; Asia;1947-08-15 USA; US;329.74;9833.52;19485.39; N.America;1776-07-04 IDN;Indonesia;268.07;1910.93;1015.54; Asia;1945-08-17 BRA; Brazil;210.32;8515.77;2055.51; S.America;1822-09-07 PAK; Pakistan;205.71;881.91;302.14; Asia;1947-08-14 NGA; Nigeria;200.96;923.77;375.77; Africa;1960-10-01 BGD; Bangladesh;167.09;147.57;245.63; Asia;1971-03-26 RUS; Russia;146.79;17098.25;1530.75;; 1992-06-12 MEX; Mexico;126.58;1964.38;1158.23; N.America;1810-09-16 JPN; Japan;126.22;377.97;4872.42; Asia; Germany;83.02;357.11;3693.2; Europe; France;67.02;640.68;2582.49; Europe;1789-07-14 GBR;UK;66.44;242.5;2631.23; Europe;ITA;Italy;60.36;301.34;1943.84; Europe; Citadel; Argentina;44.94;2780.4;637.49; S.America;1816-07-09 DZA; Algeria;43.38;2381.74;167.56; Africa;1962-07-05 CAN; Canada;37.59;9984.67;1647.12; N.America;1867-07-01 AUS; Australia;25.47;7692.02;1408.68;Oceania; KAZ; Kazakhstan;18.53;2724.9;159.41; Asia;1991-12-16 The data is separated with a semicolon (';') because you've specified sep=';'. Also, from the time you back the header =False, you will see your data without the header row of column names. Pandas read_csv() function has many additional options for managing lost data, working with date and time, quotes, encoding, handling errors, and more. For example, if you have a file with a data column and want to get a series object instead of DataFrame, then you can press Pass=true to read_csv(). You will learn later about data compression and compression, as well as how to skip rows and columns. Irana stands for JavaScript object notation. Plain text files are files that are used to exchange data, and humans can read them easily. They comply with ISO/IEC 21778:2017 and ECMA-404 standards and use the extension. Python and pandas work well with academic files, as the Python Library offers internal support for them. You can transfer data from your DataFrame to a forum file with .to_json(). Start again by creating a DataFrame object. Use dictionary data that keeps data from countries, and then apply .to_json(): >>>>>df = pd. DataFrame(data=data). T >>> df.to_json('data-columns.miss') This code generates the data-columns.ms file. You can expand the following code block to see how this file should look: data-columns. . : >>>>>> df.to_json('data-index.', orient='index') .'' . You should get a new file data-index.. : {CHN:{COUNTRY:China,POP:1398.72,AREA:9596.96,GDP:12234.78,CONT:Asia,IND_DAY:null},IND:{COUNTRY:India,POP:1351.16,AREA:3287.26,GDP:2575.67,CONT:Asia,IND_DAY:1947-08-15},USA:{COUNTRY:US,POP:329.74,AREA:9833.52,GDP:19485.39 CONT:N.America,IND_DAY:1776-07-04},IDN: {COUNTRY:Indonesia,POP:268.07 ,AREA:1910.93 GDP:1015.54,CONT:Asia,IND_DAY:1945-08-17},BRA:{COUNTRY:Brazil,POP:210.32,AREA:8515.77,GDP:210.32,AREA:8515.77,GDP:22055.51,CONT:S.America,IND_DAY:1822-09-07},PAK:{COUNTRY:Pakistan,POP:205.71,AREA:881.91,GDP:302.. 