df = pd.DataFrame([[1,0],[4,5]], columns=['A','B'])
df = df.append(pd.DataFrame([[6,9]], columns=list(df.columns)))

#
df = pd.read_csv("File.csv")

# 
df.shape

df.info()

df.head()
df.tail()

df["col1"]
df["col1"].value_counts()

df[["col1","col2"]]

df.columns
list(df.columns)

#pandas.core.series.Series
df.iloc[0]

df.iloc[0]["col1"]

df.iloc[ [0,1] ]
df.iloc[ [0,1] , [0,1] ]

df.loc[ [0,1] , ["col1","col2"] ]

df.loc[ 0:1 , "col1":"col2" ]

df.index
df.set_index('col1',inplace=True)
df.reset_index(inplace=True)

#
df = pd.read_csv("File.csv",index_col = "col")

#
df.sort_index(inplace=True)

filt = df["col"] == value
filt = (df["col"] == value | df["col1"] == value)

#
df[filt]

#
df.loc(filt)

# negation
df.loc(~filt)

# 
filt = df["col"].isin(["v1","v2"])
# na=False ignore Nan
filt = df["col"].str.containts(["v1","v2"],na=False)

# change columns names
df.columns = [x.lower() for x in df.columns]
df.rename(columns={"col":"col_new"}, incplace = True)

# change all values of a row
df.loc[0] = ["v1","v2"]

# change one column
df.loc[0,"col"] = "new_value"
df.loc[0,["col"]] = ["new_value"]

# change all rows
df["col"] = ['v1','v2']

def func(x):
	return ""

df["col"].apply(func)

df.apply(len)

df["col"].map({"v1":"nv1"})
df["col"].replease({"v1":"nv1"})

df["newcol"] = df["col"]  + 1
df.drop(columsn=["col"], inplace = True)

df = df.append({"A":10}, ignore_index = True)
df.drop(index=4)
df.drop(index=df[df["A"]==1].index)

df.sort_values(by="A", ascending=False,inplace=True	)
df["A"].sort_values()