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29. na_values | Handling Missing Values in Pandas | Part 2

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12.11.2020

Handling Missing Values in Pandas | Part 2 | na_values Github Link: 🤍 If you enjoy these tutorials, like the video, and give it a thumbs up, and also share these videos with your friends and families if you think these videos would help him. Please consider clicking the SUBSCRIBE button to be notified of future videos.

[Pandas Tutorial] how to check NaN and replace it (fillna)

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25.02.2018

You can practice with below jupyter notebook. 🤍

What's a NaN value in a Pandas DataFrame?

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17.07.2020

Step by step explanation (with examples) of what's a NaN value in Pandas DataFrame and the various parameters ('keep_default_na', 'na_values', 'na_filter') associated with NaNs. How to load a csv file into a Pandas DataFrame: 🤍 Documentation of 'read_csv' function : 🤍

Python Tutorial: Importing & exporting data

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04.04.2020

Want to learn more? Take the full course at 🤍 at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. - Now, let's extend our skills for reading DataFrames from files. We'll use a comma-separated-values file of sunspot observations collected from SILSO (Sunspot Index & Long-term Solar Observations). The entries date back to the 19th century with over seventy thousand rows. The read_csv function requires a string describing a filepath as input. We read into a DataFrame sunspots. Using info, we see the DataFrame has mostly integer or floating-point entries. Notice the index of the DataFrame (the row labels) are of type RangeIndex (just integers). Let's use the accessor dot iloc to view a slice of the middle of the DataFrame. We can see some of the problems: the column headers don't make sense and there are many perplexing negative one entries in one column. What's going on? First, the CSV file does not provide column labels in a header row. The column meanings can be gleaned from SILSO's website. Columns zero through two give the Gregorian date, column three is a decimal value of the date, column four is the number of sunspots observed that day, and column five indicates confidence in the measurement (zero or one). Second, the negative ones in column four denote missing values; we need to take care of those. Finally, as written, the dates are awkward for computation, a common problem with CSV files. Let's tidy this up. Using header equals None prevents pandas from assuming the first line of the file gives column labels. Alternatively, an integer header argument gives the row number (indexed from 0) where column labels actually are and the data begins. Notice, now, the columns & rows are assigned integers from 0 as labels. We can explicitly label the columns with the option names. We define a list of strings col_names to label the columns properly. We can also read the negative one entries in the sunspots column as NaN or Not-a-Number (sometimes called a null value). We do this with the na_values keyword. We try "na_values equals quote minus one quote" but the sunspots column still has entries of negative one. Looking at the original CSV file reveals the problem; there are space characters preceding minus ones throughout column 4. Thus, we use "na_values equals quote space minus one quote" and it works. Notice the sunspot numbers are now floating-point values (not integers). Several strings can represent invalid or missing values. To do so, we use a list of strings with na_values or a dictionary mapping column names to lists of strings. Note it is possible to use distinct patterns for null values in different columns using dictionaries; see the documentation for examples. Finally, we notice the year, month, and date columns can be loaded in a better way. The parse_dates keyword in read_csv infers dates intelligently. We use a list of lists of column positions (indexed from 0) to inform read_csv which columns hold the dates. Sure enough, there's a new column of datetimes named year_month_day amalgamating the three original columns. In fact, using the info method, we see the year_month_day column has entries of type datetime64. We'll learn more about datetimes when studying time series; they are invaluable for many time-based computations. Also, the sunspots column has about 69 thousand non-null entries. The DataFrame still lacks meaningful row labels in the Index. The year_month_day column can be assigned as the DataFrame index using the index attribute. Similarly, assigning date to the index's name attribute gives more concise label. Notice we still have the year_month_day and dec_date columns. To get rid of them, we list the meaningful column names and extract them. The result is a more compact DataFrame with only the meaningful data. What if we want to share this new DataFrame with others? The sensible thing would be to export our compact DataFrame to a new CSV file. The method to_csv does the job for us. Like read_csv, the method to_csv has a host of options to fine-tune its behavior. we can even export to Excel using to_excel. Try some exercises now to practice loading and saving DataFrames. #Python #PythonTutorial #DataCamp #pandas #Foundations #Importing #exporting #DataFrames

Python Pandas Tutorial 4: Read Write Excel CSV File

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04.02.2017

This tutorial covers how to read/write excel and csv files in pandas. We will cover, 1) Different options on cleaning up messy data while reading csv/excel files 2) Use convertors to transform data read from excel file 3) Export only portion of dataframe to excel file Topics that are covered in this Python Pandas Video: 0:00 Introduction 1:26 Read CSV file using read_csv() method 2:39 Skip rows in dataframe using "skiprows" 4:44 Import data from CSV file with "null header" 6:28 Read limited data from CSV file 7:19 Clean up messy data from file "not available" and "n.a." replace with "na_values" 9:01 Supply dictionary for replace with "na_values" 11:40 Write dataframe into "csv" file with "to_csv() method" 15:27 Read excel file using read_excel() method 18:03 Converters argument in read_excel() method 20:17 Write dataframe into "excel" file with "to_excel() method" 22:56 Use ExcelWritter() class 25:13 All properties for Read Write Excel CSV File Do you want to learn technology from me? Check 🤍 for my affordable video courses. Very Simple Explanation Of Neural Network: 🤍 Code (jupyter notebook link): 🤍 Next Video: Python Pandas Tutorial 5: Handle Missing Data: fillna, dropna, interpolate: 🤍 Popular Playlist: Complete python course: 🤍 Data science course: 🤍 Machine learning tutorials: 🤍 Pandas tutorials: 🤍 Git github tutorials: 🤍 Matplotlib course: 🤍 Data structures course: 🤍 Data Science Project - Real Estate Price Prediction: 🤍 To download csv and code for all tutorials: go to 🤍 click on a green button to clone or download the entire repository and then go to relevant folder to get access to that specific file. 🌎 My Website For Video Courses: 🤍 Need help building software or data analytics and AI solutions? My company 🤍 can help. Click on the Contact button on that website. Facebook: 🤍 Twitter: 🤍

In Hindi: What's a NaN value in a Pandas DataFrame?

