How Do I Read a Csv File in R
This Pandas tutorial volition evidence you, by examples, how to use Pandas read_csv() method to import data from .csv files. In the start section, we will become through how to read a CSV file, how to read specific columns from a CSV, how to read multiple CSV files and combine them to one dataframe. Finally, we volition also learn how to convert data according to specific datatypes (eastward.g., using Pandas read_csv dtypes parameter).
In the last section, we will continue by learning how to utilize Pandas to write CSV files. That is, we will learn how to export dataframes to CSV files.
Pandas Import CSV from the Harddrive
In the offset example, of this Pandas tutorial, nosotros will but use read_csv to load CSV files, to dataframe, that are in the same directory as the script. If we accept the file in another directory we have to recollect to add the full path to the file.
Naturally, Pandas can be used to import data from a range of different file types. For case, Excel (xlsx), and JSON files can be read into Pandas dataframes. Acquire more than about importing data in Pandas:
- Pandas Excel Tutorial: How to Read and Write Excel Files
- Pandas Read & Write JSON Tutorial
How to Read CSV File in Python Pandas
In this section, we are going to learn how to read CSV files from the harddrive. Offset, yet, nosotros are going to respond the question How practice I open a CSV file in pandas?
How do I import a CSV file into Pandas using Python?
Here'south two simple steps to learn how to read a CSV file in Pandas:
1. Import the Pandas package:
import pandas as pd
2. Apply the pd.read_csv() method:
df = pd.read_csv('yourCSVfile.csv')
Note, the first parameter should be the file path to your CSV file.
In this tutorial, we volition learn how to work with comma-separated (CSV) files in Python and Pandas. We volition get an overview of how to apply Pandas to load CSV to dataframes and how to write dataframes to CSV.
df = pd.read_csv('amis.csv') df.caput()
Code language: Python ( python ) If nosotros are interested in reading files, in general, using Python we can use the open up() method. This way, we can read many file formats (e.g., .txt) in Python.
Additionally, we tin can use the index_col argument to make a column index in the Pandas dataframe. Finally, the data can be downloaded hither simply in the following examples, we are going to employ Pandas read_csv to load data from a URL.
Pandas Read CSV from a URL Examples
Can we import a CSV file from a URL using Pandas? Yes, and in this section, we are going to learn how to read a CSV file in Python using Pandas, simply similar in the previous instance. However, in the next read_csv instance, nosotros are going to read the same dataset just this time from a URL. Information technology'south very simple nosotros just put the URL in every bit the first parameter. Hither areto 3 unproblematic steps that will assistance us read a CSV from a URL:
- Once more, we need to import Pandas
- Create a string variable with the URL
- At present employ Pandas read_csv together with the URL (come across example below)
Example 1: Read CSV from a URL
In the case code below, we follow the 3 easy steps to import a CSV file into a Pandas dataframe:
import pandas as pd # String with URL: url_csv = 'https://vincentarelbundock.github.io/Rdatasets/csv/boot/amis.csv' # First case to read csv from URL df = pd.read_csv(url_csv) df.caput()
Code language: Python ( python ) 
In the epitome above, we tin can see that nosotros get a column named 'Unnamed: 0'. Furthermore, we can see that it contains numbers. Thus, when using Pandas, we can use this column as the alphabetize column. In the next code example, we are doing exactly this; we are going to use Pandas read_csv and the index_col parameter.
Example ii: Read CSV from a URL with index_col
This parameter can have an integer or a sequence. In our case, we are going to apply the integer 0 and nosotros will get a nicer dataframe:
url_csv = 'https://vincentarelbundock.github.io/Rdatasets/csv/boot/amis.csv' # Pandas Read CSV from URL Instance: df = pd.read_csv(url_csv, index_col=0) df.head()
Code language: Python ( python ) 
The index_col parameter also can take a string as input and we volition at present use a different datafile. In the next instance we will read a CSV into a Pandas dataframe and utilise the idNum column as alphabetize.
csv_url = 'http://vincentarelbundock.github.io/Rdatasets/csv/carData/MplsStops.csv' df = pd.read_csv(csv_url, index_col='idNum') df.iloc[:, 0:6].caput()
Code language: Python ( python ) 
Annotation, to get the in a higher place output we used Pandas iloc to select the get-go 7 rows. This was done to get an output that could be easier illustrated. That said, we are now continuing to the adjacent section where we are going to read certain columns to a dataframe from a CSV file.