14,CONT:Asia,IND_DAY:1947-08-14},NGA:{COUNTRY:Nigeria,POP:200.96,AREA:923.77,GDP:375.77,CONT. Africa,IND_DAY:1960-10-01},BGD:{COUNTRY:Bangladesh,POP:167.09 ,AREA:147.57,GDP:245.63,CONT:Asia IND_DAY:1971-03-26},RUS:{COUNTRY:Russia,POP:146.79,AREA:17098.25,GDP:1530.75,CONT:null,IND_DAY:1992-06-12},MEX:{COUNTRY:Mexico,POP:126.58,AREA:1964.38,GDP:1158.23,CONT:N.. America,IND_DAY:1810-09-16},JPN:{COUNTRY:Japan,POP:126.22,AREA:377.97,GDP:4872.42,CONT:. Asia,IND_DAY:null},DEU:{COUNTRY:Germany,POP:83.02,AREA:357.11,GDP:3693.2,CONT:Europe,IND_DAY:null},FRA : {COUNTRY:France,POP:67.02,AREA:640.68,GDP:2582.49,CONT:Europe,IND_DAY:1789-07-14},G {COUNTRY:UK,POP:66.44,AREA:242.5,GDP:2631.23,CONT:Europe,IND_DAY:null},ITA:{COUNTRY:Italy,POP :60.36,AREA:301.34,GDP:1943.84,CONT:Europe,IND_DAY:null},ARG :{COUNTRY:Argentina,POP:44.94,AREA::2780.4,GDP:637.49,CONT:S.America,IND_DAY:1816-07-09},DZA:{COUNTRY:Algeria,POP:43.38 AREA:2381.74,GDP:167.56,CONT:Africa,IND_DAY:1962-07-05},CAN:{COUNTRY:Canada,POP:37 .59,AREA:9984.67,GDP:1647.12,CONT:N.America,IND_DAY:1867-07-01},AUS:{COUNTRY:Australia POP:25.47,AREA:7692.02,GDP:1408.68,CONT:Oceania,IND_DAY:null},KAZ:{COUNTRY:Kazakhstan ,POP:18 .53,AREA:2724.9,GDP:159.41,CONT:Asia,IND_DAY:1991-12-16}} data-index. . . 'records': >>>>>> df.to_json('data-records., orient='records') data-records.. : data-records.. It has a list with a dictionary for each row. Row tags are not written. You can split another interesting file structure with East ='Split': >>>>> df.to_json('data-split. you can expand the following code block to see how this file should look: {column:[COUNTRY,POP,AREA,GDP,CONT,IND_DAY],index:[CHN,IND,USA,IDN,BRA,PAK,NGA,BGD,RUS,MEX,JPN,DEU,FRA ,GBR,ITA,ARG,DZA,CAN,AUS,KAZ],data:[China,1398.72,9596.96,12234.78,, [China,1398.72,9596.96,12234.78,. Asia,null],[India,1351.16,3287.26,2575.67,Asia,1947-08-15],[US,329.74,9833.52,52,05 19485.39,N.America,1776-07-04],[Indonesia,268.07,1910.93,1015.54,Asia,1945-08-17] ,[Brazil,210.32,8515.77,2055.51,S.America,1822-09-07],[Pakistan,205.71,881.91,302.14,Asia,1947-08-14],[Nigeria,200.96,923.77,375.77,Africa,1960-10-01],[Bangladesh,167.09,147.57,245.63,Asia,1971-03-26],[Russia,146.79,17098.25,1530.75,null,1992-06-12],[Mexico,126.58,1964.38,1158.23,N.America,1810-0916],[Japan,126.22,377.97,4872.42,Asia,null],[Germany,83.02,357.11,3693.2,Europe,null],[France,67.02,640.68,2582.49,Europe,1789-07-14],[UK,66.44,242.5,2631.23,Europe,null],[Italy,60.36,301.34,1943.84,Europe ,null],[Argentina,44.94,2780.4,637.49,S.America,1816-07-09],[Algeria,43.38,2381.74,16 7.56,Africa,1962-07-05],[Canada,37.59,9984.67,1647.12,N.America,1867-07-01],[Australia ,25.47,7692.02,1408.68,Oceania,null],[Kazakhstan,18.53,2724.9,159.41,Asia,1991-12-12 16]]} data-split.Letter contains a dictionary which has the following lists: column names label rows of internal lists (two-dimensional sequence) that keep the data values if you do not provide values for the optional parameter path_or_buf that defines the file path, then .to_json() a global organization string to Instead of writing the results, return to a file. This behavior is treated .to_csv(). There are other optional parameters that you can use. For example, you can set the =False indicator to predict how to save row labels. You can carefully double_precision, and date date_format date_unit. These last two parameters are particularly important when you have time series among your data: >>>>>gt; df = pd. DataFrame(data=data). T >>> df['IND_DAY'] = pd.to_datetime(df['IND_DAY']) >>> df.dtypes