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23.07.2020

In Hindi: Step by step explanation (with examples) of what's a NaN value in Pandas DataFrame and the various parameters ('keep_default_na', 'na_values', 'na_filter') associated with NaNs. How to load a csv file into a Pandas DataFrame: 🤍 Documentation of 'read_csv' function : 🤍 Above concept in English: 🤍

How to use pandas read_csv function || Python read_csv pandas || pd.read_csv In 5 Min.

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28.05.2020

#Python #Pandas #Topictrick Python Tutorial - How to load CSV file into Pandas Data frame. Welcome back to another exciting tutorial on “How to load CSV file into Pandas Data frame”. In this Python tutorial, you’ll learn the pandas read_csv method. The method read and load the CSV data into Pandas Dataframe. You’ll also learn various optional and mandatory parameters of the pandas read_csv method syntax. In the end, you will see the live coding demo for better understanding. Let’s begin our tutorial with an introduction to the CSV file, followed by an introduction to Python Pandas and Pandas Dataframe. Introduction to CSV file. CSV stands for a Comma-separated (CSV) file. It’s a text file in which each field value is delimited by “,” (comma). These files are generally used to store data into a tabular format. Pandas Dataframe. A pandas data frame is an object, that represents data in the form of rows and columns. Python data frames are like excel worksheets or a DB2 table. A pandas data frame has an index row and a header column along with data rows. Pandas Read_CSV Syntax: # Python read_csv pandas syntax with # minimum set of parametrs. pd.read_csv(filepath, sep=',', dtype=None, header=None, skiprows=None, index_col=None, skip_blank_lines=True, na_filter=True) Now, let’s understand the importance of these parameters. filepath: The filepath parameter specifies the file location. A local file could be passed as://localhost/path/to/table.csv. sep: The sep parameter specifies the delimiter which is used in the file. dtype: The dtype parameter specifies the column datatype (i.e. integer or float). header: The header parameter specifies the column header row. A list of values can be used while reading a CSV file. skiprows: The skiprows parameter use to skip initial rows, for example, skiprows=05 means data would be read from 06th row. index_col: The index_col parameter use to specify the column as the row labels of the data frame. skip_blank_lines: The parameter is used to skip blank lines while reading data from the dataset using read_csv pandas. na_filter: The parameter is used to drop NaN value from the dataset. low_memory: Internally process the file in chunks, resulting in lower memory use while parsing, but possibly mixed type inference. encoding: Encoding to use for UTF when reading/writing (ex. ‘utf-8’). Python Read_CSV Pandas Example. import numpy as np # import numpy as np import pandas as pd # import pandas as pd # Reading and load loan file into df. df_loan = pd.read_csv("loan.csv", sep=",", encoding = "ISO-8859-1", index_col=None, low_memory=False, dtype={'id':np.int32}, nrows=16, skiprows=0) df_loan.head(3) Topictirck Youtube Channel. Website : 🤍topictrick.com Youtube : topictrick Twitter : 🤍 Facebook : 🤍 Linkedin : 🤍 Redditt : 🤍 Topictrick Tutorial : 🤍 Full Syntax: # Python read_csv pandas all parameters list. read_csv(filepath_or_buffer, sep=',', delimiter=None, header='infer', names=None, index_col=None, usecols=None, squeeze=False, prefix=None, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, skipfooter=0, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, iterator=False, chunksize=None, compression='infer', thousands=None, decimal=b'.', lineterminator=None, quotechar='"', quoting=0, doublequote=True, escapechar=None, comment=None, encoding=None, dialect=None, tupleize_cols=None, error_bad_lines=True, warn_bad_lines=True, delim_whitespace=False, low_memory=True, memory_map=False, float_precision=None) Tags: 🤍topictrick

Python pandas Read csv and writing data to a flat file session 5

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28.08.2018

python pandas read_csv parameters , na_values , usecols to_csv how to export data to a flatfile using index and delimiter

