Manipulating Built Dataframes

Apply function

df['column_name'] = df['column_name'].apply(
lambda x : 
  float(x.replace("$", "0",).replace(",", "").strip()))

Insert column

df.insert(1, 'column_name', data)

Resetting index

df = df.reset_index(drop=True)
pd.concat([s1, s2], ignore_index=True)

Remove rows that do not contain numbers

df[pd.to_numeric(df['Funding'], errors='coerce').notnull()]

Drop if all columns are Nan

df.dropna(axis=0, how='all')

Clean up all cells in dataframe

def clean(row):
    return row.replace("nan", "").strip()

df.applymap(clean)

Drop rows with NAs in specific columns

df = df.dropna(axis=0, subset=['column_name'])

# Replace values with NA to make this wok
import numpy as np

df['column_name'] = df['column_name'].replace('-', np.nan)
df = df.dropna(axis=0, subset=['column_name'])

Drop rows with specific strings

df[~df.C.str.contains("XYZ", na=False)]

Drop rows with empty cells

df['Tenant'].replace('', np.nan, inplace=True)