COUNTRY OBJECT POP OBJECT AREA GDP OBJECT CONT OBJECT IND_DAY datetime64[ns] dtype: object >>> df.to_json('data-time.madam') In this example, you have created dataFrame from dictionary data and used to_datetime() to convert values in the last column to datetime64. You can expand the following code block to see the resulting file: . date_format ' , . . '' ' date_format='iso', ISO 8601 . date_unit : >>>>>> df = pd. DataFrame(data=data). T >>> df['IND_DAY'] = pd.to_datetime(df['IND_DAY']) >>> df.to_json('new-data-time.date_format', date_format='iso', date_unit='s') : {COUNTRY:{CHN:China,IND:India,USA:US,IDN:Indonesia,BRA:Brazil,PAK:Pakistan,NGA:Nigeria,BGD Bangladesh,RUS:Russia,MEX:Mexico,JPN:Japan,DEU:Germany,FRA:France,GBR :UK,ITA:Italy,ARG:Argentina,DZA:Algeria,CAN :Canada,AUS:Australia,KAZ:Kazakhstan},POP: {CHN:1398.72,IND:1351.16,USA:329.74,IDN:268.07,BRA:210.32,PAK:205.71,NGA:200.96,BGD:167.09,RUS:146.79,MEX:126.58,JPN:126.22,DEU:83.02,FRA:6 7.02,GBR:66.44,ITA:60.36,ARG:44.94,DZA:43.38,CAN:37.59,AUS:25.47,KAZ:18.53},AREA:{CHN:9596.96,IND:3287.26,USA:9833.52 ,IDN:1910.93,BRA:8515.77,PAK:881.91,NGA:923.77 BGD:147.57,RUS:17098.25,MEX:1964.38,JPN:377.97,DEU:357.11,FRA:640.68,GBR:242.5,ITA:301 .34,ARG:2780.4,DZA:2381.74,CAN:9984.67,AUS:7692.02,KAZ:2724.9},GDP:{CHN:12234.78,IND:2575.. 67,USA:19485.39,IDN:1015.54,BRA:2055.51,PAK:302.14,NGA:375.77,BGD:245.63,RUS:1530.75,MEX:1158.23,JPN:4872.42,DEU:3693.2 ,FRA:2582.49,GBR:2631.23,ITA:1943.84,ARG:637.49,DZA:167.56 CAN:1647.12,AUS:1408.68,KAZ:159.41},CONT: {CHN:Asia,IND:Asia,USA:N.America,,IDN:Asia,BRA:S.America,PAK:Asia,NGA:Africa,BGD:Asia,RUS:null,MEX:N.America,JPN:Asia,DEU:EuropeFRA:Europe,GBR:Europe,ITA:Europe,ARG:S.America,DZA:Africa,CAN:N.America,AUS:Oceania,KAZ:Asia} ,IND_DAY {CHN:null,IND:1947-08-15T00:00:00Z,USA:1776-07-04T00:00:00Z ,IDN:1945-08-17T00:00:00Z,BRA:1822-09-07T00:00:00Z,PAK:1947-08-14T00:00:00Z,NGA:1960-10-01T00:00:00Z,BGD:1971-03-26T00:00:00Z,RUS:1992-06-12T00:00:00Z,MEX:1810-0916T00:00:00Z,JPN:null,DEU:null,FRA:1789-07-14T00:00:00Z,GBR:null,ITA:null,ARG:1816-07-09T00:00:00Z,DZA:1962-07-05T00:00:00Z,CAN:1867-07-01T00:00:00Z,AUS:null,KAZ:1991-12-16T00:00:00Z}} The dates in the resulting file are in the ISO 8601 format. READ_JSON(): >>>>>> df = pd.read_json('data-index.', orient='index', ... convert_dates=['IND_DAY']) convert_dates parse_dates CSV . . : encoding encoding . convert_dates keep_default_dates. dtype precise_float. NumPy numpy=True. . HTML plaintext . .html .htm. HTML lxml html5lib HTML: $pip lxml html5lib Conda : $ conda lxml html5lib , DataFrame HTML .to_html(): >>>df = pd . DataFrame(data=data). T df.to_html('data.html') .html. : <table border=1 class=dataframe><thead><tr style=text-align: right;> <th></th> <th></th> <th></th> <th></th> <th> </th> <th>CONT</th> <th>IND_DAY</th></tr></thead><tbody><tr><th>CHN</th><td></td><td>1398.72</td><td>9596.96</td><td>12234.8</td><td></td><td></td></tr><tr><th>IND</th><td ></td><td>1351.16</td><td>3287.26</td><td>2575.67</td><td></td><td>1947-08-15</td></tr><tr> <th> </th><td></td><td>329.74</td><td>9833.52</td><td>19485.4</td><td>.