To create a data frame, we first need a dictionary with all the column values assigned to a column name. Then turn that dictionary into a pandas data frame
Boilerplate
import pandas as pd
df = pd.DataFrame() # an empty DataFrame
# Append columns by directly assigning values
df['Name'] = None
df['Age'] = None
# Create new rows as separate DataFrames
new_row1 = pd.DataFrame({'Name': ['Charlie'], 'Age': [28]})
new_row2 = pd.DataFrame({'Name': ['David'], 'Age': [35]})
df = pd.concat([df, new_row1, new_row2], ignore_index=True) # Appending new rows using concat()
display(df)Dataframe attributes and functions
Similar to series attributes, dataframes share a bit between them.
.head(num) the first X rows of the dataframe
.tail(num) the last X rows of the dataframe
.ndim the dimension the database is in. only a integer
.shape the shape of the database
.dtypes the data types of EACH row
.T gives us a transposed version of the
.iloc locate from index. Allows for indexing and slicing to get a column from a columnname, you will do referencing with square brackets. To get a cell from the column, turn the column iloc then grab the square bracket reference again.