Python Tutorial: Reading, inspecting, & cleaning data from csv files

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19.04.2020

Want to learn more? Take the full course at 🤍 at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. - Hi, and welcome to the course "importing and managing financial data in Python"! My name is Stefan Jansen and I'll be your instructor for this course. I have been working in international finance, investment and economic research for over 15 years, and have been using python for data science for over five years. I advice companies on data strategy, machine learning, and artificial intelligence in various industries. In this first video, you will learn more about how to import data from CSV files in Python. When moving data from one format to another, you need to make sure that all information is accurately captured, and nothing gets lost in the process. To illustrate how to address some issues that often arise when you import data, we will use a file with info on companies listed on the AmEx Stock Exchange. This file contains a company’s name and stock ticker, which is the symbol needed to get price and other information about a company from an exchange, its sector, industry and IPO year, that is the year when it started trading on a stock exchange. It also contains the most recent share price, and the market capitalization, which the combined value of all its shares, and the date of the latest update. A quick look at the file reveals a few missing values: they are identified by the string ‘n/a’. You can also see that this CSV file contains three different types of data: 4 columns contain text data, also called ‘strings’ 3 columns contain numeric data, and one column has date information Pandas assigns a different data type to each column, and stores this information in a property called ‘dtype’. The dtype of a column affects how you can use its content in calculation and visualization. In particular, pandas distinguishes between four main dtypes: The dtype object is reserved for columns with text data, or a mix of text and numeric data. They are two numeric data types: int64 is for columns containing whole numbers, represented using 64 bits so that the largest number can be 2 to the power of 64. float 64 is the second numeric data type, reserved for columns containing either decimals, or whole numbers and some missing values. Lastly, the datetime64 dtype is for columns with date and time information. You can use the pandas read_csv() method to import the data. Just tell read_csv() where to find the file, and assign the result to the variable amex. Then, call the dataframe method .info() to display useful information and identify some mismatches. The index has 360 entries, so the DataFrame has 360 rows. As expected, there are eight data columns, but each has 360 valid observations, and no missing data points as you would have expected. Seven columns are of dtype object, ie, text data, and only one has dtype float. Instead, you would have expected 3 numeric end 1 datetime column. Let’s fix the import result. The read CSV function takes several parameters to help you parse a csv file. To deal with missing values, use the parameter na_values. Just pass a string that identifies missing values in the source file, and pandas will replace them with the numpy value np.nan, which stands for not-a-number. This makes sure that calculations with missing values work as expected. The numeric columns now have the correct data types. The IPO Year contains whole numbers, but is also signed the data type float because values are missing for some companies. We are not quite done yet: to parse the date information, use the parameter parse_dates, and pass a list with the names of one or several columns with date information. pandas will then interpret the data correctly, and, as you can see, now all columns have the expected data types. To display the result of your import, use the method .head(): it displays the content of the first few rows. It defaults to the first 5 rows, but you can pass another integers to display fewer or more rows. As you can see, the missing values are now represented as numpy nan values, and the dates are also properly displayed. Now it's time to put these new methods into practice! #DataCamp #PythonTutorial #Importing #Managing #Financial #Data #Python

Python Tutorial: Read data from Excel worksheets

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Want to learn more? Take the full course at 🤍 at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. - Let us now look at how to import data from Excel worksheets. As example, you will be using an Excel workbook with 3 worksheets containing listing information for 3 exchanges: the AmEx Exchange and NASDAQ that you are already familiar with, and also the NYSE. Each sheet contains the same information as the AMEX csv file you have seen before, we just omitted the ‘Last Update’ column. You can use the parameter sheet_name to tell read_excel which worksheet to import. You have several options to import either a single sheet, or multiple sheets simultaneously: you can provide an integer that refers to the position of the worksheet. The number 0 means you want to import the first sheet. You can also refer to a sheet by its name. You can import several sheets at the same time. Just provide a list with the names or positions of the sheets you would like read_excel to import. The result will be a dictionary, where the keys are the sheet names, and the values are DataFrames with the sheet content. Let's look at an example. To read the worksheet for the AmEx exchange, simply provide the label to the read_excel() parameter sheet_name. Note that read_excel() also uses the na_values parameter to parse missing values. If you call the .info() method on the result, you will notice the same output you obtained earlier from the read_csv() method. Let's now import data from two worksheets. Just supply a list with the labels ‘amex’ and ‘nasdaq’ to the sheet_name parameter. The result contained in the variable ‘listings’ is a dictionary that contains two key-value pairs. The keys contain the names of the worksheets, and the values are the corresponding DataFrames. Since listings is a dictionary, you can access the DataFrame with the NASDAQ data by providing the matching key. Once you apply the .info() method to the result, you can view the structure of the data about the listings on this stock exchange. Pandas also allows you to retrieve the sheet names from an Excel workbook. To obtain this information, create an ExcelFile object using the path to an Excel workbook, as illustrated here for the ‘listings.xlsx’ file. One to have created this object, you can access its sheetnames attribute. This attribute contains a list with the names of the worksheets for this workbook. Here we retrieve the list of all the exchange names, and assign it to the variable exchanges. In the next step, you can pass the ExcelFile object to read_excel() to import its content, instead of the path to the file. You can then select the name of the target worksheet from the list stored the exchanges variable. Assigning the resulting DataFrame to the variable NYSE, and calling the method .info() on this DataFrame shows the expected output. Let’s practice your new skills. #DataCamp #PythonTutorial #Importing #Managing #Financial #Data #Python

Python | Read CSV in Pandas

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18.04.2019

In this video, we'll walk through a common process of reading data from a CSV file (outside of your Python code) and constructing a DataFrame from the processed data.

Hindi - What's a NaN value in a Pandas DataFrame?

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17.08.2020

In Hindi: Step by step explanation (with examples) of what's a NaN value in a Pandas DataFrame and the various parameters ('keep_default_na', 'na_values', 'na_filter') associated with NaNs. How to load a csv file into a Pandas DataFrame: 🤍 Documentation of 'read_csv' function : 🤍