</td><td>1776-07-04</td></tr><tr><th>IDN</th><td></td><td>268.07</td><td>1910.93</td><td>1015.54</td><td></td><td>1945-08-17</td></tr><tr><th >BRA</th><td></td><td>210.32</td><td>8515.77</td><td>2055.51</td><td>.</td><td>1822-09-07</td></tr><tr><th>PAK</th><td></td><td>205.71</td><td>881.91</td><td>302.14</td><td></td><td>1947-0814</td></tr><tr><th>NGA</th><td></td><td>200.96</td><td>923.77</td><td>375.77</td><td></td><td>1960-10-01</td></tr> <tr><th> </th><td></td><td>126.22</td><td>377.97</td><td>4872.42</td><td></td><td></td></tr><tr><th>DEU</th><td></td><td>83.02</td><td>357.11</td><td>3693.2</td><td></td><td></td></tr><tr><th>FRA</th><td></td><td>67.02</td><td>640.68</td><td>2582.49</td><td></td><td>178907-14</td></tr> <tr> <th> </th><td></td><td>66.44</td><td>242.5</td><td>2631.23</td><td></td><td></td></tr><tr><th></th><td></td><td>60.36</td><td>301.34</td><td>1943.84</td></tr></tbody></table> </th><td></td><td>66.44</td><td>242.5</td><td>2631.23</td><td></td><td></td></tr><tr><th> </th><td></td><td>60.36</td><td>301.34</td><td>1943.84</td></tr></tbody></table> <td></td><tr> <th></th><td></td><td>44.94</td><td>2780.4</td><td>637.49</td><td>.</td><td>1816-07-09</td></tr><tr><th>DZA</th><td></td><td>43.38</td><td>2381.74</td><td>167.56</td><td></td><td>1962-0705</td></tr><tr><th>CAN</th><td></td><td>37.59</td><td>9984.67</td><td>1647.12</td><td>.</ td><td>1867-07-01</td></tr><tr><th>AUS</th><td>australia</td>>>td>td>25.25 47</td><td>7692.02</td><td>1408.68</td>>oceania>oceania</td>><td>bread<td>bread<td>gt;</tr><tr><th>kaz</th><td>kazakhstan</td>>td>18.53</td>>td>2724.9;lt;/td>gt;<td>159.41</td>>>asia</td>>>td>1991-12-16</td>>>/tr>This file shows the contents of DataFrame well. However, notice that you have earned a full web page. You only output data that corresponds to df in HTML format. .to_html() If you buf does not provide an optional parameter, which will write the buffer to note, it will not create a file. If you leave this parameter, then your code will return a string as .to_csv() .to_json(). Here are some other optional parameters: Header determines whether to save the column name. The indicator determines whether to save row labels or not. Class Assign Cascading Sheet Style (CSS) Class. render_links specifies whether address addresses are converted to HTML links. table_id css to the table label. Escape decides whether to convert characters <,>, and & to html strings is secure. You use parameters like these to specify different aspects of the resulting files or strings. You can create a DataFrame object from a suitable HTML file using read_html(), which returns a DataFrame sample or a list of them: >>>>>>> df = pd.read_html('data.html', index_col=0, parse_dates=['IND_DAY']) it's very similar to what you did when reading CSV files. You also have parameters that help you work with history, lost value, accuracy, coding, HTML parsing, and more. You have already learned how to read and write Excel files with Panda. However, there are a few other options worth considering. For one, when .to_excel(), you can specify the target worksheet name with optional parameter sheet_name: >>>>>> df = pd. DataFrame(data=data). T >>> df.to_excel('data.xlsx', sheet_name='COUNTRIES') Here, you create a file