A concluding annotation before going futher with reading CSV files: It is as well possible to use Pandas to read the iex cloud api with Python to import stock information.
Pandas Read CSV usecols
In some cases, we don't want to parse every column in the CSV file. To only read certain columns we can apply the parameter usecols. Note, if we want the first column to be the index column and we desire to parse the three first columns we need to take a listing with 4 elements (compare my read_excel usecols example here).
Pandas Read CSV Example: Specifying Columns to Import
Here's an instance when we employ Pandas read_csv() and only read the three first columns:
cols = [0, one, ii, 3] df = pd.read_csv(url_csv, index_col=0, usecols=cols) df.head()
Lawmaking language: Python ( python ) 
Note, we really did read 4 columns only set the first column as the index column. Of course, using read_csv usecols make more sense if nosotros had a CSV file with more columns. We can use usecols with a list of strings, every bit well. In the side by side example, we return to the larger file we used previously. Here'due south how to employ the cavalcade names in the datafile:
csv_url = 'http://vincentarelbundock.github.io/Rdatasets/csv/carData/MplsStops.csv' df = pd.read_csv(csv_url, index_col='idNum', usecols=['idNum', 'date', 'problem', 'MDC']) df.caput()
Lawmaking language: Python ( python ) 
Pandas Import CSV files and Remove Unnamed Cavalcade
In some of the previous read_csv example, we get an unnamed cavalcade. In previous sections, of this Pandas read CSV tutorial, we have solved this by setting this cavalcade equally the alphabetize columns, or used usecols to select specific columns from the CSV file. Nevertheless, we may not want to exercise that for some reason. Hither'southward one example of how to use pd.read_csv to go rid of the column "Unnamed:0":
csv_url = 'http://vincentarelbundock.github.io/Rdatasets/csv/carData/MplsStops.csv' cols = pd.read_csv(csv_url, nrows=1).columns df = pd.read_csv(csv_url, usecols=cols[1:]) df.iloc[:, 0:6].head()
Code language: Python ( python ) 
How to Drib a Column from Pandas dataframe
It's of course likewise possible to remove the unnamed columns afterwards we have loaded the CSV to a dataframe. To remove the unnamed columns nosotros can apply two different methods; loc and drib, together with other Pandas dataframe methods. When using the drop method we tin use the inplace parameter and become a dataframe without unnamed columns.
df.drop(df.columns[df.columns.str.contains('unnamed', case=False)], axis=1, inplace=True) # The following line volition give us the same result as the line above # df = df.loc[:, ~df.columns.str.contains('unnamed', case=False)] df.iloc[:, 0:vii].head()
Code linguistic communication: PHP ( php ) 
To explain the lawmaking example higher up; we select the columns without columns containing the string 'unnamed'. Furthermore, we used the case parameter so that the contains method is not case-sensitive. Thus, we volition get columns named "Unnamed" and "unnamed". In the commencement row, using Pandas drib, we are as well using the inplace parameter so that it changes our dataframe. The axis parameter, however, is used to drop columns instead of indices (i.e., rows).
- Larn some data manipulation techniques using Python and Pandas.
Pandas Read CSV and Missing Values
In the next Pandas read .csv example, we volition larn how to handle missing values in a Pandas dataframe. If nosotros accept missing data in our CSV file and it's coded in a way that makes information technology impossible for Pandas to detect them we can use the parameter na_values. In the example beneath, the amis.csv file has been changed and there are some cells with the string "Not Bachelor".
