Pandas: How to Read and Write Files

enter image description here

Pandas is a powerful and flexible Python package that allows you to work with labeled and time series data. It also provides statistics methods, enables plotting, and more. One crucial feature of Pandas is its ability to write and read Excel, CSV, and many other types of files. Functions like the Pandas read_csv() method enable you to work with files effectively. You can use them to save the data and labels from Pandas objects to a file and load them later as Pandas Series or DataFrame instances.

In this tutorial, you’ll learn:

What the Pandas IO tools API is
How to read and write data to and from files
How to work with various file formats
How to work with big data efficiently

Let’s start reading and writing files!

Free Bonus: 5 Thoughts On Python Mastery, a free course for Python developers that shows you the roadmap and the mindset you’ll need to take your Python skills to the next level.

Installing Pandas

The code in this tutorial is executed with CPython 3.7.4 and Pandas 0.25.1. It would be beneficial to make sure you have the latest versions of Python and Pandas on your machine. You might want to create a new virtual environment and install the dependencies for this tutorial.

First, you’ll need the Pandas library. You may already have it installed. If you don’t, then you can install it with pip:

$ pip install pandas

Once the installation process completes, you should have Pandas installed and ready.

Anaconda is an excellent Python distribution that comes with Python, many useful packages like Pandas, and a package and environment manager called Conda. To learn more about Anaconda, check out Setting Up Python for Machine Learning on Windows.

If you don’t have Pandas in your virtual environment, then you can install it with Conda:

$ conda install pandas

Conda is powerful as it manages the dependencies and their versions. To learn more about working with Conda, you can check out the official documentation.

Preparing Data

In this tutorial, you’ll use the data related to 20 countries. Here’s an overview of the data and sources you’ll be working with:

Country is denoted by the country name. Each country is in the top 10 list for either population, area, or gross domestic product (GDP). The row labels for the dataset are the three-letter country codes defined in ISO 3166-1. The column label for the dataset is COUNTRY.
Population is expressed in millions. The data comes from a list of countries and dependencies by population on Wikipedia. The column label for the dataset is POP.
Area is expressed in thousands of kilometers squared. The data comes from a list of countries and dependencies by area on Wikipedia. The column label for the dataset is AREA.
Gross domestic product is expressed in millions of U.S. dollars, according to the United Nations data for 2017. You can find this data in the list of countries by nominal GDP on Wikipedia. The column label for the dataset is GDP.
Continent is either Africa, Asia, Oceania, Europe, North America, or South America. You can find this information on Wikipedia as well. The column label for the dataset is CONT.
Independence day is a date that commemorates a nation’s independence. The data comes from the list of national independence days on Wikipedia. The dates are shown in ISO 8601 format. The first four digits represent the year, the next two numbers are the month, and the last two are for the day of the month. The column label for the dataset is IND_DAY.

This is how the data looks as a table:

CODE	COUNTRY	POP	AREA	GDP	CONT	IND_DAY
CHN	China	1398.72	9596.96	12234.8	Asia	nan
IND	India	1351.16	3287.26	2575.67	Asia	15/08/1947
USA	US	329.74	9833.52	19485.4	N.America	1776-07-04
IDN	Indonesia	268.07	1910.93	1015.54	Asia	17/08/1945
BRA	Brazil	210.32	8515.77	2055.51	S.America	1822-09-07
PAK	Pakistan	205.71	881.91	302.14	Asia	14/08/1947
NGA	Nigeria	200.96	923.77	375.77	Africa	01/10/1960
BGD	Bangladesh	167.09	147.57	245.63	Asia	26/03/1971
RUS	Russia	146.79	17098.2	1530.75	nan	12/06/1992
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	3693.2	Europe	nan
FRA	France	67.02	640.68	2582.49	Europe	1789-07-14
GBR	UK	66.44	242.5	2631.23	Europe	nan
ITA	Italy	60.36	301.34	1943.84	Europe	nan
ARG	Argentina	44.94	2780.4	637.49	S.America	1816-07-09
DZA	Algeria	43.38	2381.74	167.56	Africa	05/07/1962
CAN	Canada	37.59	9984.67	1647.12	N.America	1867-07-01
	AUS	Australia	25.47	7692.02	1408.68	Oceania

You may notice that some of the data is missing. For example, the continent for Russia is not specified because it spreads across both Europe and Asia. There are also several missing independence days because the data source omits them.

You can organize this data in Python using a nested dictionary:

