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age_sex = titanic[["Age", "Sex"]]
In [9]: age_sex.head()
Out[9]:
Age Sex
0 22.0 male
1 38.0 female
2 26.0 female
3 35.0 female
4 35.0 male
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# Method 1
index_list = df.index # you can use masking to filter out specific index
col_list = ['A','C']
subset_df = df.loc[index_list, col_list]
df.loc[[('col1val1','col2val1'), ('col1val2','col2val2')]] # For multi-level index
# Method 2
df[df["col"]=="val"]
df[(df["col1"] < val1) & (df["col2"] == val2)]
# Method 3
df["col"].isin([val1, val2])
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import pandas as pd
data = pd.read_excel (r'C:\Users\Ron\Desktop\Product List.xlsx')
df = pd.DataFrame(data, columns= ['Product'])
print (df)
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import pandas as pd
data = pd.read_excel (r'C:\Users\Ron\Desktop\Product List.xlsx')
df = pd.DataFrame(data, columns= ['Product'])
print (df)
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import pandas as pd
data = pd.read_excel (r'C:\Users\Ron\Desktop\Product List.xlsx')
df = pd.DataFrame(data, columns= ['Product'])
print (df)
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import pandas as pd
data = pd.read_excel (r'C:\Users\Ron\Desktop\Product List.xlsx')
df = pd.DataFrame(data, columns= ['Product'])
print (df)
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In [25]: titanic.iloc[9:25, 2:5]
Out[25]:
Pclass Name Sex
9 2 Nasser, Mrs. Nicholas (Adele Achem) female
10 3 Sandstrom, Miss. Marguerite Rut female
11 1 Bonnell, Miss. Elizabeth female
12 3 Saundercock, Mr. William Henry male
13 3 Andersson, Mr. Anders Johan male
..
20 2 Fynney, Mr. Joseph J male
21 2 Beesley, Mr. Lawrence male
22 3 McGowan, Miss. Anna "Annie" female
23 1 Sloper, Mr. William Thompson male
24 3 Palsson, Miss. Torborg Danira female
[16 rows x 3 columns]