pandas Operations part1
Below ate the pandas opertaion 1 Example.
import numpy as np
import pandas as pd
df = pd.read_csv('/content/first_test.csv')
#print(df)
#df.info()
#print(df[["id","name"]])
#print(df.groupby(by=["zip"]).size())
#print(df["id"].sum())
#print(df["id"].count())
#print(df["id"].max())
#print(df["id"].min())
#df["id"].mean()
#df.groupby('zip')['id'].sum()
#print(df[df.id==1])
#print(df[(df.id == 1) & (df.zip == 560098)])
#print(df[(df.id == 1) | (df.zip == 560098)])
'''for index, row in df.iterrows():
print(row["id"],",",row["name"])'''
#arr = df["zip"].unique()
#print(arr)
#df.head()
df.describe()
Below ate the pandas opertaion 1 Example.
import numpy as np
import pandas as pd
df = pd.read_csv('/content/first_test.csv')
from pandasql import sqldf
output = sqldf("select * from df")
output
drop
The drop() method removes the specified row or column.
By specifying the column axis (axis='columns'), the drop() method removes the specified column.
By specifying the row axis (axis='index'), the drop() method removes the specified row.
Syntax
dataframe.drop(labels, axis, index, columns, level, inplace., errors)
df1 = pd.read_csv('/content/first_test.csv')
df1.drop(['id', 'area'],1,inplace=True)
print(df1)
#pd.concat([df1,df2])
sort_values()
The sort_values() method sorts the DataFrame by the specified label.
Syntax
dataframe.sort_values(by, axis, ascending, inplace, kind, na_position, ignore_index, key)
#res1 = df1.sort_values(by=['zip'], ascending=True)
res2 = df1.sort_values(by=['zip'], ascending=False)
print(res2)
rename
using rename we can rename From Source to column name.
df1.rename(columns = {'id':'id1', 'area':'area2'}, inplace = True)
df1
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