That is, we are going to change "Not Available" to something that we easily tin can remove when conveying out data assay after.
df = pd.read_csv('Simdata/MissingData.csv', index_col=0, na_values="Non Available") df.head()
Code language: Python ( python ) 
Reading a CSV file and Skipping Rows
What if our data file(south) contain information on the first x rows and we demand to skip rows when using Pandas read_csv? For instance, how tin we skip the starting time three rows in a file looking similar this:
We volition now learn how to use Pandas read_csv and skip x amount of rows. Luckily, information technology's very simple we just use the skiprows parameter. In the post-obit example, nosotros are setting skiprows to 3 to skip the beginning 3 rows.
Pandas read_csv skiprows example:
How exercise nosotros use Pandas skiprow parameter? Here's a Pandas read_csv instance, where we skip the three offset rows:
df = pd.read_csv('Simdata/skiprow.csv', index_col=0, skiprows=iii) df.caput()
Code language: Python ( python ) 
Note we tin can obtain the same upshot as above using the header parameter (i.e., information = pd.read_csv('Simdata/skiprow.csv', header=three)).
How to Read Certain Rows using Pandas
Can nosotros read specific rows from a CSV file using Pandas read_csv method? If nosotros don't desire to read every row in the CSV file we ca utilize the parameter nrows. In the adjacent instance, below we read the first 8 rows of a CSV file.
df = pd.read_csv(url_csv, nrows=8) df
Code linguistic communication: Python ( python ) If we want to select random rows nosotros can load the consummate CSV file and use Pandas sample to randomly select rows (learn more well-nigh this by reading the Pandas Sample tutorial).
Pandas read_csv dtype
We can likewise gear up the data types for the columns. Although, in the amis dataset all columns incorporate integers we can set some of them to string data type. This is exactly what we will exercise in the next Pandas read_csv pandas example. We will use the dtype parameter and put in a lexicon:
url_csv = 'https://vincentarelbundock.github.io/Rdatasets/csv/boot/amis.csv' df = pd.read_csv(url_csv, dtype={'speed':int, 'menstruum':str, 'alarm':str, 'pair':int}) df.info()
Code language: Python ( python ) 
It's, of course, possible to forcefulness other datatypes such every bit integer and float. All we have to do is change str to float, for instance (given that we have decimal numbers in that column, of grade).
Load Multiple Files to a Dataframe
If we have information from many sources such as experiment participants nosotros may have them in multiple CSV files. If the data, from the different CSV files, are going to exist analyzed together we may desire to load them all into one dataframe. In the adjacent examples, we are going to use Pandas read_csv to read multiple files.
Example 1: Reading Multiple CSV Files using os fnmatch
Showtime, nosotros are going to employ Python bone and fnmatch to list all files with the discussion "24-hour interval" of the file blazon CSV in the directory "SimData". Next, we are using Python list comprehension to load the CSV files into dataframes (stored in a list, see the blazon(dfs) output).
import bone, fnmatch csv_files = fnmatch.filter(os.listdir('./SimData'), '*Day*.csv') dfs = [pd.read_csv('SimData/' + bone.sep + csv_file) for csv_file in csv_files] type(dfs) # Output: list
Code language: Python ( python ) Finally, we employ the method concat to concatenate the dataframes in our list. In the case files, there is a column chosen 'Day' and then that each solar day (i.eastward., CSV file) is unique.
df = pd.concat(dfs, sort=False) df.Day.unique()
Instance two: Reading Multiple CSV Files using glob
The second method nosotros are going to use is a bit simpler; using Python glob. If we compare the two methods (os + fnmatch vs. glob) we tin can encounter that in the list comprehension we don't have to put the path. This is because glob volition have the total path to our files. Handy!
import glob csv_files = glob.glob('SimData/*Day*.csv') dfs = [] for csv_file in csv_files: temp_df = pd.read_csv(csv_file) temp_df['DataF'] = csv_file.split('\\')[1] dfs.append(temp_df)
Code language: Python ( python ) If we don't accept a column, in each CSV file, identifying which dataset information technology is (e.g., information from different days) we could apply the filename in a new column of each dataframe:
import glob csv_files = glob.glob('SimData/*Day*.csv') dfs = [] for csv_file in csv_files: temp_df = pd.read_csv(csv_file) temp_df['DataF'] = csv_file.split('\\')[1] dfs.append(temp_df)
Code language: Python ( python ) - Check the Pandas Dataframe Tutorial for Beginners
At that place are, of class, times when we need to rename multiple files (e.thousand., CSV files before loading them into Pandas dataframes). Luckily, to rename a file in Python we can use os.rename(). This method tin be used regardless if we need to rename CSV or .txt files.