data = {
    'CHN': {'COUNTRY': 'China', 'POP': 1_398.72, 'AREA': 9_596.96,
            'GDP': 12_234.78, 'CONT': 'Asia'},
    'IND': {'COUNTRY': 'India', 'POP': 1_351.16, 'AREA': 3_287.26,
            'GDP': 2_575.67, 'CONT': 'Asia', 'IND_DAY': '1947-08-15'},
    'USA': {'COUNTRY': 'US', 'POP': 329.74, 'AREA': 9_833.52,
            'GDP': 19_485.39, 'CONT': 'N.America',
            'IND_DAY': '1776-07-04'},
    'IDN': {'COUNTRY': 'Indonesia', 'POP': 268.07, 'AREA': 1_910.93,
            'GDP': 1_015.54, 'CONT': 'Asia', 'IND_DAY': '1945-08-17'},
    'BRA': {'COUNTRY': 'Brazil', 'POP': 210.32, 'AREA': 8_515.77,
            'GDP': 2_055.51, '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': 17_098.25,
            'GDP': 1_530.75, 'IND_DAY': '1992-06-12'},
    'MEX': {'COUNTRY': 'Mexico', 'POP': 126.58, 'AREA': 1_964.38,
            'GDP': 1_158.23, 'CONT': 'N.America', 'IND_DAY': '1810-09-16'},
    'JPN': {'COUNTRY': 'Japan', 'POP': 126.22, 'AREA': 377.97,
            'GDP': 4_872.42, 'CONT': 'Asia'},
    'DEU': {'COUNTRY': 'Germany', 'POP': 83.02, 'AREA': 357.11,
            'GDP': 3_693.20, 'CONT': 'Europe'},
    'FRA': {'COUNTRY': 'France', 'POP': 67.02, 'AREA': 640.68,
            'GDP': 2_582.49, 'CONT': 'Europe', 'IND_DAY': '1789-07-14'},
    'GBR': {'COUNTRY': 'UK', 'POP': 66.44, 'AREA': 242.50,
            'GDP': 2_631.23, 'CONT': 'Europe'},
    'ITA': {'COUNTRY': 'Italy', 'POP': 60.36, 'AREA': 301.34,
            'GDP': 1_943.84, 'CONT': 'Europe'},
    'ARG': {'COUNTRY': 'Argentina', 'POP': 44.94, 'AREA': 2_780.40,
            'GDP': 637.49, 'CONT': 'S.America', 'IND_DAY': '1816-07-09'},
    'DZA': {'COUNTRY': 'Algeria', 'POP': 43.38, 'AREA': 2_381.74,
            'GDP': 167.56, 'CONT': 'Africa', 'IND_DAY': '1962-07-05'},
    'CAN': {'COUNTRY': 'Canada', 'POP': 37.59, 'AREA': 9_984.67,
            'GDP': 1_647.12, 'CONT': 'N.America', 'IND_DAY': '1867-07-01'},
    'AUS': {'COUNTRY': 'Australia', 'POP': 25.47, 'AREA': 7_692.02,
            'GDP': 1_408.68, 'CONT': 'Oceania'},
    'KAZ': {'COUNTRY': 'Kazakhstan', 'POP': 18.53, 'AREA': 2_724.90,
            'GDP': 159.41, 'CONT': 'Asia', 'IND_DAY': '1991-12-16'}
}

columns = ('COUNTRY', 'POP', 'AREA', 'GDP', 'CONT', 'IND_DAY')

Each row of the table is written as an inner dictionary whose keys are the column names and values are the corresponding data. These dictionaries are then collected as the values in the outer data dictionary. The corresponding keys for data are the three-letter country codes.

You can use this data to create an instance of a Pandas DataFrame. First, you need to import Pandas:

import pandas as pd

Now that you have Pandas imported, you can use the DataFrame constructor and data to create a DataFrame object.

data is organized in such a way that the country codes correspond to columns. You can reverse the rows and columns of a DataFrame with the property .T:

df = pd.DataFrame(data=data).T
df

## output
COUNTRY      POP     AREA      GDP       CONT     IND_DAY
CHN       China  1398.72  9596.96  12234.8       Asia         NaN
IND       India  1351.16  3287.26  2575.67       Asia  1947-08-15
USA          US   329.74  9833.52  19485.4  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  17098.2  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   3693.2     Europe         NaN
FRA      France    67.02   640.68  2582.49     Europe  1789-07-14
GBR          UK    66.44    242.5  2631.23     Europe         NaN
ITA       Italy    60.36   301.34  1943.84     Europe         NaN
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         NaN
KAZ  Kazakhstan    18.53   2724.9   159.41       Asia  1991-12-16`

Now you have your DataFrame object populated with the data about each country.

Note: You can use .transpose() instead of .T to reverse the rows and columns of your dataset. If you use .transpose(), then you can set the optional parameter copy to specify if you want to copy the underlying data. The default behavior is False.

Versions of Python older than 3.6 did not guarantee the order of keys in dictionaries. To ensure the order of columns is maintained for older versions of Python and Pandas, you can specify index=columns:

df = pd.DataFrame(data=data, index=columns).T

Now that you’ve prepared your data, you’re ready to start working with files!

Using the Pandas `read_csv()` and `.to_csv()` Functions

A comma-separated values (CSV) file is a plaintext file with a .csv extension that holds tabular data. This is one of the most popular file formats for storing large amounts of data. Each row of the CSV file represents a single table row. The values in the same row are by default separated with commas, but you could change the separator to a semicolon, tab, space, or some other character.

Write a CSV File

You can save your Pandas DataFrame as a CSV file with .to_csv():

df.to_csv('data.csv')

That’s it! You’ve created the file data.csv in your current working directory. You can expand the code block below to see how your CSV file should look:

data.csvShow/Hide

This text file contains the data separated with commas. The first column contains the row labels. In some cases, you’ll find them irrelevant. If you don’t want to keep them, then you can pass the argument index=False to .to_csv().

Read a CSV File

Once your data is saved in a CSV file, you’ll likely want to load and use it from time to time. You can do that with the Pandas read_csv() function:

df = pd.read_csv('data.csv', index_col=0)
df

## output
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  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        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   Australia    25.47   7692.02   1408.68    Oceania         NaN
KAZ  Kazakhstan    18.53   2724.90    159.41       Asia  1991-12-16

In this case, the Pandas read_csv() function returns a new DataFrame with the data and labels from the file data.csv, which you specified with the first argument. This string can be any valid path, including URLs.

