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:
import pandas as pd
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:
#refer to the row index:
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:
#use a list of indexes:
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:
import pandas as pd
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":
#refer to the named index:
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:
import pandas as pd
df = pd.read_csv('data.csv')
print(df)
Try it Yourself »
You will learn more about importing files in the next chapters.