Python Tutorial: Handling missing values

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11.04.2020

Want to learn more? Take the full course at 🤍 at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. - In the previous lesson you were introduced to the two null value types that you encounter in python. In this lesson, you will assign null values to the missing values in the dataset! Missing values in a dataset aren't usually left unfilled, they are filled with dummy values like 'NA', '-' or '.' etc. In this lesson, you will learn to detect such missing values as well as replace them with 'NaN'. Let's use the 'college' dataset which contains various details of college students as an example. We'll load data using 'pd.read_csv()' of 'college.csv'. The first step in analyzing the dataset is to read and print a snippet of the dataset. We'll print the head of the 'college' DataFrame. Find that all columns have float values. If you observe clearly, you can see that a few data points are filled with a period! This suggests that missing values might be represented by a period. However, we can confirm this only through further analysis. We'll use the info() method to get a gist of the dataset. Hey, somethings' odd here! All the columns except 'private' are of 'object' type although they are supposed to be float. We can further explore and confirm by finding the unique values in one of the columns. This way we can find any non-numerical values! Let's apply the '.unique()' method on the column 'csat' and sort them using 'np.sort()'. From the output you can clearly observe that '.' is the only string value present. Hence, we need to replace it with 'NaN'. This can be simply achieved while loading the dataset to a DataFrame. You can use the argument 'na_values' in 'pd.read_csv' to specify the values for missing data. If you again check the 'info()' of 'college', you'll find that all the columns are now 'float64' type. This is great! Now, let's consider another dataset to detect hidden missing values. We will use the Pima Indians Diabetes dataset which contains various clinical diagnostic information of the patients from the Pima community. While loading the dataset we can observe 'NaN' values for missing data when you print the head of the DataFrame. As before, let's print the 'info()' of the 'diabetes' DataFrame. They are all 'float' or 'int' type as expected. Further, we can analyze using the 'describe()' method on the 'diabetes' DataFrame. Observe closely. Something very odd here is that the 'BMI' column has a minimum value of 0. But we are aware that BMI cannot be 0. Hence, the 0's must rather be missing values in disguise! To confirm the same, we can filter all the rows where 'BMI' is 0. There are 11 rows which have BMI as 0. They must be missing values. These types of missing values can be tricky as they require some level of domain knowledge. We'll replace these 11 zeros of BMI column with 'NaN' and check again using 'np.isnan()' of diabetes.BMI. Great! Now that we have successfully removed the hidden missing values and replaced them with 'NaN's, let's summarize what we learned in this lesson! We learned to detect missing value characters like '.', detect the inherent missing values within the data like '0' and replace them with NaNs. In the next lesson, you'll dig deeper into analyzing the missing values. But it's now time to practice! #PythonTutorial #DataCamp #Dealing #Missing #Data #Python

Python Pandas Tutorial 21 | How to Rank a DataFrame in Python | Ranking Data in Python

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11.04.2018

Hi guys...in this Pandas Tutorial video I have talked about how you can rank a dataframe in Python Pandas. Ranking is helpful in scenarios like where we want to see the top or bottom n values for a particular column. I have also shown you that how you can sort the rankings to get the data frame in a proper order. Also shown you various rank parameters like pct, axis, na_values etc. to get a complete control over the rank method.

PY203 | Python Pandas Read Write CSV | Camel Academy

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00:15:32
28.07.2020

Check out this video for #Python #Pandas Read Write #CSV 00:45 import pandas 01:25 pandas read_csv 03:15 read_csv skiprows 04:05 read_csv header 05:40 read_csv header names 07:00 read_csv nrows 07:50 read_csv na_values 12:00 data frame columns 13:00 data frame to_csv Also check out the below Play Lists Applied Mathematics 🤍 Data Science Academy 🤍 Programming Python 🤍 Science in Action 🤍 If you like this video then give it a thumbs up and share with your friends, don’t forget to subscribe to the Aspiring Minds Channel if you already haven’t. Also please click on the bell button to be the first to get the channel updates. If you want me to make a video in any topic of your interest then please do let me know. Also Feel free to leave any comments below if you have any questions or comments and I will get back to you soon.

Importer (lire) correctement un fichier Excel avec pandas / Read Excel files with pandas - python

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13.06.2022

Dans cette vidéo, nous allons voir toutes les méthodes pour Importer correctement Excel avec pandas.😎 Nous verrons toutes les étapes d'import avec du sheet_name, header, nrows jusqu'à l'index_col qui nous permettrons d'importer correctement tous types de fichers Excel. 🟨 Lien vers le notebook : 🤍 🟨 Cours H2ES : 🤍 🟨 Discord : 🤍 🟨 ABONNE-TOI EN CLIQUANT ICI : 🤍 00:00 Introduction 01:23 Importation des packages 03:14 Sheet name 05:59 Header 07:35 Converters 09:12 nrows 10:09 Skiprows 11:24 true_values, false_values, na_values 13:08 dtype 13:53 index_col

#Datascience #Python #pandas Python Pandas Tutorial 4: Read Write Excel CSV File

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00:26:40
14.04.2020

Subscribe- Telegram-htts://t.me/Passouts2021 This tutorial covers how to read/write excel and csv files in pandas. We will cover, 1) Different options on cleaning up messy data while reading csv/excel files 2) Use convertors to transform data read from excel file 3) Export only portion of dataframe to excel file Topics that are covered in this Python Pandas Video: 1:26 Read CSV file using read_csv() method 2:39 Skip rows in dataframe using "skiprows" 4:44 Import data from CSV file with "null header" 6:28 Read limited data from CSV file 7:19 Clean up messy data from file "not available" and "n.a." replace with "na_values" 9:01 Supply dictionary for replace with "na_values" 11:40 Write dataframe into "csv" file with "to_csv() method" 15:27 Read excel file using read_excel() method 18:03 Converters argument in read_excel() method 20:17 Write dataframe into "excel" file with "to_excel() method" 22:56 Use ExcelWritter() class 25:13 All properties for Read Write Excel CSV File