data.xlsx with a worksheet called COUNTRIES that stores the data. The string 'data.xlsx R arguments for excel_writer parameter that defines the name of the Excel file or its path. Optional Parameters And startcol is both default to 0 and shows the top left-hand cell where the data should start writing: >>>>>> df.to_excel('Changed Data.xlsx', sheet_name='Countries', ... Startrow=2, startcol=4) here you specify that the table should start in the third row and the fifth column. You also use zero-based indexing, so the third row is referred to by 2 and the fifth column by 4. Now the resulting worksheet looks like </,>: As you can see, the table starts in Third row 2 and fifth column E. . read_excel() also has an optional sheet_name specifies which sheets to read when uploading data. It can take in one of the following values: zero index based worksheet name worksheet index list of indicators or name to read multiple sheets worth none to read all sheets here how you use this parameter in your code: >>>>>> df = pd.read_excel('data.xlsx', sheet_name=0, index_col=0, ... parse_dates=['IND_DAY']) >>> df = pd.read_excel('data.xlsx', sheet_name='COUNTRIES', index_col=0, ... parse_dates=['IND_DAY']) Both statements above create a DataFrame because the sheet_name have the same values. In both cases sheet_name=0 and sheet_name='COUNTRIES' refer to the same worksheet. The parse_dates=['IND_DAY'] tells pandas to try to consider the values of this column as date or time. There are other optional parameters you can use .read_excel() and .to_excel() to determine excel engine, encoding, way to handle lost and infinite values, methods for writing column names and row labels, and the like. Pandas IO tools can also read and write databases. In this next example, we will return your data to a database called data.db. To get started, you'll need a SQLAlchemy package. For more information about it, you can read orm formal training. You also need a database driver. Python has a built-in driver for SQLite. You can install SQLAlchemy with pips: you can also install it with Conda: $conda install sqlalchemy when you have SQLAlchemy installed, Import create_engine() and create a database engine: >>>>>> sqlalchemy import engine create_engine >>> = create_engine('sqlite:///data.db', echo=False) Now that you launch everything, the next step is to create a DataFrame object. It's convenient to specify data types and apply .to_sql(). >>>>>> dtypes = {'POP': 'float64', 'AREA': 'float64', 'GDP': 'float64', ... 'IND_DAY': 'datetime64'} >>> df = pd. DataFrame(data=data). T.astype(dtype=dtypes) >>> df.dtypes COUNTRY object POP float64 AREA float64 GDP float64 CONT object IND_DAY datetime64[ns] dtype: object .astype() is a very convenient method you can use to set multiple data types at once. Once you have created your DataFrame, you can store it in the database with .to_sql(): >>>>>> df.to_sql('data.db', con=engine, index_label='ID') from the con parameter to specify the database connection or engine you want to use. The optional index_label specifies how to call a database column with row labels. You often see it in value ID, ID, or ID. You have database data .db with a single