The parameter index_col specifies the column from the CSV file that contains the row labels. You assign a zero-based column index to this parameter. You should determine the value of index_col when the CSV file contains the row labels to avoid loading them as data.

You’ll learn more about using Pandas with CSV files later on in this tutorial. You can also check out Reading and Writing CSV Files in Python to see how to handle CSV files with the built-in Python library csv as well.

Using Pandas to Write and Read Excel Files

Microsoft Excel is probably the most widely-used spreadsheet software. While older versions used binary .xls files, Excel 2007 introduced the new XML-based .xlsx file. You can read and write Excel files in Pandas, similar to CSV files. However, you’ll need to install the following Python packages first:

xlwt to write to .xls files
openpyxl or XlsxWriter to write to .xlsx files
xlrd to read Excel files

You can install them using pip with a single command:

$ pip install xlwt openpyxl xlsxwriter xlrd

You can also use Conda:

$ conda install xlwt openpyxl xlsxwriter xlrd

Please note that you don’t have to install all these packages. For example, you don’t need both openpyxl and XlsxWriter. If you’re going to work just with .xls files, then you don’t need any of them! However, if you intend to work only with .xlsx files, then you’re going to need at least one of them, but not xlwt. Take some time to decide which packages are right for your project.

Write an Excel File

Once you have those packages installed, you can save your DataFrame in an Excel file with .to_excel():

df.to_excel('data.xlsx')

The argument 'data.xlsx' represents the target file and, optionally, its path. The above statement should create the file data.xlsx in your current working directory. That file should look like this:

The first column of the file contains the labels of the rows, while the other columns store data.

Read an Excel File

You can load data from Excel files with read_excel():

df = pd.read_excel('data.xlsx', index_col=0)
df

## output
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  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        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   Australia    25.47   7692.02   1408.68    Oceania         NaN
KAZ  Kazakhstan    18.53   2724.90    159.41       Asia  1991-12-16`

read_excel() returns a new DataFrame that contains the values from data.xlsx. You can also use read_excel() with OpenDocument spreadsheets, or .ods files.

You’ll learn more about working with Excel files later on in this tutorial. You can also check out Using Pandas to Read Large Excel Files in Python.

Understanding the Pandas IO API

Pandas IO Tools is the API that allows you to save the contents of Series and DataFrame objects to the clipboard, objects, or files of various types. It also enables loading data from the clipboard, objects, or files.

Write Files

Series and DataFrame objects have methods that enable writing data and labels to the clipboard or files. They’re named with the pattern .to_<file-type>(), where <file-type> is the type of the target file.

You’ve learned about .to_csv() and .to_excel(), but there are others, including:

.to_json()
.to_html()
.to_sql()
.to_pickle()

There are still more file types that you can write to, so this list is not exhaustive.

Note: To find similar methods, check the official documentation about serialization, IO, and conversion related to Series and DataFrame objects.

These methods have parameters specifying the target file path where you saved the data and labels. This is mandatory in some cases and optional in others. If this option is available and you choose to omit it, then the methods return the objects (like strings or iterables) with the contents of DataFrame instances.

The optional parameter compression decides how to compress the file with the data and labels. You’ll learn more about it later on. There are a few other parameters, but they’re mostly specific to one or several methods. You won’t go into them in detail here.

Read Files

Pandas functions for reading the contents of files are named using the pattern .read_<file-type>(), where <file-type> indicates the type of the file to read. You’ve already seen the Pandas read_csv() and read_excel() functions. Here are a few others:

read_json()
read_html()
read_sql()
read_pickle()

These functions have a parameter that specifies the target file path. It can be any valid string that represents the path, either on a local machine or in a URL. Other objects are also acceptable depending on the file type.

The optional parameter compression determines the type of decompression to use for the compressed files. You’ll learn about it later on in this tutorial. There are other parameters, but they’re specific to one or several functions. You won’t go into them in detail here.

Working With Different File Types

The Pandas library offers a wide range of possibilities for saving your data to files and loading data from files. In this section, you’ll learn more about working with CSV and Excel files. You’ll also see how to use other types of files, like JSON, web pages, databases, and Python pickle files.

CSV Files

You’ve already learned how to read and write CSV files. Now let’s dig a little deeper into the details. When you use .to_csv() to save your DataFrame, you can provide an argument for the parameter path_or_buff to specify the path, name, and extension of the target file.

path_or_buff is the first argument .to_csv() will get. It can be any string that represents a valid file path that includes the file name and its extension. You’ve seen this in a previous example. However, if you omit path_or_buff, then .to_csv() won’t create any files. Instead, it’ll return the corresponding string:

df = pd.DataFrame(data=data).T
s = df.to_csv()
print(s)

## output
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,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,
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 missing values in your DataFrame object. For example, the continent for Russia and the independence days for several countries (China, Japan, and so on) are not available. In data science and machine learning, you must handle missing values carefully. Pandas excels here! By default, Pandas uses the NaN value to replace the missing values.

Note: nan, which stands for “not a number,” is a particular floating-point value in Python.

You can get a nan value with any of the following functions:

The continent that corresponds to Russia in df is nan:

KAZ,Kazakhstan,18.53,2724.9,159.41,Asia,1991-12-16

df.loc['RUS', 'CONT']

# nan

KAZ,Kazakhstan,18.53,2724.9,159.41,Asia,1991-12-16

This example uses .loc[] to get data with the specified row and column names.