attributes of read csv pandas part two

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23.09.2020

Attributes of read_csv() function 1: Sep some csv files are so created that their separator character is different from comma such as semicolon(;) or a pipe symbol (|) etc. to read data from such CSV files you need to specify an additional argument as sep=”separator_character”. If you skip this argument then default separator character (comma) is considered. Syntax pandas.read_csv( file_address , sep=”separator_character”) 2: Names it is used to specify own column headings for dataframe Syntax pandas.read_csv( file_address ,names=[“column_names”]) 3: skiprows skiprows argument can either take a number for number of rows to be skipped from beginning or it take a list of rows numbers to be skipped from CSV while reading data. Syntax pandas.read_csv( file_address ,skiprows=number_of_row(s) ) 4: nrows int, default None Number of rows of file to read. Useful for reading pieces of large files 5: Index_col It creates row index numbers on the basis of values (unique) of column in csv file. Makes passed column as index instead of 0, 1, 2, 3… 6: Header Use pandas read_csv header to specify which line in your data is to be considered as header. For example, the header is already present in the first line of csv file. In this case, we need to either use header = 0 or don't use any header argument. use header=None to explicitly tells people that the csv has no headers df = pd.read_csv(file_path, header=None) 7: Na_values str or list-like or dict, default None 8: Keep_default_na bool, default True If na_values are specified and keep_default_na is False the default NaN values are overridden, otherwise they’re appended to. 9: usecols:- this attribute is used to get specific columns or want a subset of the columns from csv file Syntax df = pd.read_csv(file_path, header=None, usecols=[3,6]) Attributes of read_csv() function 1: Sep some csv files are so created that their separator character is different from comma such as semicolon(;) or a pipe symbol (|) etc. to read data from such CSV files you need to specify an additional argument as sep=”separator_character”. If you skip this argument then default separator character (comma) is considered. Syntax pandas.read_csv( file_address , sep=”separator_character”) 2: Names it is used to specify own column headings for dataframe Syntax pandas.read_csv( file_address ,names=[“column_names”]) 3: skiprows skiprows argument can either take a number for number of rows to be skipped from beginning or it take a list of rows numbers to be skipped from CSV while reading data. Syntax pandas.read_csv( file_address ,skiprows=number_of_row(s) ) 4: nrows int, default None Number of rows of file to read. Useful for reading pieces of large files 5: Index_col It creates row index numbers on the basis of values (unique) of column in csv file. Makes passed column as index instead of 0, 1, 2, 3… 6: Header Use pandas read_csv header to specify which line in your data is to be considered as header. For example, the header is already present in the first line of csv file. In this case, we need to either use header = 0 or don't use any header argument. use header=None to explicitly tells people that the csv has no headers df = pd.read_csv(file_path, header=None) 7: Na_values str or list-like or dict, default None Additional strings to recognize as NA/NaN. If dict passed, specific per-column NA values. By default the following values are interpreted as NaN: ‘’, ‘#N/A’, ‘#N/A N/A’, ‘#NA’, ‘-1.#IND’, ‘-1.#QNAN’, ‘-NaN’, ‘-nan’, ‘1.#IND’, ‘1.#QNAN’, ‘N/A’, ‘NA’, ‘NULL’, ‘NaN’, ‘nan’. 8: Keep_default_na bool, default True If na_values are specified and keep_default_na is False the default NaN values are overridden, otherwise they’re appended to. 9: usecols:- this attribute is used to get specific columns or want a subset of the columns from csv file Syntax df = pd.read_csv(file_path, header=None, usecols=[3,6])

Handling Missing Values in Python | Pandas Tutorial In Hindi

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02.08.2020

#HandlingMissingValuesInPythonInHindi #PandasMissingData #MachineLearningTutorialinHindi For Online Training ping me on whatsapp - +918802245914 Data Science is very important topic in computer world . and pandas is the main topic of the data science . so in this video we will discourse how to handle missing values in python using pandas . so be continue with Mr. Manish Nain Other videos of this tutorial : 1 . What Is Pandas : 🤍 2. How to Install Pandas In Python : 🤍 3. How To Read CSV Files Using Pandas : 🤍 4. Pandas Data Frame in Hindi | Pandas Tutorial In Hindi : 🤍 5. What Is Dataframe In Pandas | How To Create Dataframe In Pandas | Pandas Tutorial In Hindi : 6. Write In Csv File In Pandas | Pandas Tutorial In Hindi : 🤍 other contents of this video: How to Handle Missing Values in Python, How to Handle Missing Values in Pandas, Handling Missing Values in Python, Handling Missing Values, Handling Missing Values in python in Hindi, Handling missing value, handling missing value in hindi, handling missing value in pandas, handling missing value in pandas in hindi, pandas tutorial in Hindi, free python tutorial, use of na_values,na_values, keep_default_na, python, data science, pandas tutorial

How to handle missing values ( Tutorial - 5 ) Machine learning series in hindi

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16.08.2020

How to handle missing data na_values() keep_default_na

Build a custom ML model with Vertex AI

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25.06.2021

Training code requirements → 🤍 Vertex AI: Training and serving a custom model codelab → 🤍 How can you build a Custom ML Model from scratch with Vertex AI? In this episode of AI Simplified, we build custom ML models and explore the different ways to run custom training in Vertex AI. Further, we kick off our training job in the console, and we deploy the TensorFlow model using a pre-built container. Watch to learn how Vertex AI makes building a custom ML model seamless! Chapters: 0:00 - Intro 0:43- What do we need to create a custom ML training job? 3:18 - Demo 9:59 - Summary Basic regression: Predict fuel efficiency → 🤍 Watch more episodes of Getting Started with Vertex AI → 🤍 Subscribe to Google Cloud Tech → 🤍 ​ #AISimplified product: Vertex AI; fullname: Priyanka Vergadia;

Importing Data with pandas

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In this video learn how to import data using pandas.