Now nosotros know how to import multiple CSV files and, in the next department, nosotros volition acquire how to use Pandas to write to a CSV file.
How to Write CSV files in Pandas
In this section, we will acquire how to export dataframes to CSV files. We will start by creating a dataframe with some variables but first, we start past importing the modules Pandas:
import pandas as pd
Lawmaking language: Python ( python ) Before we go on and acquire how to utilize Pandas to write a CSV file, we will create a dataframe. Nosotros will create the dataframe using a dictionary. The keys will be the cavalcade names and the values will be lists containing our data:
df = pd.DataFrame({'Names':['Andreas', 'George', 'Steve', 'Sarah', 'Joanna', 'Hanna'], 'Historic period':[21, 22, 20, 19, 18, 23]}) df.head()
Code linguistic communication: Python ( python ) 
Saving Pandas Dataframe to CSV
Now we are set to learn how to salvage Pandas dataframe to CSV. Information technology's quite elementary, we write the dataframe to CSV file using Pandas to_csv method. In the example below we don't use any parameters simply the path_or_buf which is, in our example, the file name.
df.to_csv('NamesAndAges.csv')
Code language: Python ( python ) Here's how the exported dataframe look similar:
As can exist seen in the image above we get a new cavalcade when we are non using whatsoever parameters. This column is the index column from our Pandas dataframe. When working with Pandas to_csv, we tin utilize the parameter index and set it to False to become rid of this column.
df.to_csv('NamesAndAges.csv', index=False)
Code language: PHP ( php ) How to Write Multiple Dataframes to i CSV file
If we have many dataframes and we want to export them all to the same CSV file it is, of form, possible. In the Pandas to_csv example below we accept 3 dataframes. We are going to use Pandas concat with the parameters keys and names.
This is done to create two new columns, named Grouping and Row Num. The of import role is Group which will place the unlike dataframes. In the final row of the code example we use Pandas to_csv to write the dataframes to CSV.
df1 = pd.DataFrame({'Names': ['Andreas', 'George', 'Steve', 'Sarah', 'Joanna', 'Hanna'], 'Age':[21, 22, twenty, 19, 18, 23]}) df2 = pd.DataFrame({'Names': ['Pete', 'Jordan', 'Gustaf', 'Sophie', 'Emerge', 'Simone'], 'Age':[22, 21, 19, 19, 29, 21]}) df3 = pd.DataFrame({'Names': ['Ulrich', 'Donald', 'Jon', 'Jessica', 'Elisabeth', 'Diana'], 'Age':[21, 21, twenty, xix, xix, 22]}) df = pd.concat([df1, df2, df3], keys =['Group1', 'Group2', 'Group3'], names=['Group', 'Row Num']).reset_index() df.to_csv('MultipleDfs.csv', alphabetize=False)
Lawmaking linguistic communication: Python ( python ) 
In the CSV file we get iv columns. The keys parameter with the list (['Group1', 'Group2', 'Group3']) will enable identification of the different dataframes we wrote. Nosotros also get the column "Row Num" which will contain the row numbers for each dataframe:
Conclusion
In this tutorial we take learned near importing CSV files into Pandas dataframe. More specifically, nosotros accept learned how to:
- Load CSV files to dataframe using Pandas read_csv
- locally
- from the WEB
- Read certain columns
- Remove unnamed columns
- Handle missing values
- Skipping rows and reading certain rows
- Changing datatypes using dtypes
- Reading many CSV files
- Saving dataframes to CSV using Pandas to_csv
Source: https://www.marsja.se/pandas-read-csv-tutorial-to-csv/
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