When you save your DataFrame to a CSV file, empty strings ('') will represent the missing data. You can see this both in your file data.csv and in the string s. If you want to change this behavior, then use the optional parameter na_rep:

KAZ,Kazakhstan,18.53,2724.9,159.41,Asia,1991-12-16

df.to_csv('new-data.csv', na_rep='(missing)')
KAZ,Kazakhstan,18.53,2724.9,159.41,Asia,1991-12-16

This code produces the file new-data.csv where the missing values are no longer empty strings. You can expand the code block below to see how this file should look: KAZ,Kazakhstan,18.53,2724.9,159.41,Asia,1991-12-16 new-data.csvShow/Hide

Now, the string '(missing)' in the file corresponds to the nan values from df.

When Pandas reads files, it considers the empty string ('') and a few others as missing values by default:

'nan'
'-nan'
'NA'
'N/A'
'NaN'
'null'

If you don’t want this behavior, then you can pass keep_default_na=False to the Pandas read_csv() function. To specify other labels for missing values, use the parameter na_values:

pd.read_csv('new-data.csv', index_col=0, na_values='(missing)')

## output
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  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        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   Australia    25.47   7692.02   1408.68    Oceania         NaN
KAZ  Kazakhstan    18.53   2724.90    159.41       Asia  1991-12-16

Here, you’ve marked the string '(missing)' as a new missing data label, and Pandas replaced it with nan when it read the file.

When you load data from a file, Pandas assigns the data types to the values of each column by default. You can check these types with .dtypes:

df = pd.read_csv('data.csv', index_col=0)
df.dtypes

## output
COUNTRY     object
POP        float64
AREA       float64
GDP        float64
CONT        object
IND_DAY     object
dtype: object

The columns with strings and dates ('COUNTRY', 'CONT', and 'IND_DAY') have the data type object. Meanwhile, the numeric columns contain 64-bit floating-point numbers (float64).

You can use the parameter dtype to specify the desired data types and parse_dates to force use of datetimes:

dtypes = {'POP': 'float32', 'AREA': 'float32', 'GDP': 'float32'}
df = pd.read_csv('data.csv', index_col=0, dtype=dtypes,
                  parse_dates=['IND_DAY'])
df.dtypes

## output
COUNTRY            object
POP               float32
AREA              float32
GDP               float32
CONT               object
IND_DAY    datetime64[ns]
dtype: object

df['IND_DAY']

## output
CHN          NaT
IND   1947-08-15
USA   1776-07-04
IDN   1945-08-17
BRA   1822-09-07
PAK   1947-08-14
NGA   1960-10-01
BGD   1971-03-26
RUS   1992-06-12
MEX   1810-09-16
JPN          NaT
DEU          NaT
FRA   1789-07-14
GBR          NaT
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 differ slightly from the original 64-bit numbers because of smaller precision. The values in the last column are considered as dates and have the data type datetime64. That’s why the NaN values in this column are replaced with NaT.

Now that you have real dates, 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’ve specified the parameter date_format to be '%B %d, %Y'. You can expand the code block below to see the resulting file:

formatted-data.csvShow/Hide

The format of the dates is different now. The format '%B %d, %Y' means the date will first display the full name of the month, then the day followed by a comma, and finally the full year.

There are several other optional parameters that you can use with .to_csv():

sep denotes a values separator.
decimal indicates a decimal separator.
encoding sets the file encoding.
header specifies whether you want to write column labels in the file.

Here’s how you would pass arguments for sep and header:

s = df.to_csv(sep=';', header=False)
 print(s)

## output
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;
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

The data is separated with a semicolon (';') because you’ve specified sep=';'. Also, since you passed header=False, you see your data without the header row of column names.

The Pandas read_csv() function has many additional options for managing missing data, working with dates and times, quoting, encoding, handling errors, and more. For instance, if you have a file with one data column and want to get a Series object instead of a DataFrame, then you can pass squeeze=True to read_csv(). You’ll learn later on about data compression and decompression, as well as how to skip rows and columns.

JSON Files

JSON stands for JavaScript object notation. JSON files are plaintext files used for data interchange, and humans can read them easily. They follow the ISO/IEC 21778:2017 and ECMA-404 standards and use the .json extension. Python and Pandas work well with JSON files, as Python’s json library offers built-in support for them.

You can save the data from your DataFrame to a JSON file with .to_json(). Start by creating a DataFrame object again. Use the dictionary data that holds the data about countries and then apply .to_json():

df = pd.DataFrame(data=data).T
df.to_json('data-columns.json')

This code produces the file data-columns.json. You can expand the code block below to see how this file should look:

data-columns.jsonShow/Hide

data-columns.json has one large dictionary with the column labels as keys and the corresponding inner dictionaries as values.

You can get a different file structure if you pass an argument for the optional parameter orient:

df.to_json('data-index.json', orient='index')

The orient parameter defaults to 'columns'. Here, you’ve set it to index.

You should get a new file data-index.json. You can expand the code block below to see the changes:

data-index.jsonShow/Hide

data-index.json also has one large dictionary, but this time the row labels are the keys, and the inner dictionaries are the values.

There are few more options for orient. One of them is 'records':

df.to_json('data-records.json', orient='records')

This code should yield the file data-records.json. You can expand the code block below to see the content:

data-records.jsonShow/Hide

data-records.json holds a list with one dictionary for each row. The row labels are not written.