Python Pandas Tutorial (Part 9): Cleaning Data - Casting Datatypes and Handling Missing Values

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In this video, we will be learning how to clean our data and cast datatypes. This video is sponsored by Brilliant. Go to 🤍 to sign up for free. Be one of the first 200 people to sign up with this link and get 20% off your premium subscription. In this Python Programming video, we will be learning how to clean our data. We will be learning how to handle remove missing values, fill missing values, cast datatypes, and more. This is an essential skill in Pandas because we will frequently need to modify our data to our needs. Let's get started... The code for this video can be found at: 🤍 StackOverflow Survey Download Page - 🤍 ✅ Support My Channel Through Patreon: 🤍 ✅ Become a Channel Member: 🤍 ✅ One-Time Contribution Through PayPal: 🤍 ✅ Cryptocurrency Donations: Bitcoin Wallet - 3MPH8oY2EAgbLVy7RBMinwcBntggi7qeG3 Ethereum Wallet - 0x151649418616068fB46C3598083817101d3bCD33 Litecoin Wallet - MPvEBY5fxGkmPQgocfJbxP6EmTo5UUXMot ✅ Corey's Public Amazon Wishlist 🤍 ✅ Equipment I Use and Books I Recommend: 🤍 ▶️ You Can Find Me On: My Website - 🤍 My Second Channel - 🤍 Facebook - 🤍 Twitter - 🤍 Instagram - 🤍 #Python #Pandas

Pandas | Pandas Tutorial Python | Pandas for Beginners | #Pandas | Pandas Complete Course

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Subscribe to Support the channel: 🤍 Need help? Message me on LinkedIn: 🤍 Want to connect on Instagram? Here is my id 🤍vikasjha001 Connect to me: 💥 LinkedIn 🤍 📷 Instagram 🤍 ✈️ Channel 🤍 Learn Pandas and be a data analytics expert. In computer programming, #pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license. Link for Pandas Tutorial : Lesson 02 🤍 Pandas Tutorial : Lesson 01 00:00 - Introduction 00:50 - Anaconda for Pandas 01:12 - Download Indian Food Dataset 01:46 - Introduction to Indian Food Dataset 02:48 - Importing Pandas Module 03:45 - Read CSV File using Pandas Dataframe 06:18 - Reading Excel File Using Pandas Dataframe 06:48 - Check Total Rows and Columns in Pandas Dataframe File 07:25 - Dataframe Shape 07:50 - Getting list of columns of a Dataframe 09:20 - Check datatypes of columns in Dataframe 11:25 - Reading Pipe Separated File in Pandas 13:11 - Read CSV with Header in Different row in Pandas 15:40 - Read CSV File with Selected Columns in Pandas Dataframe 18:11 - How Pandas Dataframe Handles Duplicate Column 21:53 - Change datatype while reading files in pandas Dataframe 24:35 - Read all the columns as string in pandas Dataframe 25:10 - Reading first few records from beginning of a file using Dataframe 25:10 - Reading first few records from beginning of a file using Dataframe 25:45 - Reading last few records from end of a file using Dataframe 26:40 - Dataframe Data cleansing Introduction 27:20 - Check Null values or Empty Values in Pandas Dataframe 28:31 - Pandas Isnull() Function 30:30 - Assign na_values in Pandas Dataframe 34:13 - Filter records with Null values in a column in Dataframe 35:20 - Count Null values in a column in Dataframe 36:50 - Replace Null values with other value in Pandas Dataframe 39:00 - Write data to excel file in Pandas Dataframe 40:40 - The End - Do Subscribe for Part 2 Feel Free to connect on LinkedIn for any Query: 🤍 Sign up to Skillshare using this link and get one month free membership. 🤍 Here is the Paypal account to support this channel financially: 🤍 Here is the Paypal account to support this channel financially: 🤍 Here is the Paypal account to support this channel financially: 🤍 Here is the Paypal account to support this channel financially: 🤍 Here is the Paypal account to support this channel financially: 🤍 Here is the Paypal account to support this channel financially: 🤍

Python Pandas Part-8 | Handling Missing Values in Python in Hindi | MachineLearning Course #01.02.08

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‘Handling Missing Values in Python in Hindi | Python Pandas Part-8 in Hindi’ Course name: “Machine Learning – Beginner to Professional Hands-on Python Course in Hindi” In this tutorial we explain How to Handle Missing Values in Python in Hindi and describe these: 1) Pandas read_csv na_values 2) Pandas read_csv keep_default_na 3) Pandas read_csv na_filter Python Pandas Tutorial Part-5 🤍 Python Pandas Part-4 | How to Read CSV File in Pandas 🤍 Course Playlists- Python Pandas Tutorial in Hindi: 🤍 Machine Learning Beginner to Professional Hands-on Python Course in Hindi: 🤍 Python NumPy Tutorial in Hindi: 🤍 Introduction of Machine Learning: 🤍 For more information: Contact Us: - -Website: 🤍 -Facebook: 🤍 -Instagram: 🤍 -Twitter: 🤍 -LinkedIn: 🤍 #HandlingMissingValuesInPythonInHindi #PandasMissingData #MachineLearningTutorialinHindi #IndianAIProduction

STOP Making These 5 Beginner Coding Mistakes!!

