In Pandas, DataFrame is the primary data structures to hold tabular data. You can create it using the DataFrame constructor Show
Tabular datasets which are located in large external databases or are present in files of different formats such as .csv files or excel files can be read into Python using the In this article, you will see different ways of making a DataFrame or loading existing tabular datasets in the form of a DataFrame. pandas.DataFrameSyntax
Purpose
Parameters
Returns
Creating a basic single column Pandas DataFrameA basic DataFrame can be made by using a list.
That creates a default column name (0) and index names (0,1,2,3..). Making a DataFrame from a dictionary of listsA pandas DataFrame can be created using a dictionary in which the keys are column names and and array or list of feature values are passed as the values to the dict. This dictionary is then passed as a value to the data parameter of the DataFrame constructor.
Making a DataFrame from a list of listsA list of lists means a list in which each element itself is a list. Each element in such a list forms a row of the DataFrame.
The elements of the inner lists, that is, the lists within Also, see that the column names have been passed as a list to the columns parameter. Get Free Complete Python CourseFacing the same situation like everyone else? Build your data science career with a globally recognised, industry-approved qualification. Get the mindset, the confidence and the skills that make Data Scientist so valuable. Get Free Complete Python CourseBuild your data science career with a globally recognised, industry-approved qualification. Get the mindset, the confidence and the skills that make Data Scientist so valuable.
For example:
Making a DataFrame from a list of dictionariesA list of dictionaries means a list in which each element is a dictionary.
Making a DataFrame from a Numpy arrayA multi-dimensional numpy array can also be used for creating a DataFrame. It looks similar to the list of lists where there is an outer array and the inner arrays form the rows of the DataFrame.
For column names, you need to pass a list of column names to the columns parameter just like it was shown in the previous section.
Alternatively, you can also make a dictionary of numpy arrays where the keys would be the column names and the corresponding values to each key would be the inner arrays which are the feature values.
Making a DataFrame using the zip functionThe zip function can be used to combine multiple objects into a single object which can then be passed into the pandas.DataFrame function for making the DataFrame.
Making Indexed Pandas DataFramesPandas DataFrames having a pre-defined index can also be made by passing a list of indices to the index parameter.
Making a new DataFrame from existing DataFramespandas.concat You can also make new DataFrames from existing DataFrames using the Joining two DataFrames horizontallyYou can join two DataFrames horizontally by setting the value of the axis parameter to 0.
Joining two DataFrames verticallyYou can also join two DataFrames vertically if they have the same column names by setting the value of the axis parameter to 1.
Making pandas DataFrames from text filesThe Even though the name of the function says ‘csv’, it can read other types of text files which are often imported from different databases because of which
they can be in different formats (.csv, .txt, etc.) or encodings (utf-8, ascii, etc.). You can find the entire list of parameters as well as their functions here. Now, you will see how to load a dataset using the read_csv function:
This does not seem right. The DataFrame has not been loaded properly as all the values of the rows are present in a single column. This is because, the default character by which Python separates the values of different columns in a row is a
comma (,). If you closely look at the data in the rows, you will see that the different values across rows are separated by a semi-colon(;). Therefore, in this case, you need to specify the value of sep as ‘ ; ‘ .
Now, you can see that the different feature values in the rows have been placed under the columns in an appropriate manner. Making pandas DataFrames from different types of filesThe
Practical Tips
Test Your KnowledgeQ1: Which parameter is used to pass a custom list of column names in the Answer Answer: names and set `header=0` Q2: Yoy have a DataFrame which cannot be fit into the memory. How will you load such a DataFrame in Python? Answer Answer: Use the chunksize parameter to load only a certain amount of rows at a time. Q3:Write the code to make the DataFrame shown using a numpy array without explicitly passing a list of column names: Answer Answer:
Q4: Complete the following line of code to ignore 100 rows from the bottom of the dataset:
Answer Answer:
Q5: Suppose you have the following dataset named ‘countries.csv’: Write the code for loading the file in Python. Make sure that the ‘NA’ values are recognized as missing values by Python Answer Answer:
This article was contributed by Shreyansh. How do you create a DataFrame from a DataFrame in python?There are three common ways to create a new pandas DataFrame from an existing DataFrame:. Method 1: Create New DataFrame Using Multiple Columns from Old DataFrame new_df = old_df[['col1','col2']]. ... . Method 2: Create New DataFrame Using One Column from Old DataFrame new_df = old_df[['col1']].. How do you convert a dataset to a DataFrame in python?“convert dataset to dataframe” Code Answer. from sklearn. datasets import load_iris.. import pandas as pd.. data = load_iris(). df = pd. DataFrame(data. data, columns=data. feature_names). df. head(). What is the method to create a DataFrame?Thus, the first and foremost method for creating a dataframe is by reading a csv file which is straightforward operation in Pandas. We just need to give the file path to the read_csv function. The read_csv function is highly versatile. It has several parameters that allow for modifying the csv file while reading.
How do I create a DataFrame for a csv file?Exporting the DataFrame into a CSV file
Pandas DataFrame to_csv() function exports the DataFrame to CSV format. If a file argument is provided, the output will be the CSV file. Otherwise, the return value is a CSV format like string. sep: Specify a custom delimiter for the CSV output, the default is a comma.
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