pandas
pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.
Add pandas library first time :
- In Terminal write :
Poetry add pandas - Then you can import the Library :
import pandas as pd
Create data frame using :
Series :
s = pd.Series([1, 3, 5, np.nan, 6, 8])
NumPy array :
df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list("ABCD"))
To check the data type for each column :
df.dtypes
to view the top and bottom rows of the frame:
For top : df.head() , for bottom :df.tail()
You can use int argument to select limit to show data :
df.tail(3)
Display the index, columns:
df.index , df.columns
Transposing the rows in the columns place :
df.T
Selecting a single column : df["A"]
Selecting specific amount of rows : df[0:3]
Select row via the position of the passed integers: df.iloc[3]
By integer slices rows and columns :
df.iloc[3:5, 0:2]
Setting a new column automatically aligns the data by the indexes :
1 – create new column :
s1 = pd.Series([1, 2, 3, 4, 5, 6], index=pd.date_range("20130102", periods=6))
2 – add new column to the data frame :
df["F"] = s1
Setting a new column automatically aligns the data by the indexes :
1 – create new column :
s1 = pd.Series([1, 2, 3, 4, 5, 6], index=pd.date_range("20130102", periods=6))
2 – add new column to the data frame :
df["F"] = s1
pandas primarily uses the value np.nan to represent missing data.
Operations
df.mean() : calculate the average of all columns
Applying functions to the data: df.apply(function())