Pandas DataFrames
What is a DataFrame?
A Pandas DataFrame is a 2 dimensional data structure, like a 2 dimensional array, or a table with rows and columns.
Example
Create a simple Pandas DataFrame:
data = {
"calories": [420, 380, 390],
"duration": [50, 40, 45]
}
#load data into a DataFrame object:
df = pd.DataFrame(data)
print(df)
Result
calories duration 0 420 50 1 380 40 2 390 45
Locate Row
As you can see from the result above, the DataFrame is like a table with rows and columns.
Pandas use the loc attribute to return
one or more specified row(s)
Example
Return row 0:
print(df.loc[0])
Result
calories 420 duration 50 Name: 0, dtype: int64
Note: This example returns a Pandas Series.
Example
Return row 0 and 1:
print(df.loc[[0, 1]])
Result
calories duration 0 420 50 1 380 40
Note: When using [], the
result is a Pandas DataFrame.
Named Indexes
With the index argument, you can name your own indexes.
Example
Add a list of names to give each row a name:
data = {
"calories": [420, 380, 390],
"duration": [50, 40, 45]
}
df = pd.DataFrame(data, index = ["day1", "day2", "day3"])
print(df)
Result
calories duration day1 420 50 day2 380 40 day3 390 45
Locate Named Indexes
Use the named index in the loc attribute to return the specified row(s).
Example
Return "day2":
print(df.loc["day2"])
Result
calories 380 duration 40 Name: day2, dtype: int64
Load Files Into a DataFrame
If your data sets are stored in a file, Pandas can load them into a DataFrame.
Example
Load a comma separated file (CSV file) into a DataFrame:
df = pd.read_csv('data.csv')
print(df)
You will learn more about importing files in the next chapters.