You can get another interesting file structure with orient='split':

df.to_json('data-split.json', orient='split')

The resulting file is data-split.json. You can expand the code block below to see how this file should look:

data-split.jsonShow/Hide

data-split.json contains one dictionary that holds the following lists:

The names of the columns
The labels of the rows
The inner lists (two-dimensional sequence) that hold data values

If you don’t provide the value for the optional parameter path_or_buf that defines the file path, then .to_json() will return a JSON string instead of writing the results to a file. This behavior is consistent with .to_csv().

There are other optional parameters you can use. For instance, you can set index=False to forego saving row labels. You can manipulate precision with double_precision, and dates with date_format and date_unit. These last two parameters are particularly important when you have time series among your data:

df = pd.DataFrame(data=data).T
df['IND_DAY'] = pd.to_datetime(df['IND_DAY'])
df.dtypes

## output
COUNTRY            object
POP                object
AREA               object
GDP                object
CONT               object
IND_DAY    datetime64[ns]
dtype: object

 df.to_json('data-time.json')

In this example, you’ve created the DataFrame from the dictionary data and used to_datetime() to convert the values in the last column to datetime64. You can expand the code block below to see the resulting file:

data-time.jsonShow/Hide

In this file, you have large integers instead of dates for the independence days. That’s because the default value of the optional parameter date_format is 'epoch' whenever orient isn’t 'table'. This default behavior expresses dates as an epoch in milliseconds relative to midnight on January 1, 1970.

However, if you pass date_format='iso', then you’ll get the dates in the ISO 8601 format. In addition, date_unit decides the units of time:

df = pd.DataFrame(data=data).T
df['IND_DAY'] = pd.to_datetime(df['IND_DAY'])
df.to_json('new-data-time.json', date_format='iso', date_unit='s')

This code produces the following JSON file:

new-data-time.jsonShow/Hide

The dates in the resulting file are in the ISO 8601 format.

You can load the data from a JSON file with read_json():

df = pd.read_json('data-index.json', orient='index',
                 convert_dates=['IND_DAY'])

files. The optional parameter orient is very important because it specifies how Pandas understands the structure of the file.

There are other optional parameters you can use as well:

Set the encoding with encoding.
Manipulate dates with convert_dates and keep_default_dates.
Impact precision with dtype and precise_float.
Decode numeric data directly to NumPy arrays with numpy=True.

Note that you might lose the order of rows and columns when using the JSON format to store your data.

HTML Files

An HTML is a plaintext file that uses hypertext markup language to help browsers render web pages. The extensions for HTML files are .html and .htm. You’ll need to install an HTML parser library like lxml or html5lib to be able to work with HTML files:

`$pip install lxml html5lib

You can also use Conda to install the same packages:

`$ conda install lxml html5lib

Once you have these libraries, you can save the contents of your DataFrame as an HTML file with .to_html():

df = pd.DataFrame(data=data).T
df.to_html('data.html')

This code generates a file data.html. You can expand the code block below to see how this file should look:

data.htmlShow/Hide

This file shows the DataFrame contents nicely. However, notice that you haven’t obtained an entire web page. You’ve just output the data that corresponds to df in the HTML format.

.to_html() won’t create a file if you don’t provide the optional parameter buf, which denotes the buffer to write to. If you leave this parameter out, then your code will return a string as it did with .to_csv() and .to_json().

Here are some other optional parameters:

header determines whether to save the column names.
index determines whether to save the row labels.
classes assigns cascading style sheet (CSS) classes.
render_links specifies whether to convert URLs to HTML links.
table_id assigns the CSS id to the table tag.
escape decides whether to convert the characters <, >, and & to HTML-safe strings.

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 will return a DataFrame instance or a list of them:

df = pd.read_html('data.html', index_col=0, parse_dates=['IND_DAY'])

This is very similar to what you did when reading CSV files. You also have parameters that help you work with dates, missing values, precision, encoding, HTML parsers, and more.

Excel Files

You’ve already learned how to read and write Excel files with Pandas. However, there are a few more options worth considering. For one, when you use .to_excel(), you can specify the name of the target worksheet with the 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' is the argument for the parameter excel_writer that defines the name of the Excel file or its path.

The optional parameters startrow and startcol both default to 0 and indicate the upper left-most cell where the data should start being written:

df.to_excel('data-shifted.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 used zero-based indexing, so the third row is denoted by 2 and the fifth column by 4.

Now the resulting worksheet looks like this:

As you can see, the table starts in the third row 2 and the fifth column E.

.read_excel() also has the optional parameter sheet_name that specifies which worksheets to read when loading data. It can take on one of the following values:

The zero-based index of the worksheet
The name of the worksheet
The list of indices or names to read multiple sheets
The value None to read all sheets

Here’s how you would 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 the same DataFrame because the sheet_name parameters have the same values. In both cases, sheet_name=0 and sheet_name='COUNTRIES' refer to the same worksheet. The argument parse_dates=['IND_DAY'] tells Pandas to try to consider the values in this column as dates or times.

There are other optional parameters you can use with .read_excel() and .to_excel() to determine the Excel engine, the encoding, the way to handle missing values and infinities, the method for writing column names and row labels, and so on.

SQL Files

Pandas IO tools can also read and write databases. In this next example, you’ll write your data to a database called data.db. To get started, you’ll need the SQLAlchemy package. To learn more about it, you can read the official ORM tutorial. You’ll also need the database driver. Python has a built-in driver for SQLite.