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Join Showwcase, the social network built for developers - 🤍?referralToken=x0jj4ve6f8q If you want to be a great software engineer, you can’t be writing bad code. And if this topic of writing good, clean code seems like boring to you and something that’s not the sexiest thing to focus on I really understand that, I do. But writing good, clean code rather than filthy dirty, disgusting code, is just something you need to learn to do if you don’t want to be laugned at by your team and scorned at by your managers. Or shouted at by commenters if you post your coding projects on Youtube. So here are 5 very easy steps you can start taking now to help you write like 80% better code which will literally put you above 95% of beginners who never bother to learn any of this stuff. First, we need to understand what good code even means. Code is clean if it can be understood easily – by everyone, not just yourself. The key thing you need to understand is that the goal of code, especially in the context of larger companies where it’s not just you working on your dumb Python script, is that it not only does the job but is also easy to understand and modify by others. This is all something that I’ve now learned myself while currently researching this and focusing on implementing this over the past few weeks. I can tell that it makes a big difference when I go back to my own programs to debug them or change them or whatever, half the time I have literally no idea what I was even trying to do when I wrote the program the first time and that is no good. 📸 FOLLOW ME ON INSTAGRAM - 🤍 OTHER VIDEOS YOU SHOULD WATCH 💻 How I Learned to Code in 4 MONTHS - & Got a Software Engineer Job (no CS Degree) - 🤍 ⌨️ How I'm Teaching Myself Computer Science using Notion (OSSU) - 🤍 ✏️ My FREE COMPUTER SCIENCE DEGREE Notion Template - 🤍 CODING RESOURCES 💰 MY FAVOURITE CODING COURSES. Use Code FRIENDS10 for 10% off - 🤍 💵 GET THE SKILLS YOU NEED FOR A $100K TECH CAREER IN JUST 3 MONTHS - 🤍 🐍 BEST PYTHON COURSE - 🤍 ➕ BEST DATA STRUCTURES & ALGORITHMS COURSE - 🤍 📗 BEST BOOK TO PASS CODING INTERVIEWS - 🤍 📱 BEST MOBILE DEVELOPMENT COURSE - 🤍 OTHER AMAZING LEARNING RESOURCES 📚 Get 1 Month Free on Skillshare and learn any skill. Code: aff30d21 🏆 (affiliate link) 🤍 📘 Make It Stick: The Science of Successful Learning - 🤍 MY BLOG 📗 JOIN MEDIUM TO ACCESS MY BLOG CONTENT - 🤍 GEAR ⌨️ BEST KEYBOARD FOR PROGRAMMERS - 🤍 🖱 BEST PRODUCTIVITY MOUSE - 🤍 🔊 MY SPEAKERS - 🤍 🎧 MY HEADSET - 🤍 📸 MY CAMERA FOR YOUTUBE VIDEOS - 🤍 🎤 MY MIC - 🤍 📹 BEST AFFORDABLE GIMBAL - 🤍 🎵 WHERE I GET MY MUSIC - 🤍 WHO AM I? On this channel, my aim is to give you the tools, strategies and methods to learn to code effectively - according to science! In addition, I document my life as a self-taught software engineer. CHAPTERS: 0:00 Why you need to STOP bad code 1:25 What is Good Code? 2:20 Tip 1 - stop confusing code 4:48 Tip 2 - how to name variables like a pro 6:20 Find a developer community (Sponsor: Showwcase) 7:53 Tip 3 - this type of code makes you look dumb (how to stop it) 9:06 Tip 4 - How to write functions properly 10:48 Tip 5 - Literally the most important coding principle DISCLAIMER: some of the links in the description may be affiliate links. If you purchase a product or service using the links that I provide I may receive a small commission. This is no extra charge to you! Thanks for supporting Internet Made Coder :) Tags: clean code, write good code, how to write good code, coding for beginners, coding tutorial, python tutorial, how to learn python, how to learn to code, ow to learn coding for free, how to become a software engineer, self-taught software developer, no cs degree, frontend developer, learn computer science

Financial Data with Python: S&P 500 Data

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07.06.2021

In this video we take a look at financial data with python using the yfinance package. We use yfinance and pandas to grab all of the tickers for the S&P 500.

Import Data Into Python

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How to import data into Python using pandas. Thanks for watching!! ❤️ \\Import data code 🤍 \\Public datasets 🤍 🤍 Tip Jar 👉🏻👈🏻 ☕️ 🤍 💵 Venmo: 🤍mathetal ♫ Eric Skiff - Chibi Ninja 🤍 #pandas

Simple explanation of Modified Z Score | Modified Z Score to detect outliers with python code

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25.12.2021

Modified Z score is used many times to detect outliers instead of simple Z score. In this video we will understand what exactly is modified Z score in a very simple language such that even a child can understand it easily. I will show you a demo in excel first and then we will write python code to detect outliers using modified Z score. Code & Excel file: 🤍 ⭐️ Timestamps ⭐️ 00:00 Introduction 00:15 What is modified Z score and MAD 04:55 Excel demo 12:36 Python code to detect outliers using mod z score Do you want to learn technology from me? Check 🤍 for my affordable video courses. 🌎 Website: 🤍 🎥 Codebasics Hindi channel: 🤍 #️⃣ Social Media #️⃣ 🔗 Discord: 🤍 📸 Instagram: 🤍 🔊 Facebook: 🤍 📱 Twitter: 🤍 📝 Linkedin (Personal): 🤍 📝 Linkedin (Codebasics): 🤍 🔗 Patreon: 🤍 ❗❗ DISCLAIMER: All opinions expressed in this video are of my own and not that of my employers'.

How To Load Machine Learning Data From Files In Python

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Get my Free NumPy Handbook: 🤍 The common data format in Machine Learning is a CSV file (comma separated values). In this Tutorial I show 4 different ways how you can load the data from such files and then prepare the data. I also show you some best practices on how to deal with the correct data type, missing values, and an optional header. The 4 approaches are: - with the csv module - with numpy: np.loadtxt() and numpy.genfromtxt() - with pandas: pd.read_csv() 📓 Notebooks available on Patreon: 🤍 ⭐ Join Our Discord : 🤍 If you enjoyed this video, please subscribe to the channel! The code and all Machine Learning tutorials can be found here: 🤍 You can find me here: Website: 🤍 Twitter: 🤍 GitHub: 🤍 #Python #MachineLearning

Starting to Clean Data With Pandas

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Are you starting to work with Pandas and Numpy data? How do you set up your environment to work with data, dataframes, and pandas? This set of lessons covers importing data, creating a dataframe, exploring data, and more. If you’re just stepping into this field or planning to step into this field, it’s important to be able to deal with messy data, whether that means missing values, inconsistent formatting, malformed records, or nonsensical outliers. This is a portion of the complete course, which you can find here: 🤍 The rest of the course covers: - Working with .loc() and iloc() - Dropping unnecessary columns in a DataFrame - Changing the index of a DataFrame - Using .str() methods to clean columns

