Text, time methods file added.

Author: LimaOscarData

Pandas_Text_Time_Methods.py

@@ -0,0 +1,206 @@
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+df = pd.read_excel('/Users/oscar/Desktop/My_Python_Libraries/text_exercise.XLSX')
+# print(df)
+#       id          staff department                 job       salary age
+# 0  M0001       Tom BLUE         HR             manager   "$150,000"  52
+# 1  M0002     JOHN BLACK         IT             manager   "$180,000"  48
+# 2  E0001  Micheal Brown         IT      data scientist   "$150,000"  35
+# 3  E0002   jason walker         HR           recruiter  130000dolar  38
+# 4  E0003     Alex Green         IT   backend developer   "$110,000"   -
+# 5  E0004    OSCAR SMİTH         IT  frontend developer   "$120,000"  32
+# 6  E0005    Adrian STAR         IT      data scientist   "$135,000"  40
+# 7  E0006   Albert simon         IT      data scientist  125000dolar  35
+
+# print(df.info())
+# <class 'pandas.core.frame.DataFrame'>
+# RangeIndex: 8 entries, 0 to 7
+# Data columns (total 6 columns):
+#  #   Column      Non-Null Count  Dtype
+# ---  ------      --------------  -----
+#  0   id          8 non-null      object
+#  1   staff       8 non-null      object
+#  2   department  8 non-null      object
+#  3   job         8 non-null      object
+#  4   salary      8 non-null      object
+#  5   age         8 non-null      object
+# dtypes: object(6)
+# memory usage: 512.0+ bytes
+# None
+
+#------------------------------------------------------------------------------------------------
+# some explanations :
+# lower() => Converts a string into lower case
+# upper() => Converts a string into upper case
+# capitalize() => Converts the first character to upper case
+# title() => Converts the first character of each word to upper case
+# swapcase() => Swaps the case lower/upper
+
+# this is python built-in lower:
+# print('steve'.lower())
+# steve
+
+# this is for series in pandas :
+# print(df['staff'].str.lower())
+# 0         tom blue
+# 1       john black
+# 2    micheal brown
+# 3     jason walker
+# 4       alex green
+# 5     oscar smi̇th
+# 6      adrian star
+# 7     albert simon
+# Name: staff, dtype: object
+
+# also you can use it for srt.upper(), str.title(), str.capitalize(), str.swapcase().
+
+#------------------------------------------------------------------------------------------------
+# isalpha() => Returns True if all characters in the string are in the alphabet
+# isnumeric() => Returns True if all characters in the string are numeric
+# isalnum() => Returns True if all characters in the string are alphanumeric
+# endswith() => Returns true if the string ends with the specified value
+# startswith() => Returns true if the string starts with the specified value
+# contains() => Returns a Boolean value True for each element if the substring contains in the element, else False.
+
+# print(df)
+#       id          staff department                 job       salary age
+# 0  M0001       Tom BLUE         HR             manager   "$150,000"  52
+# 1  M0002     JOHN BLACK         IT             manager   "$180,000"  48
+# 2  E0001  Micheal Brown         IT      data scientist   "$150,000"  35
+# 3  E0002   jason walker         HR           recruiter  130000dolar  38
+# 4  E0003     Alex Green         IT   backend developer   "$110,000"   -
+# 5  E0004    OSCAR SMİTH         IT  frontend developer   "$120,000"  32
+# 6  E0005    Adrian STAR         IT      data scientist   "$135,000"  40
+# 7  E0006   Albert simon         IT      data scientist  125000dolar  35
+
+# print(df['job'].str.isalpha())
+# 0     True
+# 1     True
+# 2    False
+# 3     True
+# 4    False
+# 5    False
+# 6    False
+# 7    False
+# Name: job, dtype: bool
+
+# print(df['age'].str.isnumeric())
+# 0      NaN
+# 1      NaN
+# 2      NaN
+# 3      NaN
+# 4    False
+# 5      NaN
+# 6      NaN
+# 7      NaN
+# Name: age, dtype: object
+#
+# it returns them as NaN but we need them as boolean.
+
+# If the types are object we can't check them by using str.isnumeric() attribute :
+# lets convert them to str initially .
+# astype let us to convert an item to related type, in below example to str .
+# print(df['age'].astype(str).str.isnumeric())
+# 0     True
+# 1     True
+# 2     True
+# 3     True
+# 4    False
+# 5     True
+# 6     True
+# 7     True
+# Name: age, dtype: bool
+
+# print(df['job'])
+# 0               manager
+# 1               manager
+# 2        data scientist
+# 3             recruiter
+# 4     backend developer
+# 5    frontend developer
+# 6        data scientist
+# 7        data scientist
+# Name: job, dtype: object
+
+# print(df['job'].str.startswith('data'))
+# 0    False
+# 1    False
+# 2     True
+# 3    False
+# 4    False
+# 5    False
+# 6     True
+# 7     True
+# Name: job, dtype: bool
+
+# Also you can use it with str.endswith("per"), str.contains("data"),
+
+
+# print(df['salary'])
+# 0     "$150,000"
+# 1     "$180,000"
+# 2     "$150,000"
+# 3    130000dolar
+# 4     "$110,000"
+# 5     "$120,000"
+# 6     "$135,000"
+# 7    125000dolar
+# Name: salary, dtype: object
+
+# print(df['salary'].str.isalnum())
+# 0    False
+# 1    False
+# 2    False
+# 3     True
+# 4    False
+# 5    False
+# 6    False
+# 7     True
+# Name: salary, dtype: bool
+
+# if there is a any punctuation or sign inside the series, result will be deceptive.
+# first of all we need to clean them .
+
+# With using regex :
+# It shows the results which contains a character a to z .
+# print(df['salary'].str.contains(r'[a-z]'))
+# 0    False
+# 1    False
+# 2    False
+# 3     True
+# 4    False
+# 5    False
+# 6    False
+# 7     True
+# Name: salary, dtype: bool
+
+#------------------------------------------------------------------------------------------------
+# We can use these string methods which returning boolean expression for creating condition and so selecting relative rows
+# it returns the all rows which contains the 'data' inside the job's column :
+# print(df[ df['job'].str.contains('data') ])
+#       id          staff department             job       salary age
+# 2  E0001  Micheal Brown         IT  data scientist   "$150,000"  35
+# 6  E0005    Adrian STAR         IT  data scientist   "$135,000"  40
+# 7  E0006   Albert simon         IT  data scientist  125000dolar  35
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