table that looks like this: the first column contains the row label. To delete their writing in the database, pass=False to .to_sql(). Other columns are related to DataFrame columns. There are a few more optional parameters. As You can use the outline to specify the database and dtype to determine the types of database columns. You can also if_exists, which says what to do if there is a database with the same name and path already: if_exists='Failure' will raise a ValueError and it is the default. if_exists='Replace' releases the table and inserts new values. if_exists='append' inserts new values into the table. You can use data from databases with read_sql(): >>>>> df = pd.read_sql('data.db', con=engine, index_col='ID') >>> df COUNTRY POP AREA GDP CONT IND_DAY ID CHN China 1398.72 9596.96 12234.78 Asia NaT IN D India 1351.16 3287.2 6 2575.67 Asia 1947-08-15 USA 329.74 9833.52 19485.39 N.America 1776-07-04 IDN Indonesia 268.0 1910.93 1015.54 Asia 1945-08-17 BRA Brazil 210.32 8515.77 2055.51 S.America 1822-09-07 PAK Pakistan 1822-09-07 PAK Pakistan 2055.51 205.71 881.91 302.14 Asia 1947-08-14 NGA Nigeria 200.96 923.77 375.77 Africa 1960-10-01 BGD Bangladesh 167.09 147.57 245.63 Asia 1971-03-26 RUS Russia 146.79 17098.25 1530.75 None 1992-06-12 MEX Mexico 126.58 1964.31 158.23 N.America 1810-09-16 JPN Japan 126.22 377.97 4872.42 Asia NaT DEU Germany 83.02 357.11 3693.20 Europe NaT FRA France 67.02 640.68 258 2.49 Europe 1789-07-14 GBR UK 66.44 242.50 2631.23 Europe NaT ITA Italy 60.36 301.34 1943.84 Europe NaT Arg Argentina 44.94 2780.40 637 49 S.America 1816-07-09 DZA Algeria 43.38 2381.74 167.56 Africa 1962-07-05 CAN Canada 37.59 9984.67 1647.12 N.America 1867-07-01 AUS Australia 25.47 7692.02 1408.68 Oceania NaT KAZ Kazakhstan 18.53 2724.90 159.41 Asia 1991-12-16 parameter index_col specified column names with row tag. Note that this will insert an extra row after the header starting with id. You can fix this behavior with the following line of code: >>>>> df.index.name = none >>> df Country Pop GDP Cont Region IND_DAY CHINA CHN 1398.72 9596.96 12234.78 Asia NaT IND India 1351.16 3287.2 6 257 5.67 Asia 1947-08-15 USA 329.74 9833.52 19485.39 N.America 1776-1776 07-04 IDN Indonesia 268.07 1910.93 1015.5 4 Asia 1945-08-17 BRA Brazil 210.32 8515.77 2055.51 S.America 1822-09-07 PAK Pakistan 205.71 881.91 302.14 Asia 1947-07 PAK Pakistan 205.71 881.91 302.14 Asia 1947-07 08-14 NGA Nigeria 200.96 923.96.08 77 375.77 Africa 1960-10-01 BGD Bangladesh 167.09 147.57 245.63 Asia 197 1-03-26 RUS Russia 146.79 17098.25 1530.75 None 1992-06-12 MEX Mexico 126.58 1964.38 1158.23 N.America 1810-09-16 JPN Japan 126.22 377. 97 4872.42 Asia NaT DEU Germany 83.02 357.11 3693.20 Europe NaT FRA France 67.02 640.68 2582.49 Europe 1789-07-14 GBR UK 66.44 242.49 50 2631.23 Europe NaT ITA Italy 60.36 301.34 1943.84 Europe NaT Citadel Argentina 44.94 2780.40 637.49 S.America 1816-07-09 DZA Algeria 43.38 2381.2381 74 167.56 Africa 1962-07-05 Can Canada 37.59 9984.67 1647.12 N.America 1867-07-01 AUS Australia 25.01 47 7692.02 1408.68 Oceania NaT KAZ Kazakhstan 18.53 2724.90 159.41 Asia 1991-12-16 Now you have the same DataFrame object before. Note that The continent