Python Working with Data Part I

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22.06.2019

File Handling and manipulation with Pandas

Read Tables from HTML page using Python Pandas - P1.5

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02.04.2018

Read Tables from HTML page using Python Pandas - P1.5 Topic to be covered - Read table from HTML Page 🤍 pandas.read_html(io, match='.+', flavor=None, header=None, index_col=None, skiprows=None, attrs=None, parse_dates=False, tupleize_cols=None, thousands=', ', encoding=None, decimal='.', converters=None, na_values=None, keep_default_na=True, displayed_only=True) io : str or file-like A URL, a file-like object, or a raw string containing HTML. Note that lxml only accepts the http, ftp and file url protocols. If you have a URL that starts with 'https' you might try removing the 's'. match : str or compiled regular expression, optional The set of tables containing text matching this regex or string will be returned. Unless the HTML is extremely simple you will probably need to pass a non-empty string here. Defaults to ‘.+’ (match any non-empty string). The default value will return all tables contained on a page. This value is converted to a regular expression so that there is consistent behavior between Beautiful Soup and lxml. flavor : str or None, container of strings The parsing engine to use. ‘bs4’ and ‘html5lib’ are synonymous with each other, they are both there for backwards compatibility. The default of None tries to use lxml to parse and if that fails it falls back on bs4 + html5lib. header : int or list-like or None, optional The row (or list of rows for a MultiIndex) to use to make the columns headers. index_col : int or list-like or None, optional The column (or list of columns) to use to create the index. skiprows : int or list-like or slice or None, optional 0-based. Number of rows to skip after parsing the column integer. If a sequence of integers or a slice is given, will skip the rows indexed by that sequence. Note that a single element sequence means ‘skip the nth row’ whereas an integer means ‘skip n rows’. attrs : dict or None, optional This is a dictionary of attributes that you can pass to use to identify the table in the HTML. These are not checked for validity before being passed to lxml or Beautiful Soup. However, these attributes must be valid HTML table attributes to work correctly. For example, Code Starts Here import pandas as pd df = pd.read_html('🤍 df0 = pd.read_html('🤍 df1 = pd.read_html('🤍 df2 = pd.read_html('🤍 df3 = pd.read_html('🤍 dfa = pd.read_html('🤍 dfb = pd.read_html('🤍 dfc = pd.read_html('🤍 All Playlist of this youtube channel 1. Data Preprocessing in Machine Learning 🤍 2. Confusion Matrix in Machine Learning, ML, AI 🤍 3. Anaconda, Python Installation, Spyder, Jupyter Notebook, PyCharm, Graphviz 🤍 4. Cross Validation, Sampling, train test split in Machine Learning 🤍 5. Drop and Delete Operations in Python Pandas 🤍 6. Matrices and Vectors with python 🤍 7. Detect Outliers in Machine Learning 🤍 8. TimeSeries preprocessing in Machine Learning 🤍 9. Handling Missing Values in Machine Learning 🤍 10. Dummy Encoding Encoding in Machine Learning 🤍 11. Data Visualisation with Python, Seaborn, Matplotlib 🤍 12. Feature Scaling in Machine Learning 🤍 13. Python 3 basics for Beginner 🤍 14. Statistics with Python 🤍 15. Sklearn Scikit Learn Machine Learning 🤍 16. Python Pandas Dataframe Operations 🤍

Scrape HTML Table as Dataframe using Python Pandas and R RVest - Hands-on Tutorial

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18.09.2021

In this video, We'll learn how to extract HTML Table or Scrape HTML Table Content directly into a Dataframe to find insights or make Data Visualizations from the scraped HTML Table. Instead of following a traditional route of web scraping, we're going to use abstracted functions using Python Pandas and R Rvest. Code Python - 🤍 Code R - 🤍 #pandas #rvest #webscraping #datascience How to use Google Colab with R - 🤍

Pandas DataFrame | Reading CSV File | DataFrame How to read CSV file in Python Pandas?

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10.08.2020

CSV File, Creating/Reading CSV File with Pandas DataFrame, Accessing Columns, Rows , Displaying data with and without header from a DataFrame

How to reset your DataFrame index

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09.10.2020

FREE data science resources: 🤍 My website and data science blog: 🤍 Follow me on twitter: 🤍

Pandas Tutorial Part:02 | Read data using pandas | how to read csv data | read csv data using pandas

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09.03.2021

Hello everyone, In this video I have told you how to read a csv file using pandas and how to check the rows and columns of the csv file and how to check the data using the pandas functions. link for python projects Playlist : 🤍 - Link for Basics of python (Hindi) Playlist : 🤍 - Link for Python Programming Playlist : 🤍 Link for Python for beginners(English) : 🤍 - Link for statistics (English) : 🤍 - Link for Numpy tutorial : 🤍 - Link for python Ide's playlist : 🤍 Link for data analysis playlist : 🤍 #pandas #python #pandaslibrary

Tutorial 10- Pandas Read CSV File ,StringIO Tutorial In Hindi- Part 2

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23.02.2022

Pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license. github: 🤍 Python playlist in hindi : 🤍 Please donate if you want to support the channel through GPay UPID, Gpay: krishnaik06🤍okicici - Recording Gears That I Use 🤍 - #krish #pythonbusted Connect with me here: Twitter: 🤍 Facebook: 🤍 instagram: 🤍

reading stock data into python using pandas

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08.03.2021

In this video, I describe how to read end-of-day (EOD) stock data downloaded from yahoo finance into python using the pandas read_csv function. Disclaimer: All examples and code presented in any of my Youtube videos are technical and illustrative in nature and do not represent any financial recommendation or investment advice.

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