To use a list of values to select rows from a Python Pandas dataframe, we call the isin method.
For instanmce, we write
df = pd.DataFrame({'A': [5,6,3,4], 'B': [1,2,3,5]})
r = df[df['A'].isin([3, 6])]
to create the df data frame and get the values from column 'A' that’s in rows 3 to 6 with isin.
We can also get the rows that aren’t in 3 to 6 with
df[~df['A'].isin([3, 6])]
To drop rows of a Python Pandas DataFrame whose value in a certain column is NaN, we call the notna method.
For instance, we write
df = df[df['EPS'].notna()]
to drop the rows where the 'EPS' column isn’t NaN by calling notna on the column.
To add a new column to an existing Python Pandas DataFram, we can the assign method.
For instance, we write
df1 = df1.assign(e=pd.Series(np.random.randn(sLength)).values)
to call assign on the df1 data frame.
We add values to it by setting e to pd.Series(np.random.randn(sLength)).values.
To change column type in Python Pandas, we can use the to_numeric function.
For instance, we write
s = pd.Series(["8", 6, "7.5", 3, "0.9"])
pd.to_numeric(s)
to call to_numeric on the s series to convert all entries in the series from strings to numbers.
To create a Python Pandas Dataframe by appending one row at a time, we use a for loop and add the entries to the loc dictionary.
For instance, we wtite
import pandas as pd
from numpy.random import randint
df = pd.DataFrame(columns=['lib', 'qty1', 'qty2'])
for i in range(5):
df.loc[i] = ['name' + str(i)] + list(randint(10, size=2))
to create the df dataframe with pd.DataFrame with a few columns.
Then we add the rows by adding them to df.loc by using
df.loc[i] = ['name' + str(i)] +
where list(randint(10, size=2)) is the new data.