You can install SQLAlchemy with pip:

$ pip install sqlalchemy

You can also install it with Conda:

$ conda install sqlalchemy

Once you have SQLAlchemy installed, import create_engine() and create a database engine:

from sqlalchemy import create_engine
engine = create_engine('sqlite:///data.db', echo=False)

Now that you have everything set up, the next step is to create a DataFrame object. It’s convenient to specify the 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

## output
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’ve created your DataFrame, you can save it to the database with .to_sql():

df.to_sql('data.db', con=engine, index_label='ID')

The parameter con is used to specify the database connection or engine that you want to use. The optional parameter index_label specifies how to call the database column with the row labels. You’ll often see it take on the value ID, Id, or id.

You should get the database data.db with a single table that looks like this:

The first column contains the row labels. To omit writing them into the database, pass index=False to .to_sql(). The other columns correspond to the columns of the DataFrame.

There are a few more optional parameters. For example, you can use schema to specify the database schema and dtype to determine the types of the database columns. You can also use if_exists, which says what to do if a database with the same name and path already exists:

if_exists='fail' raises a ValueError and is the default.
if_exists='replace' drops the table and inserts new values.
if_exists='append' inserts new values into the table.

You can load the data from the database with read_sql():

df = pd.read_sql('data.db', con=engine, index_col='ID')
df

## output
 COUNTRY      POP      AREA       GDP       CONT    IND_DAY
ID
CHN       China  1398.72   9596.96  12234.78       Asia        NaT
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       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    242.50   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`

The parameter index_col specifies the name of the column with the row labels. Note that this inserts an extra row after the header that starts with ID. You can fix this behavior with the following line of code:

df.index.name = None
df

## output
COUNTRY      POP      AREA       GDP       CONT    IND_DAY
CHN       China  1398.72   9596.96  12234.78       Asia        NaT
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       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    242.50   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

Now you have the same DataFrame object as before.

Note that the continent for Russia is now None instead of nan. If you want to fill the missing values with nan, then you can use .fillna():

df.fillna(value=float('nan'), inplace=True)

.fillna() replaces all missing values with whatever you pass to value. Here, you passed float('nan'), which says to fill all missing values with nan.

Also note that you didn’t have to pass parse_dates=['IND_DAY'] to read_sql(). That’s because your database was able to detect that the last column contains dates. However, you can pass parse_dates if you’d like. You’ll get the same results.

There are other functions that you can use to read databases, like read_sql_table() and read_sql_query(). Feel free to try them out!

Pickle Files

Pickling is the act of converting Python objects into byte streams. Unpickling is the inverse process. Python pickle files are the binary files that keep the data and hierarchy of Python objects. They usually have the extension .pickle or .pkl.

You can save your DataFrame in a pickle 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 the data types. Then, you create a file data.pickle to contain your data. You could also pass an integer value to the optional parameter protocol, which specifies the protocol of the pickler.

You can get the data from a pickle file with read_pickle():

df = pd.read_pickle('data.pickle')
df

## output
COUNTRY      POP      AREA       GDP       CONT    IND_DAY
CHN       China  1398.72   9596.96  12234.78       Asia        NaT
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        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    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`

read_pickle() returns the DataFrame with the stored data. You can also check the data types:

df.dtypes

## output
COUNTRY            object
POP               float64
AREA              float64
GDP               float64
CONT               object
IND_DAY    datetime64[ns]
dtype: object

These are the same ones that you specified before using .to_pickle().

As a word of caution, you should always beware of loading pickles from untrusted sources. This can be dangerous! When you unpickle an untrustworthy file, it could execute arbitrary code on your machine, gain remote access to your computer, or otherwise exploit your device in other ways.

Working With Big Data

If your files are too large for saving or processing, then there are several approaches you can take to reduce the required disk space:

Compress your files
Choose only the columns you want
Omit the rows you don’t need
Force the use of less precise data types
Split the data into chunks

You’ll take a look at each of these techniques in turn.

Compress and Decompress Files

You can create an archive file like you would a regular one, with the addition of a suffix that corresponds to the desired compression type:

'.gz'
'.bz2'
'.zip'
'.xz'

Pandas can deduce the compression type by itself:

df = pd.DataFrame(data=data).T
df.to_csv('data.csv.zip')

Here, you create a compressed .csv file as an archive. The size of the regular .csv file is 1048 bytes, while the compressed file only has 766 bytes.

You can open this compressed file as usual with the Pandas read_csv() function:

df = pd.read_csv('data.csv.zip', index_col=0,
                 parse_dates=['IND_DAY'])
df

COUNTRY      POP      AREA       GDP       CONT    IND_DAY
CHN       China  1398.72   9596.96  12234.78       Asia        NaT
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        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    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

read_csv() decompresses the file before reading it into a DataFrame.

You can specify the type of compression with the optional parameter compression, which can take on any of the following values:

'infer'
'gzip'
'bz2'
'zip'
'xz'
None

The default value compression='infer' indicates that Pandas should deduce the compression type from the file extension.

Here’s how you would compress a pickle file:

df = pd.DataFrame(data=data).T
df.to_pickle('data.pickle.compress', compression='gzip')

You should get the file data.pickle.compress that you can later decompress and read:

df = pd.read_pickle('data.pickle.compress', compression='gzip')

df again corresponds to the DataFrame with the same data as before.

You can give the other compression methods a try, as well. If you’re using pickle files, then keep in mind that the .zip format supports reading only.