for Russia now has none instead of bread. If you want to fill the missing values with bread, then you can use .fillna(): >>>>> df.fillna(value=float('nan'), .fillna() replaces all lost values with whatever you pass to value. Here, you pass the float ('nan'), which says to fill all lost values with bread. Also note that you have to pass parse_dates=['IND_DAY'] to read_sql(). That's because your database was able to recognize that the last column contains dates. However, you can parse_dates if you'd like. You will get the same results. There are other functions that you can use to read databases, such as read_sql_table() and read_sql_query(). Feel free to try them out! Pickles is the act of converting python objects into byte streams. Open the reverse process. Pickled Python files are binary files that hold data and a hierarchy of Python objects. They usually have .pickle or .pkl extensions. You can find your DataFrame in a pickled file with .to_pickle(): >>>>> dtypes = {'POP': 'float64', 'AREA': 'float64', 'GDP': 'float64', ... 'IND_DAY': 'datetime64'} >>> df = pd. DataFrame(data=data). T.astype(dtype=dtypes) >>> df.to_pickle('data.pickle') like you did with databases, it can be convenient first to specify data types. Then, you will find a data.pickle file to contain your data. You can also move some of an answer to the optional parameter protocol that specifies the pickle protocol. You can extract data from a pickled file with read_pickle(): >>>>> df = pd.read_pickle('data.pickle') >>> df Country Pop GDP Region CONT IND_DAY CHN China 1398.72 9596.96 12234.78 Asia NaT IND India 135 11.16 135 3287.26 2575.67 Asia 1947-08-15 USA 329.74 9833.52 19485.39 N.America 1776-07-04 IDN Indonesia 268.07 1910.93 1015.54 Asia 1945-08-17 BRA Brazil 1945-08-17 BRA Brazil 210.32 8515.77 2055.51 S.America 1822-09-07 PAK Pakistan 205.71 881.9 1 302.14 Asia 1947-08-14 NGA Nigeria 200.96 923.77 375.77 Africa 1960-10-01 BGD Bangladesh 167.09 147.57 245.63 Asia 1971-03-2 6 RUS Russia 146.79 17098.25 1530.75 NaN 1992-06-12 MEX Mexico 126.58 1964.38 1158.23 N.America 1810-09-16 JPN Japan 126.22 377.97 4872.42 Asia NaT DEU Germany 83.02 357.11 3693.20 Europe NaT FRA France 67.02 640.68 2582.49 Europe 1789-07-14 GBR UK 6 6.44 6 242.50 2631.23 Europe NaT ITA Italy 60.36 301.34 1943.84 Europe NaT Citadel Argentina 44.94 2780.40 637.49 S.America 1816-07-09 DZA Algeria 43.43. 38 2381.74 167.56 Africa 1962-07-05 CAN Canada 37.59 9984.67 1647.12 N.America 1867-07-01 AUS Australia 25.47 7692.02 1408.68 Oceania NaT KAZ Kazakhstan 18.53 2724.90 159.41 Asia 1991-12-16 read_pickle() returns the DataFrame with the stored data. You can also check the data types: >>>>>> df.dtypes country object pop float64 float64 GDP float64 CONT object IND_DAY datetime64[ns] dtype: These objects are the ones you specified before using .to_pickle(). As a word of caution, you should always be careful of loading pickles from unreliable sources. This could be dangerous! When You open an unreliable file, can run arbitrary code on your device, gain remote access to your computer, or otherwise exploit your device in other ways. Way. Way.

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