PandasDataFrames
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 theloc 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 PandasSeries.
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 PandasDataFrame.
Named Indexes
With theindex 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 theloc 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.