Choose Columns

The Pandas read_csv() and read_excel() functions have the optional parameter usecols that you can use to specify the columns you want to load from the file. You can pass the list of column names as the corresponding argument:

df = pd.read_csv('data.csv', usecols=['COUNTRY', 'AREA'])
df

## output
 COUNTRY      AREA
0        China   9596.96
1        India   3287.26
2           US   9833.52
3    Indonesia   1910.93
4       Brazil   8515.77
5     Pakistan    881.91
6      Nigeria    923.77
7   Bangladesh    147.57
8       Russia  17098.25
9       Mexico   1964.38
10       Japan    377.97
11     Germany    357.11
12      France    640.68
13          UK    242.50
14       Italy    301.34
15   Argentina   2780.40
16     Algeria   2381.74
17      Canada   9984.67
18   Australia   7692.02
19  Kazakhstan   2724.90

Now you have a DataFrame that contains less data than before. Here, there are only the names of the countries and their areas.

Instead of the column names, you can also pass their indices:

df = pd.read_csv('data.csv',index_col=0, usecols=[0, 1, 3])
df

## output
 COUNTRY      AREA
CHN       China   9596.96
IND       India   3287.26
USA          US   9833.52
IDN   Indonesia   1910.93
BRA      Brazil   8515.77
PAK    Pakistan    881.91
NGA     Nigeria    923.77
BGD  Bangladesh    147.57
RUS      Russia  17098.25
MEX      Mexico   1964.38
JPN       Japan    377.97
DEU     Germany    357.11
FRA      France    640.68
GBR          UK    242.50
ITA       Italy    301.34
ARG   Argentina   2780.40
DZA     Algeria   2381.74
CAN      Canada   9984.67
AUS   Australia   7692.02
KAZ  Kazakhstan   2724.90

Expand the code block below to compare these results with the file 'data.csv':

data.csvShow/Hide

You can see the following columns:

The column at index 0 contains the row labels.
The column at index 1 contains the country names.
The column at index 3 contains the areas.

Simlarly, read_sql() has the optional parameter columns that takes a list of column names to read:

df = pd.read_sql('data.db', con=engine, index_col='ID',
...                  columns=['COUNTRY', 'AREA'])
df.index.name = None
df

## output
COUNTRY      AREA
CHN       China   9596.96
IND       India   3287.26
USA          US   9833.52
IDN   Indonesia   1910.93
BRA      Brazil   8515.77
PAK    Pakistan    881.91
NGA     Nigeria    923.77
BGD  Bangladesh    147.57
RUS      Russia  17098.25
MEX      Mexico   1964.38
JPN       Japan    377.97
DEU     Germany    357.11
FRA      France    640.68
GBR          UK    242.50
ITA       Italy    301.34
ARG   Argentina   2780.40
DZA     Algeria   2381.74
CAN      Canada   9984.67
AUS   Australia   7692.02
KAZ  Kazakhstan   2724.90

Again, the DataFrame only contains the columns with the names of the countries and areas. If columns is None or omitted, then all of the columns will be read, as you saw before. The default behavior is columns=None.

Omit Rows

When you test an algorithm for data processing or machine learning, you often don’t need the entire dataset. It’s convenient to load only a subset of the data to speed up the process. The Pandas read_csv() and read_excel() functions have some optional parameters that allow you to select which rows you want to load:

skiprows: either the number of rows to skip at the beginning of the file if it’s an integer, or the zero-based indices of the rows to skip if it’s a list-like object
skipfooter: the number of rows to skip at the end of the file
nrows: the number of rows to read

Here’s how you would skip rows with odd zero-based indices, keeping the even ones:

df = pd.read_csv('data.csv', index_col=0, skiprows=range(1, 20, 2))
df

## output
COUNTRY      POP     AREA      GDP       CONT     IND_DAY
IND       India  1351.16  3287.26  2575.67       Asia  1947-08-15
IDN   Indonesia   268.07  1910.93  1015.54       Asia  1945-08-17
PAK    Pakistan   205.71   881.91   302.14       Asia  1947-08-14
BGD  Bangladesh   167.09   147.57   245.63       Asia  1971-03-26
MEX      Mexico   126.58  1964.38  1158.23  N.America  1810-09-16
DEU     Germany    83.02   357.11  3693.20     Europe         NaN
GBR          UK    66.44   242.50  2631.23     Europe         NaN
ARG   Argentina    44.94  2780.40   637.49  S.America  1816-07-09
CAN      Canada    37.59  9984.67  1647.12  N.America  1867-07-01
KAZ  Kazakhstan    18.53  2724.90   159.41       Asia  1991-12-16`

In this example, skiprows is range(1, 20, 2) and corresponds to the values 1, 3, …, 19. The instances of the Python built-in class range behave like sequences. The first row of the file data.csv is the header row. It has the index 0, so Pandas loads it in. The second row with index 1 corresponds to the label CHN, and Pandas skips it. The third row with the index 2 and label IND is loaded, and so on.

If you want to choose rows randomly, then skiprows can be a list or NumPy array with pseudo-random numbers, obtained either with pure Python or with NumPy.

Force Less Precise Data Types

If you’re okay with less precise data types, then you can potentially save a significant amount of memory! First, get the data types with .dtypes again:

df = pd.read_csv('data.csv', index_col=0, parse_dates=['IND_DAY'])
df.dtypes

## output
COUNTRY            object
POP               float64
AREA              float64
GDP               float64
CONT               object
IND_DAY    datetime64[ns]
dtype: object

The columns with the floating-point numbers are 64-bit floats. Each number of this type float64 consumes 64 bits or 8 bytes. Each column has 20 numbers and requires 160 bytes. You can verify this with .memory_usage():

df.memory_usage()

## output
Index      160
COUNTRY    160
POP        160
AREA       160
GDP        160
CONT       160
IND_DAY    160
dtype: int64

.memory_usage() returns an instance of Series with the memory usage of each column in bytes. You can conveniently combine it with .loc[] and .sum() to get the memory for a group of columns:

df.loc[:, ['POP', 'AREA', 'GDP']].memory_usage(index=False).sum()

# 480

This example shows how you can combine the numeric columns 'POP', 'AREA', and 'GDP' to get their total memory requirement. The argument index=False excludes data for row labels from the resulting Series object. For these three columns, you’ll need 480 bytes.

