Creating DataFrames Cookbook

Combining multiple Series into a DataFrame Combining multiple Series to form a DataFrame Converting a Series to a DataFrame Converting list of lists into DataFrame Converting list to DataFrame Converting percent string into a numeric for read_csv Converting scikit-learn dataset to Pandas DataFrame Converting string data into a DataFrame Creating a DataFrame from a string Creating a DataFrame using lists Creating a DataFrame with different type for each column Creating a DataFrame with empty values Creating a DataFrame with missing values Creating a DataFrame with random numbers Creating a DataFrame with zeros Creating a MultiIndex DataFrame Creating a Pandas DataFrame Creating a single DataFrame from multiple files Creating empty DataFrame with only column labels Filling missing values when using read_csv Importing Dataset Initialising a DataFrame using a constant Initialising a DataFrame using a dictionary Initialising a DataFrame using a list of dictionaries Inserting lists into a DataFrame cell Keeping leading zeroes when using read_csv Parsing dates when using read_csv Preventing strings from getting parsed as NaN for read_csv Reading data from GitHub Reading file without header Reading large CSV files in chunks Reading n random lines using read_csv Reading space-delimited files Reading specific columns from file Reading tab-delimited files Reading the first few lines of a file to create DataFrame Reading the last n lines of a file Reading URL using read_csv Reading zipped csv file as a DataFrame Removing Unnamed:0 column Resolving ParserError: Error tokenizing data Saving DataFrame as zipped csv Skipping rows without skipping header for read_csv Specifying data type for read_csv Treating missing values as empty strings rather than NaN for read_csv

thumb_up

thumb_down

chat_bubble_outline
0
 auto_stories new 
settings

Creating a Pandas DataFrame

Programming

Python

Pandas

Cookbooks

DataFrame Cookbooks

Creating DataFrames Cookbook

Mar 10, 2022

Last updated

local_offer Pandas●Python

Using Objects/Dictionaries

To create a DataFrame using objects:


        
        
            
                
                
                    people_age = {
   "Alex": 30,
   "Bob": 35,
   "Cathy": 25
}
people_height = {
   "Alex": 175,
   "Bob": 170,
   "Cathy": 160
}
data = pd.DataFrame({"Age":people_age, "Height":people_height})
print(data)
                
            
                   Age   Height
Alex   30    175
Bob    35    170
Cathy  25    160

Using a Dictionary of arrays

To create a DataFrame using a dictionary of arrays:


        
        
            
                
                
                    df = pd.DataFrame({"A":[3,4], "B":[5,6]}, index=["a","b"])
df
                
            
               A  B
a  3  5
b  4  6

Using Pandas Series

To create a DataFrame using Series:


        
        
            
                
                
                    s_one = pd.Series([3,4], index=["a","b"])
s_two = pd.Series([5,6], index=["a","b"])
df = pd.DataFrame({"A":s_one, "B":s_two})
df
                
            
               A  B
a  3  5
b  4  6

Using Standard Lists

To create a DataFrame using Python's standard lists:


        
        
            
                
                
                    values = [[1,2,3], [4,5,6]]
column_labels = ["A", "B", "C"]
indices = ["D", "E"]
data = pd.DataFrame(values, index=indices, columns=column_labels)
print(data)
                
            
               A  B  C
D  1  2  3
E  4  5  6

Using 2D array

To create a DataFrame using 2D array:


        
        
            
                
                
                    df = pd.DataFrame([[3,4],[5,6]], index=["a","b"], columns=["A","B"])
df
                
            
               A  B
a  3  4
b  5  6