Final Python-Pandas necessary-code-for-me
# 1 import necessary libraries - Monir
import pandas as pd
import os
import glob
import numpy as np
# 2 assign dataset names
PUBLIC_DISPATCHSCADA_list_of_files = []
# all dataset names with starting PUBLIC_DISPATCHSCADA
PUBLIC_DISPATCHSCADA_list_of_files = glob.glob('PUBLIC_DISPATCHSCADA*.csv')
# create empty list
dataframes_list = []
list_of_names = PUBLIC_DISPATCHSCADA_list_of_files
# 3 append datasets into teh list
for i in range(len(list_of_names)):
temp_df = pd.read_csv(list_of_names[i], skiprows = 1, skipfooter = 1)
#dataframes_list[i]=temp_df
dataframes_list.append(temp_df)
# 4
dataframes_list[2].tail()
# 5
PUBLIC_DISPATCHSCADA_df = pd.concat(dataframes_list)
#6
PUBLIC_DISPATCHSCADA_df.tail()
#7
PUBLIC_DISPATCHSCADA_df.shape
# 8
PUBLIC_DISPATCHSCADA_df.to_csv("PUBLIC_DISPATCHSCADA_all.csv") # Saving as csv file/ Monir Python-Pandas necessary-code-for-me
# Find out your current working directory
import os
print(os.getcwd())
# Skiprows
df = pd.read_csv("data/cereal.csv", skiprows = 1)
# skipfooter
df = pd.read_csv("data/cereal.csv", skipfooter = 1)
# Skiprows and skipfooter
df = pd.read_csv("data/cereal.csv", skiprows = 1, skipfooter = 1)
#================================================================
import glob
# assign dataset names
PUBLIC_DISPATCHSCADA_list_of_files = []
# all dataset names with starting PUBLIC_DISPATCHSCADA
PUBLIC_DISPATCHSCADA_list_of_files = glob.glob('PUBLIC_DISPATCHSCADA*.csv')
# create empty list
dataframes_list = []
#================================================================
# import module
import pandas as pd
# assign dataset names
list_of_names = ['crime','username']
# create empty list
dataframes_list = []
# append datasets into teh list
for i in range(len(list_of_names)):
temp_df = pd.read_csv("./csv/"+list_of_names[i]+".csv")
dataframes_list.append(temp_df)
#================================================================
# assign dataset names
PUBLIC_DISPATCHSCADA_list_of_files = []
# all dataset names with starting PUBLIC_DISPATCHSCADA
PUBLIC_DISPATCHSCADA_list_of_files = glob.glob('PUBLIC_DISPATCHSCADA*.csv')
# create empty list
dataframes_list = []
list_of_names = PUBLIC_DISPATCHSCADA_list_of_files
# append datasets into teh list
for i in range(len(list_of_names)):
# temp_df = pd.read_csv("./csv/"+list_of_names[i]+".csv")
if i==0:
dataframes_list[i] = pd.read_csv(list_of_names[i], skiprows = 1, skipfooter = 1)
else:
dataframes_list[i] = pd.read_csv(list_of_names[i], skiprows = 2, skipfooter = 1)
#dataframes_list.append(temp_df)
PUBLIC_DISPATCHSCADA_df = pd.concat(dataframes_list)# To select rows whose column value equals a scalar, some_value, use ==:
df.loc[df['column_name'] == some_value]
# To select rows whose column value is in an iterable, some_values, use isin:
df.loc[df['column_name'].isin(some_values)]
# Combine multiple conditions with &:
df.loc[(df['column_name'] >= A) & (df['column_name'] <= B)]
# Note the parentheses. Due to Python's operator precedence rules, & binds more tightly than <= and >=. Thus, the parentheses in the last example are necessary. Without the parentheses
df['column_name'] >= A & df['column_name'] <= B
is parsed as
df['column_name'] >= (A & df['column_name']) <= B
# To get the highest values of a column (such as top 2 or top 3), you can use nlargest() :
df['High'].nlargest(2)
You can also use nsmallest() to get the lowest values.