Pandas Techniques for Data Manipulation in Python apply function:
Pandas.apply allow users to pass a function to every cell in the dataframe. Ander conditions of the function it works to dataframe it increase the simplicity and readability of code.
DataFrame.apply(func, axis=0, raw=False, result_type=None, args=(), **kwargs) fun: function you want to apply to rows or columns
axis(0 or index,1 or column) axis:0 or 'index': apply function to each column. axis:1 or 'column': apply function to each row.
row:(False is Default,Determines if row or column is passed as a Series or ndarray object) if row = False :passes each row or column as a Series to the function. else: the passed function will receive ndarray objects instead
args :tuple Positional arguments to pass to func in addition to the array/series.
**kwargs:additional keyword arguments to pass as keywords arguments to func.
import pandas as pd
import numpy as np
df3 = pd.DataFrame([[2,3,4,5]] * 3, columns=['A', 'B','C','D'])
df3
df4 = df3.apply(np.sqrt)
#sum each column alone so you will get 4 values
df3.apply(np.sum,axis = 0)A 6
B 9
C 12
D 15
dtype: int64#sum all row so you will get 3 rows
df3.apply(np.sum,axis = 1)0 14
1 14
2 14
dtype: int64df = pd.read_csv('traffic.csv')
df.head()
dfi.export(df.head(),'df.png')
def use_apply(i):
j = "NotFound"
if i == 'M':
j ="Male"
elif i == "F":
j = "Female"
return j
result = df['driver_gender'].apply(use_apply)
result0 Male
1 Male
2 Male
3 Male
4 Female
...
91736 Female
91737 Female
91738 Male
91739 Female
91740 Male
Name: driver_gender, Length: 91741, dtype: objectAs we see above we change every cell in column driver_gender as function said
pandas.DataFrame.agg
This Function help you to do some operations at the same time so reduce your code.
DataFrame.agg(func=None, axis=0, *args, **kwargs)
func : accept functions to perform them this function accept list of funtions
axis: The default is (0)to perform along columns
axis :1 to perform along rows
*args: to add some parameters
**kwargs:Keyword arguments to pass to func to name identify parameters
df3 = pd.DataFrame([[1, 2, 3],
[4, 5, 6],
[7, 8, 9],
[np.nan, np.nan, np.nan],
[3,4,5],
[8,9,6]],
columns=['A', 'B', 'C'])
df3.agg(['max','sum'])
df3.agg(sum)
A 23.0
B 28.0
C 29.0
dtype: float64df3.agg(min)
A 1.0
B 2.0
C 3.0
dtype: float64Merge Dataframe
If You have tow Data sets and you want to Work at them at the same time to get results from them all you should use merge.
dataframe pandas.merge(left, right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False, validate=None)
left: Dataframe name
right: the second dataframe name
how:(inner defult, outer, left, right, cross)
left: use keys from left data frame
right: use keys from right data frame
inner: intersection between the two data frame
outer: the union of the two data frame
on: column name which should be in the two data Frame
left_on: column or index level names to join on in the left DataFrame.
right_on: column or index level names to join on in the right DataFrame.
left_index: default False, use index of the left dataframe
right_index: use index of the right dataframe
suffixes: to distinguish between two data frame columns if there exist the same names
df4 = pd.DataFrame({'dfk': ['foo', 'bar', 'baz', 'foo'],
'value': [1, 2, 3, 5]})
df5 = pd.DataFrame({'dfk': ['foo', 'bar', 'baz', 'foo'],
'value': [5, 6, 7, 8]})
pd.merge(df4,df5,on ='dfk') # intersection
pd.merge(df4,df5,how = 'outer' ,on = 'dfk')
df4.merge(df5,how = 'cross') # cross product 
pandas.isnull
Catch Empty cells and Return True if NaN and False if not
df3.isnull()
pandas.unique(values)
Return unique values
# notice that the output not sorted
pd.unique(pd.Series([4,5,7,8,9,99,4,5,4 ,2,33]))array([ 4, 5, 7, 8, 9, 99, 2, 33], dtype=int64)pd.unique([("m", "n"), ("z", "x"), ("n", "v"), ("z", "x")])
# note that (a,b) != (b,a)array([('m', 'n'), ('z', 'x'), ('n', 'v')], dtype=object)
melt in pandas
used to change Data format from wide-----> to long(⬇️)
m = {"Name": ["Aya", "Lisa", "David"], "ID": [1, 2, 3], "Role": ["CEO", "Editor", "Author"]}
df = pd.DataFrame(m)
print(df)
print('\n________________________________________\n')
df_melted = pd.melt(df, id_vars=["ID"], value_vars=["Name", "Role"])
print(df_melted) Name ID Role
0 Aya 1 CEO
1 Lisa 2 Editor
2 David 3 Author
________________________________________
ID variable value
0 1 Name Aya
1 2 Name Lisa
2 3 Name David
3 1 Role CEO
4 2 Role Editor
5 3 Role Authorwe can use pivot to unmelt dataframe
m = {"Name": ["Aya", "Lisa", "David"], "ID": [1, 2, 3], "Role": ["CEO", "Editor", "Author"]}
df = pd.DataFrame(m)
print(df)
print('\n________________________________________\n')
melted = pd.melt(df, id_vars=["ID"], value_vars=["Name", "Role"], var_name="Attribute", value_name="Value")
print(melted)
print('\n________________________________________\n')
# unmelting using pivot()
unmelted = melted.pivot(index='ID', columns='Attribute')
print(unmelted) Name ID Role
0 Aya 1 CEO
1 Lisa 2 Editor
2 David 3 Author
________________________________________
ID Attribute Value
0 1 Name Aya
1 2 Name Lisa
2 3 Name David
3 1 Role CEO
4 2 Role Editor
5 3 Role Author
________________________________________
Value
Attribute Name Role
ID
1 Aya CEO
2 Lisa Editor
3 David Authorunmelted = unmelted['Value'].reset_index()
unmelted.columns.name = None
print(unmelted) ID Name Role
0 1 Aya CEO
1 2 Lisa Editor
2 3 David Author
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