DataFrame Summary with Functions
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
df = pd.read_excel("D:\\1.xlsx", "SheetName", index_col='Index_Column_rowlabel', usecols='B:AG',skiprows=2)
df.loc['No_of_Cats':'No_of_Dogs',:]
df = df.drop('Animals', axis=1)
df=df.reset_index()
df.loc[(df['Index_Column_rowlabel']=='No_of_Cats') | (df['Index_Column_rowlabel']=='No_of_Dogs')]
df=df.rename(columns={"Mobile Voice":"Date_T"})
df=df.transpose()
df.columns = df.iloc[0]
df
####################
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
df = pd.read_excel("D:\\1.xlsx", "sheetname",index_col='Movekp' usecols='B:AGG',skiprows=2)
#get the/quering the specific information from the sheet using loc function of the pandas
newdf=df.loc[(df['Movekp']=='Sub in Ind') | (df['Movekp']=='Sub in Pk')]
newdf
#droping the column
newdf=newdf.drop('Venture', axis=1)
#rename the columns
newdf=newdf.rename(columns={'Move`':'Date'})
#take transpose of the dataframe
newdf=newdf.transpose()
newdf=newdf.reset_index()
#assigning the value of first row to columns
newdf.columns = newdf.iloc[0]
# remove first row
newdf=newdf.tail(-1)
#save to disk
newdf.to_csv("D:\\1_data.csv")
# Information about df
newdf.info()
#importing datetime library.
from datetime import date,datetime
# converting Date column to datetime type
newdf["Date"]=pd.to_datetime(newdf["Date"])
# set the date column as index of dataframe df
newdf=newdf.set_index('Date')
# plot the graph of the dataframe df that is line
newdf.plot()
Selection under condition in data frame ,along with Group by clause in Pandas dataframe
import pandas
df=pandas.read_csv(".//abc.csv")
df['date_t']=df.ds.astype(dtype='datetime64[ms]')
df['month'] = df['date_t'].dt.month
df['year'] = df['date_t'].dt.year
df.info()
df_tmp = df.groupby(['date_t','month','year'])['yhat'].sum().reset_index().sort_values( 'yhat',ascending = False)
r=df_tmp[((df_tmp['month'] == 1)|(df_tmp['month'] == 2)) & (df_tmp['year'] == 2002)].groupby(['month','year'])['yhat'].apply(lambda grp: grp.nlargest(3).mean())
r.to_csv('./Average_of_t3.csv')