You can also extract the data values in the form of a NumPy array with .to_numpy() or .values. Then, use the .nbytes attribute to get the total bytes consumed by the items of the array:

df.loc[:, ['POP', 'AREA', 'GDP']].to_numpy().nbytes

# 480

The result is the same 480 bytes. So, how do you save memory?

In this case, you can specify that your numeric columns 'POP', 'AREA', and 'GDP' should have the type float32. Use the optional parameter dtype to do this:

dtypes = {'POP': 'float32', 'AREA': 'float32', 'GDP': 'float32'}
 df = pd.read_csv('data.csv', index_col=0, dtype=dtypes,
...                  parse_dates=['IND_DAY'])

The dictionary dtypes specifies the desired data types for each column. It’s passed to the Pandas read_csv() function as the argument that corresponds to the parameter dtype.

Now you can verify that each numeric column needs 80 bytes, or 4 bytes per item:

df.dtypes

## output
COUNTRY            object
POP               float32
AREA              float32
GDP               float32
CONT               object
IND_DAY    datetime64[ns]
dtype: object

df.memory_usage()

## output
Index      160
COUNTRY    160
POP         80
AREA        80
GDP         80
CONT       160
IND_DAY    160
dtype: int64

df.loc[:, ['POP', 'AREA', 'GDP']].memory_usage(index=False).sum()
# 240

df.loc[:, ['POP', 'AREA', 'GDP']].to_numpy().nbytes
# 240

Each value is a floating-point number of 32 bits or 4 bytes. The three numeric columns contain 20 items each. In total, you’ll need 240 bytes of memory when you work with the type float32. This is half the size of the 480 bytes you’d need to work with float64.

In addition to saving memory, you can significantly reduce the time required to process data by using float32 instead of float64 in some cases.

Use Chunks to Iterate Through Files

Another way to deal with very large datasets is to split the data into smaller chunks and process one chunk at a time. If you use read_csv(), read_json() or read_sql(), then you can specify the optional parameter chunksize:

data_chunk = pd.read_csv('data.csv', index_col=0, chunksize=8)
 type(data_chunk)

# <class 'pandas.io.parsers.TextFileReader'>

hasattr(data_chunk, '__iter__')
# True

hasattr(data_chunk, '__next__')
# True

chunksize defaults to None and can take on an integer value that indicates the number of items in a single chunk. When chunksize is an integer, read_csv() returns an iterable that you can use in a for loop to get and process only a fragment of the dataset in each iteration:

for df_chunk in pd.read_csv('data.csv', index_col=0, chunksize=8):
...     print(df_chunk, end='\n\n')
...     print('memory:', df_chunk.memory_usage().sum(), 'bytes',
...           end='\n\n\n')

## output
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  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

memory: 448 bytes

COUNTRY     POP      AREA      GDP       CONT     IND_DAY
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

memory: 448 bytes

 COUNTRY    POP     AREA      GDP       CONT     IND_DAY
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         NaN
KAZ  Kazakhstan  18.53  2724.90   159.41       Asia  1991-12-16

memory: 224 bytes

In this example, the chunksize is 8. The first iteration of the for loop returns a DataFrame with the first eight rows of the dataset only. The second iteration returns another DataFrame with the next eight rows. The third and last iteration returns the remaining four rows.

Note: You can also pass iterator=True to force the Pandas read_csv() function to return an iterator object instead of a DataFrame object.

In each iteration, you get and process the DataFrame with the number of rows equal to chunksize. It’s possible to have fewer rows than the value of chunksize in the last iteration. You can use this functionality to control the amount of memory required to process data and keep that amount reasonably small.

Conclusion

You now know how to save the data and labels from Pandas DataFrame objects to different kinds of files. You also know how to load your data from files and create DataFrame objects.

You’ve used the Pandas read_csv() and .to_csv() methods to read and write CSV files. You also used similar methods to read and write Excel, JSON, HTML, SQL, and pickle files. These functions are very convenient and widely used. They allow you to save or load your data in a single function or method call.

You’ve also learned how to save time, memory, and disk space when working with large data files:

Compress or decompress files
Choose the rows and columns you want to load
Use less precise data types
Split data into chunks and process them one by one

You’ve mastered a significant step in the machine learning and data science process! If you have any questions or comments, then please put them in the comments section below.

Source : .

Pandas: How to Read and Write Files

Table of Contents

Installing Pandas

Preparing Data

Using the Pandas read_csv() and .to_csv() Functions

Write a CSV File

Read a CSV File

Using Pandas to Write and Read Excel Files

Write an Excel File

Read an Excel File

Understanding the Pandas IO API

Write Files

Read Files

Working With Different File Types

CSV Files

JSON Files

HTML Files

Excel Files

SQL Files

Pickle Files

Working With Big Data

Compress and Decompress Files

Choose Columns

Omit Rows

Force Less Precise Data Types

Use Chunks to Iterate Through Files

Conclusion

Using the Pandas `read_csv()` and `.to_csv()` Functions