pandas: .str method

In [92]:

import pandas as pd
df = pd.read_csv("W1.csv")
df

Out[92]:

	customerID	OrderID	Product	Company_Name
0	1234.0	9991.0	laptop	Dell
1	2345.0	9992.0	desktop	HP
2	3456.0	9993.0	monitor	LG
3	NaN	8888.0	trackpad	Apple
4	4567.0	9994.0	keyboard	Anker
5	123.0	NaN	NaN	Apple
6	1234.0	9990.0	tablet	Apple

In [93]:

df["Product"].str.upper()

Out[93]:

0       LAPTOP
1      DESKTOP
2      MONITOR
3     TRACKPAD
4     KEYBOARD
5          NaN
6       TABLET
Name: Product, dtype: object

In [94]:

df["Company_Name"].str.upper()

Out[94]:

0      DELL
1        HP
2        LG
3     APPLE
4     ANKER
5     APPLE
6     APPLE
Name: Company_Name, dtype: object

In [95]:

df["Product"].str.replace("tablet", "cellphone")

Out[95]:

0        laptop
1       desktop
2       monitor
3      trackpad
4      keyboard
5           NaN
6     cellphone
Name: Product, dtype: object

In [96]:

df["Product"] = df["Product"].str.title()

In [97]:

df

Out[97]:

	customerID	OrderID	Product	Company_Name
0	1234.0	9991.0	Laptop	Dell
1	2345.0	9992.0	Desktop	HP
2	3456.0	9993.0	Monitor	LG
3	NaN	8888.0	Trackpad	Apple
4	4567.0	9994.0	Keyboard	Anker
5	123.0	NaN	NaN	Apple
6	1234.0	9990.0	Tablet	Apple

In [98]:

search = df["Company_Name"].dropna(how = "All").str.lower().str.contains("apple")
df[search]

Out[98]:

	customerID	OrderID	Product	Company_Name
3	NaN	8888.0	Trackpad	Apple
5	123.0	NaN	NaN	Apple
6	1234.0	9990.0	Tablet	Apple

In [143]:

import pandas as pd
df = pd.read_csv("W1.csv")

#search = df["Product"].str.lower().str.startswith("ta", na=False)
#search

search = df["Company_Name"].str.lower().str.strip().str.startswith("a")
df[search]

Out[143]:

	customerID	OrderID	Product	Company_Name
3	NaN	8888.0	trackpad	Apple
4	4567.0	9994.0	keyboard	Anker
5	123.0	NaN	NaN	Apple
6	1234.0	9990.0	tablet	Apple

In [120]:

search = df["Company_Name"].str.lower().str.endswith("apple")
df[search]

Out[120]:

	customerID	OrderID	Product	Company_Name
3	NaN	8888.0	trackpad	Apple
5	123.0	NaN	NaN	Apple
6	1234.0	9990.0	tablet	Apple

In [148]:

import pandas as pd
df = pd.read_csv("W1.csv")
df["Product"] = df["Product"].dropna(how="all")
df["Product"]

Out[148]:

0       laptop
1      desktop
2      monitor
3     trackpad
4     keyboard
5          NaN
6       tablet
Name: Product, dtype: object

In [149]:

df["Product"].replace("trackpad", "notracpad")

Out[149]:

0       laptop
1      desktop
2      monitor
3    notracpad
4     keyboard
5          NaN
6       tablet
Name: Product, dtype: object

In [159]:

import pandas as pd
df = pd.read_csv("W1.csv", index_col="Company_Name").dropna(how="all")
df["Product"] = df["Product"].astype("category")
df
df.index.str.strip().str.title()
df.index = df.index.str.strip().str.title()
df

Out[159]:

	customerID	OrderID	Product
Company_Name
Dell	1234.0	9991.0	laptop
Hp	2345.0	9992.0	desktop
Lg	3456.0	9993.0	monitor
Apple	NaN	8888.0	trackpad
Anker	4567.0	9994.0	keyboard
Apple	123.0	NaN	NaN
Apple	1234.0	9990.0	tablet

In [161]:

df.columns.str.upper()

Out[161]:

Index(['CUSTOMERID', 'ORDERID', 'PRODUCT'], dtype='object')

In [163]:

import pandas as pd
df = pd.read_csv("W1.csv")
df

Out[163]:

	customerID	OrderID	Product	Company_Name
0	1234.0	9991.0	laptop	Dell
1	2345.0	9992.0	desktop	HP
2	3456.0	9993.0	monitor	LG
3	NaN	8888.0	trackpad	Apple
4	4567.0	9994.0	keyboard	Anker
5	123.0	NaN	NaN	Apple
6	1234.0	9990.0	tablet	Apple

In [165]:

df["new_column"] = df["Product"].str.title()
df

Out[165]:

	customerID	OrderID	Product	Company_Name	new_column
0	1234.0	9991.0	laptop	Dell	Laptop
1	2345.0	9992.0	desktop	HP	Desktop
2	3456.0	9993.0	monitor	LG	Monitor
3	NaN	8888.0	trackpad	Apple	Trackpad
4	4567.0	9994.0	keyboard	Anker	Keyboard
5	123.0	NaN	NaN	Apple	NaN
6	1234.0	9990.0	tablet	Apple	Tablet

In [169]:

df["customerID"].nlargest(5)

Out[169]:

4    4567.0
2    3456.0
1    2345.0
0    1234.0
6    1234.0
Name: customerID, dtype: float64

In [167]:

df["customerID"].nsmallest()

Out[167]:

5     123.0
0    1234.0
6    1234.0
1    2345.0
2    3456.0
Name: customerID, dtype: float64

In [ ]: